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S1476927114001443 | Protein–RNA interaction plays a very crucial role in many biological processes, such as protein synthesis, transcription and post-transcription of gene expression and pathogenesis of disease. Especially RNAs always function through binding to proteins. Identification of binding interface region is especially useful for cellular pathways analysis and drug design. In this study, we proposed a novel approach for binding sites identification in proteins, which not only integrates local features and global features from protein sequence directly, but also constructed a balanced training dataset using sub-sampling based on submodularity subset selection. Firstly we extracted local features and global features from protein sequence, such as evolution information and molecule weight. Secondly, the number of non-interaction sites is much more than interaction sites, which leads to a sample imbalance problem, and hence biased machine learning model with preference to non-interaction sites. To better resolve this problem, instead of previous randomly sub-sampling over-represented non-interaction sites, a novel sampling approach based on submodularity subset selection was employed, which can select more representative data subset. Finally random forest were trained on optimally selected training subsets to predict interaction sites. Our result showed that our proposed method is very promising for predicting protein–RNA interaction residues, it achieved an accuracy of 0.863, which is better than other state-of-the-art methods. Furthermore, it also indicated the extracted global features have very strong discriminate ability for identifying interaction residues from random forest feature importance analysis. | Predicting protein–RNA interaction amino acids using random forest based on submodularity subset selection |
S1476927114001455 | Presence of polyalanine (polyA) stretches in some proteins is found to be associated with their aggregation, which causes disorders in various developmental processes. In this work, inherent propensities towards aggregation of some residues, which are not part of the polyA stretches, have been identified by using the primary sequences of seven polyA proteins with the help of Betascan, PASTA and Tango programs and explored unambiguously. This provides a basis for proposing molecular mechanism of this type of aggregation. Reported suppression of aggregation of polyA proteins by chaperones like HSP40 and HSP70 is substantiated through molecular docking. The hydrophobic residues of identified aggregating region are found to be interacting with hydrophobic surface of chaperones. This suggests a crucial clue for possible way to inhibit the aggregation of such proteins. | Involvement of non-polyalanine (polyA) residues in aggregation of polyA proteins: Clue for inhibition of aggregation |
S1476927114001467 | Protein structure prediction is a fundamental issue in the field of computational molecular biology. In this paper, the AB off-lattice model is adopted to transform the original protein structure prediction scheme into a numerical optimization problem. We present a balance-evolution artificial bee colony (BE-ABC) algorithm to address the problem, with the aim of finding the structure for a given protein sequence with the minimal free-energy value. This is achieved through the use of convergence information during the optimization process to adaptively manipulate the search intensity. Besides that, an overall degradation procedure is introduced as part of the BE-ABC algorithm to prevent premature convergence. Comprehensive simulation experiments based on the well-known artificial Fibonacci sequence set and several real sequences from the database of Protein Data Bank have been carried out to compare the performance of BE-ABC against other algorithms. Our numerical results show that the BE-ABC algorithm is able to outperform many state-of-the-art approaches and can be effectively employed for protein structure optimization. | A balance-evolution artificial bee colony algorithm for protein structure optimization based on a three-dimensional AB off-lattice model |
S1476927114001674 | Tandem repeats of short DNA sequences are commonly found in human DNA. These simple sequence repeats or microsatellites are highly polymorphic in the human genome. Since the anti-tumour agent cisplatin preferentially forms DNA adducts at runs of consecutive guanine nucleotides (poly(G)), the position and frequency of occurrence of poly(G) sequences in the updated human genome was investigated. There are more runs of consecutive guanines than would be expected by random chance. This especially true for poly(G) sequences longer than approximately n =9. A plot of poly(G) length against log(observed/expected) frequency produced a straight line for n >9. A similar observation was also found for poly(A) DNA sequence repeats. This data implied that the increase in observed/expected frequency is directly related to length of DNA repeat. It was proposed that long runs of consecutive guanine nucleotides could be a sensitive sensor of cellular DNA damage since a number of DNA damaging agents cause lesions at poly(G) sequences. | The frequency of poly(G) tracts in the human genome and their use as a sensor of DNA damage |
S1476927114001698 | Relative amino acid residue solvent accessibility values allow the quantitative comparison of atomic solvent-accessible surface areas in different residue types and physical environments in proteins and in protein structural alignments. Geometry-optimised tri-peptide structures in extended solvent-exposed reference conformations have been obtained for 43 amino acid residue types at a high level of quantum chemical theory. Significant increases in side-chain solvent accessibility, offset by reductions in main-chain atom solvent exposure, were observed for standard residue types in partially geometry-optimised structures when compared to non-minimised models built from identical sets of proper dihedral angles abstracted from the literature. Optimisation of proper dihedral angles led most notably to marked increases of up to 54% in proline main-chain atom solvent accessibility compared to literature values. Similar effects were observed for fully-optimised tri-peptides in implicit solvent. The relief of internal strain energy was associated with systematic variation in N, Cα and Cβ atom solvent accessibility across all standard residue types. The results underline the importance of optimisation of ‘hard’ degrees of freedom (bond lengths and valence bond angles) and improper dihedral angle values from force field or other context-independent reference values, and impact on the use of standardised fixed internal co-ordinate geometry in sampling approaches to the determination of absolute values of protein amino acid residue solvent accessibility. Quantum chemical methods provide a useful and accurate alternative to molecular mechanics methods to perform energy minimisation of peptides containing non-standard (chemically modified) amino acid residues frequently present in experimental protein structure data sets, for which force field parameters may not be available. Reference tri-peptide atomic co-ordinate sets including hydrogen atoms are made freely available. | Tri-peptide reference structures for the calculation of relative solvent accessible surface area in protein amino acid residues |
S1476927114001704 | Polyadenylation is the process of addition of poly(A) tail to mRNA 3′ ends. Identification of motifs controlling polyadenylation plays an essential role in improving genome annotation accuracy and better understanding of the mechanisms governing gene regulation. The bioinformatics methods used for poly(A) motifs recognition have demonstrated that information extracted from sequences surrounding the candidate motifs can differentiate true motifs from the false ones greatly. However, these methods depend on either domain features or string kernels. To date, methods combining information from different sources have not been found yet. Here, we proposed an improved poly(A) motifs recognition method by combing different sources based on decision level fusion. First of all, two novel prediction methods was proposed based on support vector machine (SVM): one method is achieved by using the domain-specific features and principle component analysis (PCA) method to eliminate the redundancy (PCA–SVM); the other method is based on Oligo string kernel (Oligo-SVM). Then we proposed a novel machine-learning method for poly(A) motif prediction by marrying four poly(A) motifs recognition methods, including two state-of-the-art methods (Random Forest (RF) and HMM-SVM), and two novel proposed methods (PCA–SVM and Oligo-SVM). A decision level information fusion method was employed to combine the decision values of different classifiers by applying the DS evidence theory. We evaluated our method on a comprehensive poly(A) dataset that consists of 14,740 samples on 12 variants of poly(A) motifs and 2750 samples containing none of these motifs. Our method has achieved accuracy up to 86.13%. Compared with the four classifiers, our evidence theory based method reduces the average error rate by about 30%, 27%, 26% and 16%, respectively. The experimental results suggest that the proposed method is more effective for poly(A) motif recognition. | An improved poly(A) motifs recognition method based on decision level fusion |
S147692711500002X | Mammalian target of rapamycin (mTOR), a key mediator of PI3K/Akt/mTOR signaling pathway, has recently emerged as a compelling molecular target in glioblastoma. The mTOR is a member of serine/threonine protein kinase family that functions as a central controller of growth, proliferation, metabolism and angiogenesis, but its signaling is dysregulated in various human diseases especially in certain solid tumors including the glioblastoma. Here, considering that there are various kinase inhibitors being approved or under clinical or preclinical development, it is expected that some of them can be re-exploited as new potent agents to target mTOR for glioblastoma therapy. To achieve this, a synthetic pipeline that integrated molecular grafting, consensus scoring, virtual screening, kinase assay and structure analysis was described to systematically profile the binding potency of various small-molecule inhibitors deposited in the protein kinase–inhibitor database against the kinase domain of mTOR. Consequently, a number of structurally diverse compounds were successfully identified to exhibit satisfactory inhibition profile against mTOR with IC50 values at nanomolar level. In particular, few sophisticated kinase–inhibitors as well as a flavonoid myricetin showed high inhibitory activities, which could thus be considered as potential lead compounds to develop new potent, selective mTOR–inhibitors. Structural examination revealed diverse nonbonded interactions such as hydrogen bonds, hydrophobic forces and van der Waals contacts across the complex interface of mTOR with myricetin, conferring both stability and specificity for the mTOR–inhibitor binding. | Structure-based grafting and identification of kinase–inhibitors to target mTOR signaling pathway as potential therapeutics for glioblastoma |
S1476927115000031 | Cell cycle regulates proliferative cell capacity under normal or pathologic conditions, and in general it governs all in vivo/in vitro cell growth and proliferation processes. Mathematical simulation by means of reliable and predictive models represents an important tool to interpret experiment results, to facilitate the definition of the optimal operating conditions for in vitro cultivation, or to predict the effect of a specific drug in normal/pathologic mammalian cells. Along these lines, a novel model of cell cycle progression is proposed in this work. Specifically, it is based on a population balance (PB) approach that allows one to quantitatively describe cell cycle progression through the different phases experienced by each cell of the entire population during its own life. The transition between two consecutive cell cycle phases is simulated by taking advantage of the biochemical kinetic model developed by Gérard and Goldbeter (2009) which involves cyclin-dependent kinases (CDKs) whose regulation is achieved through a variety of mechanisms that include association with cyclins and protein inhibitors, phosphorylation–dephosphorylation, and cyclin synthesis or degradation. This biochemical model properly describes the entire cell cycle of mammalian cells by maintaining a sufficient level of detail useful to identify check point for transition and to estimate phase duration required by PB. Specific examples are discussed to illustrate the ability of the proposed model to simulate the effect of drugs for in vitro trials of interest in oncology, regenerative medicine and tissue engineering. Superficial area in the Petri dish effectively available for adhesion of proliferating cells at time t, μm2 Superficial area in the Petri dish available for adhesion of proliferating cells at time t, μm2 Superficial area in the Petri dish roofed by proliferating adherent cells at time t, μm2 Superficial area in the Petri dish roofed by proliferating adherent cells in G1 at time t, μm2 Superficial area in the Petri dish, μm2 Division probability density function, 1/μm3 Proportionality constant in the expression of the rate rv (Eq. (20)), 1/h Number of total cell, dimensionless Number of initial total cell, dimensionless Number of total cell for the generic P phase, dimensionless 2D number density distribution of cells for the generic P phase, μm−3 1D number density distribution of cells for the generic P phase in the volume variable v, μm−3 1D number density distribution of cells for the generic P phase in the age variable, ξ, dimensionless Generic phase, G1, G0, S, G2, M Partitioning function, μm−3 Coefficient appearing in the symmetric beta function, dimensionless Time rate of change of v, μm3 h−1 Time rate of change of ξ, h−1 Time, h Single cell volume, μm3 Mother cell volume, μm3 Maximum value for cell volume, μm3 Minimum value for cell volume, μm3 Shape factor of the Weibull distribution function, dimensionless Constant parameter appearing in the definition of A EF, dimensionless Symmetric beta function, dimensionless Gamma function, dimensionless Rate of transition G1 → G0, h−1 Scale factor of the Weibull distribution function, μm3 Mean of bivariate normal Gaussian distribution for v variable, μm3 Mean of bivariate normal Gaussian distribution for ξ variable, dimensionless Age, dimensionless Phase maturation time for transition P→P+1, h Geometric limiting factor, dimensionless Standard deviation of bivariate normal Gaussian distribution for v variable, μm3 Standard deviation of bivariate normal Gaussian distribution for ξ variable, dimensionless | A novel quantitative model of cell cycle progression based on cyclin-dependent kinases activity and population balances |
S1476927115000043 | Mutations in the SCN1A gene have commonly been associated with a wide range of mild to severe epileptic syndromes. They generate a wide spectrum of phenotypes ranging from the relatively mild generalized epilepsy with febrile seizures plus (GEFS+) to other severe epileptic encephalopathies, including myoclonic epilepsy in infancy (SMEI), cryptogenic focal epilepsy (CFE), cryptogenic generalized epilepsy (CGE) and a distinctive subgroup termed as severe infantile multifocal epilepsy (SIMFE). The present study was undertaken to investigate the potential effects of a transition in the first nucleotide at the donor splice site of intron 15 of the SCN1A gene leading to CGES. Functional analyses using site-directed mutagenesis by PCR and subsequent ex-vivo splicing assays, revealed that the c.2946+1G>T mutation lead to a total skipping of exon 15. The exclusion of this exon did not alter the reading frame but induced the deletion of the amino acids (853 Leu −971 Val) which are a major part in the fourth, fifth and sixth transmembrane segments of the SCN1A protein. The theoretical implications of the splice site mutations predicted with the bioinformatic tool human splice finder were investigated and compared with the results obtained by the cellular assay. | Evaluation of the effect of c.2946+1G>T mutation on splicing in the SCN1A gene |
S1476927115000055 | Plastic changes in the brain required for memory formation and long-term learning are dependent on N-methyl-d-aspartic acid (NMDA) receptor signaling. Nefiracetam reportedly boosts NMDA receptor functions as a basis for its nootropic properties. Previous studies suggest that nefiracetam potentiates the NMDA receptor activation, as a more potent co-agonist for glycine binding site than glycine, though the underlying mechanisms remain elusive. Here, using BSP-SLIM method, a novel binding site within the core of spiral β-strands-1-5 of LBD-GLUN1 has been predicted in glycine-bound GLUN1 conformation in addition to the glycine pocket in Apo-GLUN1. Within the core of spiral β-strands-1-5 of LBD-GLUN1 pocket, all-atom molecular dynamics simulation revealed that nefiracetam disrupts Arg523-glycine-Asp732 interaction resulting in open GLUN1 conformation and ultimate diffusion of glycine out of the clamshell cleft. Open GLUN1 conformation coerces other intra-chain domains and proximal inter-chain domains to sample inactivate conformations resulting in closure of the transmembrane gate via a novel gauche trap on threonine 647 (chi-1 dihedral (χ 1)=−45° instead of +45°). Docking of nefiracetam into the glycine pocket reversed the gauche trap and meditates partial opening of the TMD gate within a time-scale of 100ns as observed in glycine-only state. All these results suggest that nefiracetam can favorably complete with glycine for GLUN1-LBD in a two-step process, first by binding to a novel site of GLUN1-LBD-NMDA receptor followed by disruption of glycine-binding dynamics then replacing glycine in the GLUN1-LBD cleft. | Molecular dynamics study-based mechanism of nefiracetam-induced NMDA receptor potentiation |
S1476927115000067 | Development of a protein-based drug delivery system has major impact on the efficacy and bioavailability of unstable and water insoluble drugs. In the present study, the binding modes of a nonspecific lipid transfer protein (nsLTP2) from Oryza sativa with various nucleosides and analogous molecules were identified. The 3-D structure of the protein was designed and validated using modeler 9.13, Molegro virtual docker and procheck tool, respectively. The binding affinity and strength of interactions, key contributing residues and specificity toward the substrates were accomplished by computational docking and model prediction. The protein presented high affinity to acyclovir and vidarabine as purine-analogous drugs. Binding affinity is influenced by the core template and functional groups of the ligands which are structurally different cause the variation of interaction energies with nsLTP2. Nonetheless, all the evaluated analogous drugs occupy the proximity space at the nsLTP active site with high similarity in their binding modes. Our findings hold great promise for the future applications of nsLTPs in various aspects of pharmaceutical science and molecular biology. | A novel biological role for nsLTP2 from Oriza sativa: Potential incorporation with anticancer agents, nucleosides and their analogues |
S1476927115000079 | Protein chains are generally long and consist of multiple domains. Domains are distinct structural units of a protein that can evolve and function independently. The accurate and reliable prediction of protein domain linkers and boundaries is often considered to be the initial step of protein tertiary structure and function predictions. In this paper, we introduce CISA as a method for predicting inter-domain linker regions solely from the amino acid sequence information. The method first computes the amino acid compositional index from the protein sequence dataset of domain-linker segments and the amino acid composition. A preference profile is then generated by calculating the average compositional index values along the amino acid sequence using a sliding window. Finally, the protein sequence is segmented into intervals and a simulated annealing algorithm is employed to enhance the prediction by finding the optimal threshold value for each segment that separates domains from inter-domain linkers. The method was tested on two standard protein datasets and showed considerable improvement over the state-of-the-art domain linker prediction methods. | Inter-domain linker prediction using amino acid compositional index |
S1476927115000171 | Since Ambros’ discovery of small non-protein coding RNAs in the early 1990s, the past two decades have seen an upsurge in the number of reports of predicted microRNAs (miR), which have been implicated in various functions. The correlation of miRs with cancer has spurred the usage of this class of non-coding RNAs in various cancer therapies, although most of them are at trial stages. However, the experimental identification of a miR to be associated with cancer is still an elaborate, time-consuming process. To aid this process of miR association, we undertook an in-silico study involving the identification of global signatures in experimentally validated microRNAs associated with cancer. Subsequently, a support vector machine based two-step binary classifier system has been trained and modeled from the features extracted from the above study. A total of 60 distinguishing features were selected and ranked to form the feature set for classification – 26 of these extracted from the miR sequence itself, and the remainder from the thermodynamics of folding and the hybridized miRNA–mRNA structure. The two step classifier model – miRSEQ and miRINT had reasonably good performance measures with fairly high values of Matthew’s correlation coefficient (MCC) values ranging from 0.72 to 0.82 (availability: https://sites.google.com/site/sumitslab/tools). | Identifying microRNAs involved in cancer pathway using support vector machines |
S1476927115000201 | The topology or shape of evolutionary trees and their unbalanced nature has been a long standing area of interest in the field of phylogenetics. Coevolutionary analysis, which considers the evolutionary relationships between a pair of phylogenetic trees, has to date not considered leveraging this unbalanced nature as a means to reduce the complexity of coevolutionary analysis. In this work we apply previous analyses of tree shapes to improve the efficiency of inferring coevolutionary events. In particular, we use this prior research to derive a new data structure for inferring coevolutionary histories. Our new data structure is proven to provide a reduction in the time and space required to infer coevolutionary events. It is integrated into an existing framework for coevolutionary analysis and has been validated using both synthetic and previously published biological data sets. This proposed data structure performs twice as fast as algorithms implemented using existing data structures with no degradation in the algorithm's accuracy. As the coevolutionary data sets increase in size so too does the running time reduction provided by the newly proposed data structure. This is due to our data structure offering a logarithmic time and space complexity improvement. As a result, the proposed update to existing coevolutionary analysis algorithms outlined herein should enable the inference of larger coevolutionary systems in the future. | A time and space complexity reduction for coevolutionary analysis of trees generated under both a Yule and Uniform model |
S1476927115000213 | Gene expression profiles based on high-throughput technologies contribute to molecular classifications of different cell lines and consequently to clinical diagnostic tests for cancer types and other diseases. Statistical techniques and dimension reduction methods have been devised for identifying minimal gene subset with maximal discriminative power. For sets of in silico candidate genes, assuming a unique gene signature or performing a parsimonious signature evaluation seems to be too restrictive in the context of in vitro signature validation. This is mainly due to the high complexity of largely correlated expression measurements and the existence of various oncogenic pathways. Consequently, it might be more advantageous to identify and evaluate multiple gene signatures with a similar good predictive power, which are referred to as near-optimal signatures, to be made available for biological validation. For this purpose we propose the bead-chain-plot approach originating from swarm intelligence techniques, and a small scale computational experiment is conducted in order to convey our vision. We simulate the acquisition of candidate genes by using a small pool of differentially expressed genes derived from microarray-based CNS tumour data. The application of the bead-chain-plot provides experimental evidence for improved classifications by using near-optimal signatures in validation procedures. | A new vision of evaluating gene expression signatures |
S1476927115000237 | Quantitative analysis of behaviors shown by interacting multiple animals can provide a key for revealing high-order functions of their nervous systems. To resolve these complex behaviors, a video tracking system that preserves individual identity even under severe overlap in positions, i.e., occlusion, is needed. We developed GroupTracker, a multiple animal tracking system that accurately tracks individuals even under severe occlusion. As maximum likelihood estimation of Gaussian mixture model whose components can severely overlap is theoretically an ill-posed problem, we devised an expectation–maximization scheme with additional constraints on the eigenvalues of the covariance matrix of the mixture components. Our system was shown to accurately track multiple medaka (Oryzias latipes) which freely swim around in three dimensions and frequently overlap each other. As an accurate multiple animal tracking system, GroupTracker will contribute to revealing unexplored structures and patterns behind animal interactions. The Java source code of GroupTracker is available at https://sites.google.com/site/fukunagatsu/software/group-tracker. | GroupTracker: Video tracking system for multiple animals under severe occlusion |
S1476927115000262 | Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have been proposed to solve this problem. However, there is still no method that can fully utilize the information hidden in the input data. In this article, we propose a learning-based method named BagReg for protein inference. The method firstly artificially extracts five features from the input data, and then chooses each feature as the class feature to separately build models to predict the presence probabilities of proteins. Finally, the weak results from five prediction models are aggregated to obtain the final result. We test our method on six public available data sets. The experimental results show that our method is superior to the state-of-the-art protein inference algorithms. | BagReg: Protein inference through machine learning |
S1476927115000286 | The active cholera toxin responsible for the massive loss of water and ions in cholera patients via its ADP ribosylation activity is a heterodimer of the A1 subunit of the bacterial holotoxin and the human cytosolic ARF6 (ADP Ribosylation Factor 6). The active toxin is a potential target for the design of inhibitors against cholera. In this study we identified the potential ligandable sites of the active cholera toxin which can serve as binding sites for drug-like molecules. By employing an energy-based approach to identify ligand binding sites, and comparison with the results of computational solvent mapping, we identified two potential ligandable sites in the active toxin which can be targeted during structure-based drug design against cholera. Based on the probe affinities of the identified ligandable regions, docking-based virtual screening was employed to identify probable inhibitors against these sites. Several indole-based alkaloids and phosphates showed strong interactions to the important residues of the ligandable region at the A1 active site. On the other hand, 26 top scoring hits were identified against the ligandable region at the A1 ARF6 interface which showed strong hydrogen bonding interactions, including guanidines, phosphates, Leucopterin and Aristolochic acid VIa. This study has important implications in the application of hybrid structure-based and ligand-based methods against the identified ligandable sites using the identified inhibitors as reference ligands, for drug design against the active cholera toxin. | Identification of inhibitors against the potential ligandable sites in the active cholera toxin |
S1476927115000298 | In the homologous genes studied, the exons and introns alternated in the same order in mouse and human. We studied, in both species: corresponding short segments of introns, whole corresponding introns and complete homologous genes. We considered the total number of nucleotides and the number and orientation of the SINE inserts. Comparisons of mouse and human data series showed that at the level of individual relatively short segments of intronic sequences the stochastic variability prevails in the local structuring, but at higher levels of organization a deterministic component emerges, conserved in mouse and human during the divergent evolution, despite the ample re-editing of the intronic sequences and the fact that processes such as SINE spread had taken place in an independent way in the two species. Intron conservation is negatively correlated with the SINE occupancy, suggesting that virus inserts interfere with the conservation of the sequences inherited from the common ancestor. | Determinism and randomness in the evolution of introns and sine inserts in mouse and human mitochondrial solute carrier and cytokine receptor genes |
S1476927115000304 | Bacteriocins are antimicrobial peptides which are ribosomally synthesized by mainly all bacterial species. LABs (lactic acid bacteria) are a diverse group of bacteria that include around 20 genera of various species. Though LABs have a tremendous potential for production of anti-microbial peptides, this group of bacteria is still underexplored for bacteriocins. To study the diversity among bacteriocin encoding clusters and the putative bacteriocin precursors, genome mining was performed on 20 different species of LAB not reported to be bacteriocin producers. The phylogenetic tree of gyrB, rpoB, and 16S rRNA were constructed using MEGA6 software to analyze the diversity among strains. Putative bacteriocins operons identified were found to be diverse and were further characterized on the basis of physiochemical properties and the secondary structure. The presence of at least two cysteine residues in most of the observed putative bacteriocins leads to disulphide bond formation and provide stability. Our data suggests that LABs are prolific source of low molecular weight non modified peptides. | Genome level analysis of bacteriocins of lactic acid bacteria |
S1476927115000316 | β-amyloid aggregation and formation of senile plaques is one of the hallmarks of Alzheimer’s disease (AD). It leads to degeneration of neurons and decline of cognitive functions. The most aggregative and toxic form of β-amyloid is Aβ1-42 but in experiments, the shorter forms able to form aggregates are also used. The early stages of amyloid formation are of special interest due to the influence of this peptide on progression of AD. Here, we employed nine helices of undecapeptide Aβ13-23 and studied progress of amyloid formation using 500ns molecular dynamics simulation and implicit membrane environment. The small β-sheets emerged very early during simulation as separated two-strand structures and a presence of the membrane facilitated this process. Later, the larger β-sheets were formed. However, the ninth helix which did not form paired structure stayed unchanged till the end of MD simulation. Paired helix–helix interactions seemed to be a driving force of β-sheet formation at early stages of amyloid formation. Contrary, the specific interactions between α-helix and β-sheet can be very stable and be stabilized by the membrane. | Study of early stages of amyloid Aβ13-23 formation using molecular dynamics simulation in implicit environments |
S1476927115000328 | Cytochrome P450s are a superfamily of heme monooxygenases which catalyze a wide range of biochemical reactions. The reactions involve the introduction of an oxygen atom into an inactivated carbon of a compound which is essential to produce an intermediate of a hydroxylated product. The diversity of chemical reactions catalyzed by cytochrome P450s has led to their increased demand in numerous industrial and biotechnology applications. A recent study showed that a gene sequence encoding a CYP was found in the genome of Bacillus lehensis G1, and this gene shared structural similarity with the bacterial vitamin D hydroxylase (Vdh) from Pseudonocardia autotrophica. The objectives of present study was to mine, for a novel CYP from a new isolate B. lehensis G1 alkaliphile and determine the biological properties and functionalities of CYP in this bacterium. Our study employed the usage of computational methods to search for the novel CYP from CYP structural databases to identify the conserved pattern, functional domain and sequence properties of the uncharacterized CYP from B. lehensis G1. A computational homology model of the protein’s structure was generated and a docking analysis was performed to provide useful structural knowledge on the enzyme’s possible substrate and their interaction. Sequence analysis indicated that the newly identified CYP, termed CYP107CB2, contained the fingerprint heme binding sequence motif FxxGxxxCxG at position 336-345 as well as other highly conserved motifs characteristic of cytochrome P450 proteins. Using docking studies, we identified Ser-79, Leu-81, Val-231, Val-279, Val-383, Ala-232, Thr-236 and Thr-283 as important active site residues capable of stabilizing interactions with several potential substrates, including vitamin D3, 25-hydroxyvitamin D3 and 1α-hydroxyvitamin D3, in which all substrates docked proximally to the enzyme’s heme center. Biochemical analysis indicated that CYP107CB2 is a biologically active protein to produce 1α,25-dihydroxyvitamin D3 from 1α-hydroxyvitamin D3. Based on these results, we conclude that the novel CYP107CB2 identified from B. lehensis G1 is a putative vitamin D hydroxylase which is possibly capable of catalyzing the bioconversion of parental vitamin D3 to calcitriol, or related metabolic products. | Molecular characterization, modeling and docking of CYP107CB2 from Bacillus lehensis G1, an alkaliphile |
S147692711500033X | In order to elucidate some basic principles for protein–ligand interactions, a subset of 87 structures of human proteins with their ligands was obtained from the PDB databank. After a short molecular dynamics simulation (to ensure structure stability), a variety of interaction energies and structural parameters were extracted. Linear regression was performed to determine which of these parameters have a potentially significant contribution to the protein–ligand interaction. The parameters exhibiting relatively high correlation coefficients were selected. Important factors seem to be the number of ligand atoms, the ratio of N, O and S atoms to total ligand atoms, the hydrophobic/polar aminoacid ratio and the ratio of cavity size to the sum of ligand plus water atoms in the cavity. An important factor also seems to be the immobile water molecules in the cavity. Nine of these parameters were used as known inputs to train a neural network in the prediction of seven other. Eight structures were left out of the training to test the quality of the predictions. After optimization of the neural network, the predictions were fairly accurate given the relatively small number of structures, especially in the prediction of the number of nitrogen and sulfur atoms of the ligand. | Structural properties and interaction energies affecting drug design. An approach combining molecular simulations, statistics, interaction energies and neural networks |
S1476927115000468 | Taxol is one of the most important anti-cancer drugs. The interaction between different variants of Taxol, by altering one of its chiral centers at a time, with β-tubulin protein has been investigated. To achieve such goal, docking and molecular dynamics (MD) simulation studies have been performed. In docking studies, the preferred conformers have been selected to further study by MD method based on the binding energies reported by the AutoDock program. The best result of docking study which shows the highest affinity between ligand and protein has been used as the starting point of the MD simulations. All of the complexes have shown acceptable stability during the simulation process, based on the RMSDs of the backbone of the protein structure. Finally, MM-GBSA calculations have been carried out to select the best ligand, considering the binding energy criteria. The results predict that two of the structures have better affinity toward the mentioned protein, in comparison with Taxol. Three of the structures have affinity similar to that of the Taxol toward the β-tubulin. | Evaluation of the effect of the chiral centers of Taxol on binding to β-tubulin: A docking and molecular dynamics simulation study |
S147692711500047X | Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. | Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification |
S1476927115000481 | Gene networks (GNs) have become one of the most important approaches for modeling biological processes. They are very useful to understand the different complex biological processes that may occur in living organisms. Currently, one of the biggest challenge in any study related with GN is to assure the quality of these GNs. In this sense, recent works use artificial data sets or a direct comparison with prior biological knowledge. However, these approaches are not entirely accurate as they only take into account direct gene–gene interactions for validation, leaving aside the weak (indirect) relationships. We propose a new measure, named gene network coherence (GNC), to rate the coherence of an input network according to different biological databases. In this sense, the measure considers not only the direct gene–gene relationships but also the indirect ones to perform a complete and fairer evaluation of the input network. Hence, our approach is able to use the whole information stored in the networks. A GNC JAVA-based implementation is available at: http://fgomezvela.github.io/GNC/. The results achieved in this work show that GNC outperforms the classical approaches for assessing GNs by means of three different experiments using different biological databases and input networks. According to the results, we can conclude that the proposed measure, which considers the inherent information stored in the direct and indirect gene–gene relationships, offers a new robust solution to the problem of GNs biological validation. | Gene network coherence based on prior knowledge using direct and indirect relationships |
S1476927115000493 | Protein–protein interactions (PPIs) play essential roles in many biological processes. In protein–protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (IDPs) with unstable structures can promote the promiscuity of hubs and also involve in many disease pathways, so they also could serve as potential drug targets. Moreover, proteins with similar functions measured by semantic similarity of gene ontology (GO) terms tend to interact with each other. Here, the relationship between hub proteins and drug targets based on GO terms and intrinsic disorder was explored. The semantic similarities of GO terms and genes between two proteins, and the rate of intrinsic disorder residues of each protein were extracted as features to characterize the functional similarity between two interacting proteins. Only using 8 feature variables, prediction models by support vector machine (SVM) were constructed to predict PPIs. The accuracy of the model on the PPI data from human hub proteins is as high as 83.72%, which is very promising compared with other PPI prediction models with hundreds or even thousands of features. Then, 118 of 142 PPIs between hubs are correctly predicted that the two interacting proteins are targets of the same drugs. The results indicate that only 8 functional features are fully efficient for representing PPIs. In order to identify new targets from IDP dataset, the PPIs between hubs and IDPs are predicted by the SVM model and the model yields a prediction accuracy of 75.84%. Further research proves that 3 of 5 PPIs between hubs and IDPs are correctly predicted that the two interacting proteins are targets of the same drugs. All results demonstrate that the model with only 8-dimensional features from GO terms and intrinsic disorder still gives a good performance in predicting PPIs and further identifying drug targets. | Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder |
S147692711500050X | A short partial sequence of 28 amino acids is all the information we have so far about the putative allergen 2S albumin from almond. The aim of this work was to analyze this information using mainly bioinformatics tools, in order to verify its rightness. Based on the results reported in the paper describing this allergen from almond, we analyzed the original data of amino acids sequencing through available software. The degree of homology of the almond 12kDa protein with any other known 2S albumin appears to be much lower than the one reported in the paper that firstly described it. In a publicly available cDNA library we discovered an expressed sequence tag which translation generates a protein that perfectly matches both of the sequencing outputs described in the same paper. A further analysis indicated that the latter protein seems to belong to the vicilin superfamily rather than to the prolamin one. The fact that also vicilins are seed storage proteins known to be highly allergenic would explain the IgE reactivity originally observed. Based on our observations we suggest that the IgE reactive 12kDa protein from almond currently known as Pru du 2S albumin is in reality the cleaved N-terminal region of a 7S vicilin like protein. | Pru du 2S albumin or Pru du vicilin? |
S1476927115000511 | Objectives To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. Methods The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein–protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Results Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Conclusions Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC. | Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer |
S1476927115000523 | Human T-cell leukemia virus type 1 (HTLV-1) protease is an attractive target when developing inhibitors to treat HTLV-1 associated diseases. To study the catalytic mechanism and design novel HTLV-1 protease inhibitors, the protonation states of the two catalytic aspartic acid residues must be determined. Free energy simulations have been conducted to study the proton transfer reaction between the catalytic residues of HTLV-1 protease using a combined quantum mechanical and molecular mechanical (QM/MM) molecular dynamics simulation. The free energy profiles for the reaction in the apo-enzyme and in an enzyme – substrate complex have been obtained. In the apo-enzyme, the two catalytic residues are chemically equivalent and are expected to be both unprotonated. Upon substrate binding, the catalytic residues of HTLV-1 protease evolve to a singly protonated state, in which the OD1 of Asp32 is protonated and forms a hydrogen bond with the OD1 of Asp32′, which is unprotonated. The HTLV-1 protease–substrate complex structure obtained from this simulation can serve as the Michaelis complex structure for further mechanistic studies of HTLV-1 protease while providing a receptor structure with the correct protonation states for the active site residues toward the design of novel HTLV-1 protease inhibitors through virtual screening. | Characterizing the protonation states of the catalytic residues in apo and substrate-bound human T-cell leukemia virus type 1 protease |
S1476927115000535 | This paper introduces a mathematical model representing the biochemical interactions between insulin signaling and Parkinson’s disease. The model can be used to examine the changes that occur over the course of the disease as well as identify which processes would be the most effective targets for treatment. The model is mathematized using biochemical systems theory (BST). It incorporates a treatment strategy that includes several experimental drugs along with current treatments. In the past, BST models of neurodegeneration have used power law analysis and simulation (PLAS) to model the system. This paper recommends the use of MATLAB instead. MATLAB allows for more flexibility in both the model itself and in data analysis. Previous BST analyses of neurodegeneration began treatment at disease onset. As shown in this model, the outcomes of delayed, realistic treatment and full treatment at disease onset are significantly different. The delayed treatment strategy is an important development in BST modeling of neurodegeneration. It emphasizes the importance of early diagnosis, and allows for a more accurate representation of disease and treatment interactions. | A mathematical model of insulin resistance in Parkinson’s disease |
S1476927115000547 | In this paper, we explore the impact of different forms of model abstraction and the role of discreteness on the dynamical behaviour of a simple model of gene regulation where a transcriptional repressor negatively regulates its own expression. We first investigate the relation between a minimal set of parameters and the system dynamics in a purely discrete stochastic framework, with the twofold purpose of providing an intuitive explanation of the different behavioural patterns exhibited and of identifying the main sources of noise. Then, we explore the effect of combining hybrid approaches and quasi-steady state approximations on model behaviour (and simulation time), to understand to what extent dynamics and quantitative features such as noise intensity can be preserved. | On the impact of discreteness and abstractions on modelling noise in gene regulatory networks |
S1476927115000560 | Due to the high amount of artificial food colorants present in infants’ diets, their adverse effects have been of major concern among the literature. Artificial food colorants have been suggested to affect children's behavior, being hyperactivity the most common disorder. In this study we compare binding affinities of a group of artificial colorants (sunset yellow, quinoline yellow, carmoisine, allura red and tartrazine) and their natural industrial equivalents (carminic acid, curcumin, peonidin-3-glucoside, cyanidin-3-glucoside) to human serum albumin (HSA) by a docking approach and further refinement through atomistic molecular dynamics simulations. Due to the protein–ligand conformational interface complexity, we used collective variable driven molecular dynamics to refine docking predictions and to score them according to a hydrogen-bond criterion. With this protocol, we were able to rank ligand affinities to HSA and to compare between the studied natural and artificial food additives. Our results show that the five artificial colorants studied bind better to HSA than their equivalent natural options, in terms of their H-bonding network, supporting the hypothesis of their potential risk to human health. | Study on the interaction of artificial and natural food colorants with human serum albumin: A computational point of view |
S1476927115000572 | CRTh2 receptor is an important mediator of inflammatory effects and has attracted much attention as a therapeutic target for the treatment of conditions such as asthma, COPD, allergic rhinitis and atopic dermatitis. In pursuit of better CRTh2 receptor antagonist agents, 3D-QSAR studies were performed on a series of 2-(2-(benzylthio)-1H-benzo[d]imidazol-1-yl) acetic acids. There is no crystal structure information available on this protein; hence in this work, ligand-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed by atom by atom matching alignment using systematic search and simulated annealing methods. The 3D-QSAR models were generated with 10 different combinations of test and training set molecules, since the robustness and predictive ability of the model is very important. We have generated 20 models for CoMFA and 100 models for CoMSIA based on two different alignments. Each model was validated with statistical cut off values such as q 2 >0.4, r 2 >0.5 and r 2 pred >0.5. Based on better q 2 and r 2 pred values, the best predictions were obtained for the CoMFA (model 5 q 2 =0.488, r 2 pred =0.732), and CoMSIA (model 45 q 2 =0.525, r 2 pred =0.883) from systematic search conformation alignment. The high correlation between the cross-validated/predicted and experimental activities of a test set revealed that the CoMFA and CoMSIA models were robust. Statistical parameters from the generated QSAR models indicated the data is well fitted and have high predictive ability. The generated models suggest that steric, electrostatic, hydrophobic, H-bond donor and acceptor parameters are important for activity. Our study serves as a guide for further experimental investigations on the synthesis of new CRTh2 antagonist. | Computational Analysis of CRTh2 receptor antagonist: A Ligand-based CoMFA and CoMSIA approach |
S1476927115000584 | Interaction between ATP, a multifunctional and ubiquitous nucleotide, and proteins initializes phosphorylation, polypeptide synthesis and ATP hydrolysis which supplies energy for metabolism. However, current knowledge concerning the mechanisms through which ATP is recognized by proteins is incomplete, scattered, and inaccurate. We systemically investigate sequence and structural motifs of proteins that recognize ATP. We identified three novel motifs and refined the known p-loop and class II aminoacyl-tRNA synthetase motifs. The five motifs define five distinct ATP–protein interaction modes which concern over 5% of known protein structures. We demonstrate that although these motifs share a common GXG tripeptide they recognize ATP through different functional groups. The p-loop motif recognizes ATP through phosphates, class II aminoacyl-tRNA synthetase motif targets adenosine and the other three motifs recognize both phosphates and adenosine. We show that some motifs are shared by different enzyme types. Statistical tests demonstrate that the five sequence motifs are significantly associated with the nucleotide binding proteins. Large-scale test on PDB reveals that about 98% of proteins that include one of the structural motifs are confirmed to bind ATP. | Systematic investigation of sequence and structural motifs that recognize ATP |
S1476927115000596 | G-quadruplex is a stable, four-stranded DNA or RNA structure formed from guanine-rich regions and implicated in telomere maintenance, replication, gene regulation at transcription level or translation level, etc. Based on bioinformatics methods, we analyzed different putative G-quadruplex motifs (PGQMs) patterns in various genomic regions of two subspecies (indica and japonica) of Oryza sativa and the whole genomes of other 8 species. In total, in the 10 species we discussed, the PGQMs densities in monocots were higher than dicots. 40,483 and 31,795 PGQMs were identified with a density of 108.46 and 84.89 PGQMs/Mb, respectively, in japonica and indica genomes, 10,655 and 5420 loci were found to contain at least one PGQM in their gene bodies (with a percentage of 19% and 14%) indicating a wide distribution of G-quadruplex motifs in O. sativa genome. They preferred to locate in transcription start sites proximal regions and 5′-UTR with relative high enrichment. This phenomenon supports the hypothesis that PGQMs are involved in gene transcription and translation. In addition, we analyzed the distribution of different loop length in G-quadruplex and found the density of long loop PGQMs was less than short loop in indica’s intron but it was similar in japonica. Meanwhile, we focused on the loci with PGQMs and conducted gene ontology (GO) analysis of them. As a result, many GO terms were identified and significantly correlated with the loci containing at least one PGQM. The GO analysis in the two subspecies of rice may be helpful for elucidating the functional roles of G-quadruplexes. | Genomic distribution and possible functional roles of putative G-quadruplex motifs in two subspecies of Oryza sativa |
S1476927115000602 | Glycolytic enzymes, such as enolase, have been described as multifunctional complex proteins that also display non-glycolytic activities, termed moonlighting functions. Although enolase multifunctionality has been described for several organisms, the conservation of enolase alternative functions through different phyla has not been explored with more details. A useful strategy to investigate moonlighting functions is the use of systems biology tools, which allow the prediction of protein functions/interactions by graph design and analysis. In this work, available information from protein–protein interaction (PPI) databases were used to design enolase PPI networks for four eukaryotic organisms, namely Homo sapiens, Drosophila melanogaster, Caenorhabditis elegans, and Saccharomyces cerevisiae, covering a wide spectrum of this domain of life. PPI networks with number of nodes ranging from 140 to 411 and up to 15,855 connections were generated, and modularity and centrality analyses, and functional enrichment were performed for all of them. The performed analyses showed that enolase is a central node within the networks, and that, in addition to its canonical interactions with proteins related to glycolysis and energetic metabolism, it is also part of protein clusters related to different biological processes, like transcription, development, and apoptosis, among others. Some of these non-glycolytic clusters, are partially conserved between networks, in terms of overall sharing of orthologs, overall cluster structure, and/or at the levels of key regulatory proteins within clusters. Overall, our results provided evidences of enolase multifunctionality and evolutionary conservation of enolase PPIs at all these levels. | Systems biology approach reveals possible evolutionarily conserved moonlighting functions for enolase |
S1476927115000833 | With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction. To fuse and reuse biomedical datasets from diverse resources, data abstraction is frequently required. Here we summarize some of the major Big Data biomedical research resources for genomics, proteomics and phenotype data, collected from mammalian cells, tissues and organisms. We then suggest simple data abstraction methods for fusing this diverse but related data. Finally, we demonstrate examples of the potential utility of such data integration efforts, while warning about the inherit biases that exist within such data. | Reprint of “Abstraction for data integration: Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction” |
S1476927115300013 | Momordica charantia (bitter gourd, bitter melon) is a monoecious Cucurbitaceae with anti-oxidant, anti-microbial, anti-viral and anti-diabetic potential. Molecular studies on this economically valuable plant are very essential to understand its phylogeny and evolution. MicroRNAs (miRNAs) are conserved, small, non-coding RNA with ability to regulate gene expression by bind the 3′ UTR region of target mRNA and are evolved at different rates in different plant species. In this study we have utilized homology based computational approach and identified 27 mature miRNAs for the first time from this bio-medically important plant. The phylogenetic tree developed from binary data derived from the data on presence/absence of the identified miRNAs were noticed to be uncertain and biased. Most of the identified miRNAs were highly conserved among the plant species and sequence based phylogeny analysis of miRNAs resolved the above difficulties in phylogeny approach using miRNA. Predicted gene targets of the identified miRNAs revealed their importance in regulation of plant developmental process. Reported miRNAs held sequence conservation in mature miRNAs and the detailed phylogeny analysis of pre-miRNA sequences revealed genus specific segregation of clusters. | Identification of evolutionarily conserved Momordica charantia microRNAs using computational approach and its utility in phylogeny analysis |
S1476927115300025 | A gene regulatory network (GRN) is a large and complex network consisting of interacting elements that, over time, affect each other’s state. The dynamics of complex gene regulatory processes are difficult to understand using intuitive approaches alone. To overcome this problem, we propose an algorithm for inferring the regulatory interactions from knock-out data using a Gaussian model combines with Pearson Correlation Coefficient (PCC). There are several problems relating to GRN construction that have been outlined in this paper. We demonstrated the ability of our proposed method to (1) predict the presence of regulatory interactions between genes, (2) their directionality and (3) their states (activation or suppression). The algorithm was applied to network sizes of 10 and 50 genes from DREAM3 datasets and network sizes of 10 from DREAM4 datasets. The predicted networks were evaluated based on AUROC and AUPR. We discovered that high false positive values were generated by our GRN prediction methods because the indirect regulations have been wrongly predicted as true relationships. We achieved satisfactory results as the majority of sub-networks achieved AUROC values above 0.5. | Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient |
S1476927115300037 | Background The complexity of DNA can be quantified using estimates of entropy. Variation in DNA complexity is expected between the promoters of genes with different transcriptional mechanisms; namely housekeeping (HK) and tissue specific (TS). The former are transcribed constitutively to maintain general cellular functions, and the latter are transcribed in restricted tissue and cells types for specific molecular events. It is known that promoter features in the human genome are related to tissue specificity, but this has been difficult to quantify on a genomic scale. If entropy effectively quantifies DNA complexity, calculating the entropies of HK and TS gene promoters as profiles may reveal significant differences. Results Entropy profiles were calculated for a total dataset of 12,003 human gene promoters and for 501 housekeeping (HK) and 587 tissue specific (TS) human gene promoters. The mean profiles show the TS promoters have a significantly lower entropy (p <2.2e−16) than HK gene promoters. The entropy distributions for the 3 datasets show that promoter entropies could be used to identify novel HK genes. Conclusion Functional features comprise DNA sequence patterns that are non-random and hence they have lower entropies. The lower entropy of TS gene promoters can be explained by a higher density of positive and negative regulatory elements, required for genes with complex spatial and temporary expression. | DNA entropy reveals a significant difference in complexity between housekeeping and tissue specific gene promoters |
S1476927115300074 | The binding energies of imatinib and nilotinib to tyrosine kinase have been determined by quantum mechanical (QM) computations, and compared with literature binding energy studies using molecular mechanics (MM). The potential errors in the computational methods include these critical factors: Errors in X-ray structures such as structural distortions and steric clashes give unrealistically high van der Waals energies, and erroneous binding energies. MM optimization gives a very different configuration to the QM optimization for nilotinib, whereas the imatinib ion gives similar configurations. Solvation energies are a major component of the overall binding energy. The QM based solvent model (PCM/SMD) gives different values from those used in the implicit PBSA solvent MM models. A major error in inhibitor—kinase binding lies in the non-polar solvation terms. Solvent transfer free energies and the required empirical solvent accessible surface area factors for nilotinib and imatinib ion to give the transfer free energies have been reverse calculated. These values differ from those used in the MM PBSA studies. An intertwined desolvation—conformational binding selectivity process is a balance of thermodynamic desolvation and intramolecular conformational kinetic control. The configurational entropies (TΔS) are minor error sources. | Binding energies of tyrosine kinase inhibitors: Error assessment of computational methods for imatinib and nilotinib binding |
S1476927115300086 | Bacterial MocR transcriptional regulators possess an N-terminal DNA-binding domain containing a conserved helix-turn-helix module and an effector-binding and/or oligomerization domain at the C-terminus, homologous to fold type-I pyridoxal 5′-phosphate (PLP) enzymes. Since a comprehensive structural analysis of the MocR regulators is still missing, a comparisons of Firmicutes MocR sequences was undertook to contribute to the understanding of the structural characteristics of the C-terminal domain of these proteins, and to shed light on the structural and functional relationship with fold type-I PLP enzymes. Results of this work suggest the presence of at least three subgroups within the MocR sequences and provide a guide for rational site-directed mutagenesis studies aimed at deciphering the structure-function relationships in this new protein family. | The aspartate aminotransferase-like domain of Firmicutes MocR transcriptional regulators |
S1476927115300098 | For biological applications, sequence alignment is an important strategy to analyze DNA and protein sequences. Multiple sequence alignment is an essential methodology to study biological data, such as homology modeling, phylogenetic reconstruction and etc. However, multiple sequence alignment is a NP-hard problem. In the past decades, progressive approach has been proposed to successfully align multiple sequences by adopting iterative pairwise alignments. Due to rapid growth of the next generation sequencing technologies, a large number of sequences can be produced in a short period of time. When the problem instance is large, progressive alignment will be time consuming. Parallel computing is a suitable solution for such applications, and GPU is one of the important architectures for contemporary parallel computing researches. Therefore, we proposed a GPU version of ClustalW v2.0.11, called CUDA ClustalW v1.0, in this work. From the experiment results, it can be seen that the CUDA ClustalW v1.0 can achieve more than 33× speedups for overall execution time by comparing to ClustalW v2.0.11. | CUDA ClustalW: An efficient parallel algorithm for progressive multiple sequence alignment on Multi-GPUs |
S1476927115300116 | Yersinia organisms cause many infectious diseases by invading human cells and delivering their virulence factors via the type three secretion system (T3SS). One alternative strategy in the fight against these pathogenic organisms is to interfere with their T3SS. Previous studies demonstrated that thiol peroxidase, Tpx is functional in the assembly of T3SS and its inhibition by salicylidene acylhydrazides prevents the secretion of pathogenic effectors. In this study, the aim was to identify potential inhibitors of Tpx using an integrated approach starting with high throughput virtual screening and ending with molecular dynamics simulations of selected ligands. Virtual screening of ZINC database of 500,000 compounds via ligand-based and structure-based pharmacophore models retrieved 10,000 hits. The structure-based pharmacophore model was validated using high-throughput virtual screening (HTVS). After multistep docking (SP and XP), common scaffolds were used to find common substructures and the ligand binding poses were optimized using induced fit docking. The stability of the protein–ligand complex was examined with molecular dynamics simulations and the binding free energy of the complex was calculated. As a final outcome eight compounds with different chemotypes were proposed as potential inhibitors for Tpx. The eight ligands identified by a detailed virtual screening protocol can serve as leads in future drug design efforts against the destructive actions of pathogenic bacteria. | Identification of potential Tpx inhibitors against pathogen-host interactions |
S1476927115300128 | Plants have evolved exquisite molecular mechanisms to adapt to diverse abiotic stresses. MicroRNAs play an important role in stress response in plants. However, whether the other small RNAs (sRNAs) possess stress-related roles remains elusive. In this study, thousands of sRNAs responsive to cold, drought and salt stresses were identified in rice seedlings and panicles by using high-throughput sequencing data. These sRNAs were classified into 12 categories, including “Panicle_Cold_Down”, “Panicle_Cold_Up”, “Panicle_Drought_Down”, “Panicle_Drought_Up”, “Panicle_Salt_Down”, “Panicle_Salt_Up”, “Seedling_Cold_Down”, “Seedling_Cold_Up”, “Seedling_Drought_Down”, “Seedling_Drought_Up”, “Seedling_Salt_Down” and “Seedling_Salt_Up”. The stress-responsive sRNAs enriched in Argonaute 1 were extracted for target prediction and degradome sequencing data-based validation, which enabled network construction. Within certain subnetworks, some target genes were further supported by microarray data. Literature mining indicated that certain targets were potentially involved in stress response. These results demonstrate that the established networks are biologically meaningful. We discovered that in some cases, one sRNA sequence could be assigned to two or more categories. Moreover, within certain target-centered subnetworks, one transcript was regulated by several stress-responsive sRNAs assigned to different categories. It implies that these subnetworks are potentially implicated in stress signal crosstalk. Together, our results could advance the current understanding of the biological role of plant sRNAs in stress signaling. | Construction of regulatory networks mediated by small RNAs responsive to abiotic stresses in rice (Oryza sativa) |
S1476927115300141 | The metabolic rearrangements occurring in cancer cells can be effectively investigated with a Systems Biology approach supported by metabolic network modeling. We here present tissue-specific constraint-based core models for three different types of tumors (liver, breast and lung) that serve this purpose. The core models were extracted and manually curated from the corresponding genome-scale metabolic models in the Human Metabolic Atlas database with a focus on the pathways that are known to play a key role in cancer growth and proliferation. Along similar lines, we also reconstructed a core model from the original general human metabolic network to be used as a reference model. A comparative Flux Balance Analysis between the reference and the cancer models highlighted both a clear distinction between the two conditions and a heterogeneity within the three different cancer types in terms of metabolic flux distribution. These results emphasize the need for modeling approaches able to keep up with this tumoral heterogeneity in order to identify more suitable drug targets and develop effective treatments. According to this perspective, we identified key points able to reverse the tumoral phenotype toward the reference one or vice-versa. | Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models |
S1476927115300153 | Traditional clustering algorithms often exhibit poor performance for large networks. On the contrary, greedy algorithms are found to be relatively efficient while uncovering functional modules from large biological networks. The quality of the clusters produced by these greedy techniques largely depends on the underlying heuristics employed. Different heuristics based on different attributes and properties perform differently in terms of the quality of the clusters produced. This motivates us to design new heuristics for clustering large networks. In this paper, we have proposed two new heuristics and analyzed the performance thereof after incorporating those with three different combinations in a recently celebrated greedy clustering algorithm named SPICi. We have extensively analyzed the effectiveness of these new variants. The results are found to be promising. | Impact of heuristics in clustering large biological networks |
S1476927115300219 | A methodology for performing sequence-free comparison of functional sites in protein structures is introduced. The method is based on a new notion of similarity among superimposed groups of amino acid residues that evaluates both geometry and physico-chemical properties. The method is specifically designed to handle disconnected and sparsely distributed sets of residues. A genetic algorithm is employed to find the superimposition of protein segments that maximizes their similarity. The method was evaluated by performing an all-to-all comparison on two separate sets of ligand-binding sites, comprising 47 protein-FAD (Flavin-Adenine Dinucleotide) and 64 protein-NAD (Nicotinamide-Adenine Dinucleotide) complexes, and comparing the results with those of an existing sequence-based structural alignment tool (TM-Align). The quality of the two methodologies is judged by the methods’ capacity to, among other, correctly predict the similarities in the protein-ligand contact patterns of each pair of binding sites. The results show that using a sequence-free method significantly improves over the sequence-based one, resulting in 23 significant binding-site homologies being detected by the new method but ignored by the sequence-based one. | Comparison of non-sequential sets of protein residues |
S1476927115300232 | Motivated by the experimental evidences accumulated in the last ten years and based on information deposited in RegulonDB, literature look up, and sequence analysis, we analyze the repertoire of 304 DNA-binding Transcription factors (TFs) in Escherichia coli K-12. These regulators were grouped in 78 evolutionary families and are regulating almost half of the total genes in this bacterium. In structural terms, 60% of TFs are composed by two-domains, 30% are monodomain, and 10% three- and four-structural domains. As previously noticed, the most abundant DNA-binding domain corresponds to the winged helix-turn-helix, with few alternative DNA-binding structures, resembling the hypothesis of successful protein structures with the emergence of new ones at low scales. In summary, we identified and described the characteristics associated to the DNA-binding TF in E. coli K-12. We also identified twelve functional modules based on a co-regulated gene matrix. Finally, diverse regulons were predicted based on direct associations between the TFs and potential regulated genes. This analysis should increase our knowledge about the gene regulation in the bacterium E. coli K-12, and provide more additional clues for comprehensive modelling of transcriptional regulatory networks in other bacteria. | The functional landscape bound to the transcription factors of Escherichia coli K-12 |
S1476927115300244 | Carotenoids are essential isoprenoid pigments produced by plants, algae, fungi and bacteria. Lycopene cyclase (LYC) commonly cyclize carotenoids, which is an important branching step in the carotenogenesis, at one or both end of the backbone. Plants have two types of LYC (β-LCY and ϵ-LCY). In this study, plant LYCs were analyzed. Based on domain analysis, all LYCs accommodate lycopene cyclase domain (Pf05834). Furthermore, motif analysis indicated that motifs were conserved among the plants. On the basis of phylogenetic analysis, β-LCYs and ϵ-LCYs were classified in β and ϵ groups. Monocot and dicot plants separated from each other in the phylogenetic tree. Subsequently, Oryza sativa Japonica Group and Zea mays of LYCs as monocot plants and Vitis vinifera and Solanum lycopersicum of LYCs as dicot plants were analyzed. According to nucleotide diversity analysis of β-LCY and ϵ-LCY genes, nucleotide diversities were found to be π: 0.30 and π: 0.25, respectively. The result highlighted β-LCY genes showed higher nucleotide diversity than ϵ-LCY genes. LYCs interacting genes and their co-expression partners were also predicted using String server. The obtained data suggested the importance of LYCs in carotenoid metabolism. 3D modeling revealed that depicted structures were similar in O. sativa, Z mays, S. lycopersicum, and V. vinifera β-LCYs and ϵ-LCYs. Likewise, the predicted binding sites were highly similar between O. sativa, Z mays, S. lycopersicum, and V. vinifera LCYs. Most importantly, analysis elucidated the V/IXGXGXXGXXXA motif for both type of LYC (β-LCY and ϵ-LCY). This motif related to Rossmann fold domain and probably provides a flat platform for binding of FAD in O. sativa, Z mays, S. lycopersicum, and V. vinifera β-LCYs and ϵ-LCYs with conserved structure. In addition to lycopene cyclase domain, the V/IXGXGXXGXXXA motif can be used for exploring LYCs proteins and to annotate the function of unknown proteins containing lycopene cyclase domain. Overall results indicated that a high degree of conserved signature were observed in plant LYCs. | Comparative analysis of plant lycopene cyclases |
S1476927115300311 | Interleukin-1β is a drug target in rheumatoid arthritis and several auto-immune disorders. In this study, a set of 48 compounds with the determined IC50 values were used for QSAR analysis by MOE. The QSAR model was developed by using training set of 41 compounds, based on 12 unique descriptors. Model was validated by predicting the IC50 values for a test set of 7 compounds. A correlation analysis was carried out comparing the statistics of the measured IC50 values with predicted ones. Subsequently, model was used for the screening of a large data set of 7,397,957 compounds obtained from “Drugs Now” category of ZINC database. The activities of those compounds were predicted by developed model. 708,960 compounds that showed best predicted activities were chosen for further studies. Additionally this set of 708,960 compounds was screened by pharmacophore modeling that led to the retrieval of 1809 molecules. Finally docking of 1809 molecules was conducted at the IL-1β receptor binding site using MOE and FRED docking program. Several new compounds were predicted as IL-1β inhibitors in silico. This study provides valuable insight for designing more potent and selective inhibitors for the treatment of inflammatory diseases. | In silico identification of novel IL-1β inhibitors to target protein–protein interfaces |
S1476927115300451 | Rift Valley fever virus (RVFV) is a potent human and livestock pathogen endemic to sub-Saharan Africa and the Arabian Peninsula that has potential to spread to other parts of the world. Although there is no proven effective and safe treatment for RVFV infections, a potential therapeutic target is the virally encoded nucleocapsid protein (N). During the course of infection, N binds to viral RNA, and perturbation of this interaction can inhibit viral replication. To gain insight into how N recognizes viral RNA specifically, we designed an algorithm that uses a distance matrix and multidimensional scaling to compare the predicted secondary structures of known N-binding RNAs, or aptamers, that were isolated and characterized in previous in vitro evolution experiment. These aptamers did not exhibit overt sequence or predicted structure similarity, so we employed bioinformatic methods to propose novel aptamers based on analysis and clustering of secondary structures. We screened and scored the predicted secondary structures of novel randomly generated RNA sequences in silico and selected several of these putative N-binding RNAs whose secondary structures were similar to those of known N-binding RNAs. We found that overall the in silico generated RNA sequences bound well to N in vitro. Furthermore, introduction of these RNAs into cells prior to infection with RVFV inhibited viral replication in cell culture. This proof of concept study demonstrates how the predictive power of bioinformatics and the empirical power of biochemistry can be jointly harnessed to discover, synthesize, and test new RNA sequences that bind tightly to RVFV N protein. The approach would be easily generalizable to other applications. | Computational prediction and biochemical characterization of novel RNA aptamers to Rift Valley fever virus nucleocapsid protein |
S1476927115300517 | The identification of protein complexes in protein–protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions. | MOEPGA: A novel method to detect protein complexes in yeast protein–protein interaction networks based on MultiObjective Evolutionary Programming Genetic Algorithm |
S147692711530058X | Gene silencing is an important function as it keeps newly acquired foreign DNA repressed, thereby avoiding possible deleterious effects in the host organism. Known transcriptional regulators associated with this process are called xenogeneic silencers (XS) and belong to either the H-NS, Lsr2, MvaT or Rok families. In the work described here we looked for XS-like regulators and their distribution in prokaryotic organisms was evaluated. Our analysis showed that putative XS regulators similar to H-NS, Lsr2, MvaT or Rok are present only in bacteria (31.7%). This does not exclude the existence of alternative XS in the rest of the organisms analyzed. Additionally, of the four XS groups evaluated in this work, those from the H-NS family have diversified more than the other groups. In order to compare the distribution of these putative XS regulators we also searched for other nucleoid-associated proteins (NAPs) not included in this group such as Fis, EbfC/YbaB, HU/IHF and Alba. Results showed that NAPs from the Fis, EbfC/YbaB, HU/IHF and Alba families are widely (94%) distributed among prokaryotes. These NAPs were found in multiple combinations with or without XS-like proteins. In regard with XS regulators, results showed that only XS proteins from one family were found in those organisms containing them. This suggests specificity for this type of regulators and their corresponding genomes. | Distribution of putative xenogeneic silencers in prokaryote genomes |
S147692711530061X | As a pivotal domain within envelope protein, fusion peptide (FP) plays a crucial role in pathogenicity and therapeutic intervention. Taken into account the limited FP annotations in NCBI database and absence of FP prediction software, it is urgent and desirable to develop a bioinformatics tool to predict new putative FPs (np-FPs) in retroviruses. In this work, a sequence-based FP model was proposed by combining Hidden Markov Method with similarity comparison. The classification accuracies are 91.97% and 92.31% corresponding to 10-fold and leave-one-out cross-validation. After scanning sequences without FP annotations, this model discovered 53,946 np-FPs. The statistical results on FPs or np-FPs reveal that FP is a conserved and hydrophobic domain. The FP software programmed for windows environment is available at https://sourceforge.net/projects/fptool/files/?source=navbar. | A computational model for predicting fusion peptide of retroviruses |
S1476927115300670 | Ion channels are integral membrane proteins that are responsible for controlling the flow of ions across the cell. There are various biological functions that are performed by different types of ion channels. Therefore for new drug discovery it is necessary to develop a novel computational intelligence techniques based approach for the reliable prediction of ion channels families and their subfamilies. In this paper random forest based approach is proposed to predict ion channels families and their subfamilies by using sequence derived features. Here, seven feature vectors are used to represent the protein sample, including amino acid composition, dipeptide composition, correlation features, composition, transition and distribution and pseudo amino acid composition. The minimum redundancy and maximum relevance feature selection is used to find the optimal number of features for improving the prediction performance. The proposed method achieved an overall accuracy of 100%, 98.01%, 91.5%, 93.0%, 92.2%, 78.6%, 95.5%, 84.9%, MCC values of 1.00, 0.92, 0.88, 0.88, 0.90, 0.79, 0.91, 0.81 and ROC area values of 1.00, 0.99, 0.99, 0.99, 0.99, 0.95, 0.99 and 0.96 using 10-fold cross validation to predict the ion channels and non-ion channels, voltage gated ion channels and ligand gated ion channels, four subfamilies (calcium, potassium, sodium and chloride) of voltage gated ion channels, and four subfamilies of ligand gated ion channels and predict subfamilies of voltage gated calcium, potassium, sodium and chloride ion channels respectively. | An efficient approach for the prediction of ion channels and their subfamilies |
S1476927115300682 | Background Many studies have shown roles of microRNAs on human disease and a number of computational methods have been proposed to predict such associations by ranking candidate microRNAs according to their relevance to a disease. Among them, machine learning-based methods usually have a limitation in specifying non-disease microRNAs as negative training samples. Meanwhile, network-based methods are becoming dominant since they well exploit a “disease module” principle in microRNA functional similarity networks. Of which, random walk with restart (RWR) algorithm-based method is currently state-of-the-art. The use of this algorithm was inspired from its success in predicting disease gene because the “disease module” principle also exists in protein interaction networks. Besides, many algorithms designed for webpage ranking have been successfully applied in ranking disease candidate genes because web networks share topological properties with protein interaction networks. However, these algorithms have not yet been utilized for disease microRNA prediction. Methods We constructed microRNA functional similarity networks based on shared targets of microRNAs, and then we integrated them with a microRNA functional synergistic network, which was recently identified. After analyzing topological properties of these networks, in addition to RWR, we assessed the performance of (i) PRINCE (PRIoritizatioN and Complex Elucidation), which was proposed for disease gene prediction; (ii) PageRank with Priors (PRP) and K-Step Markov (KSM), which were used for studying web networks; and (iii) a neighborhood-based algorithm. Results Analyses on topological properties showed that all microRNA functional similarity networks are small-worldness and scale-free. The performance of each algorithm was assessed based on average AUC values on 35 disease phenotypes and average rankings of newly discovered disease microRNAs. As a result, the performance on the integrated network was better than that on individual ones. In addition, the performance of PRINCE, PRP and KSM was comparable with that of RWR, whereas it was worst for the neighborhood-based algorithm. Moreover, all the algorithms were stable with the change of parameters. Final, using the integrated network, we predicted six novel miRNAs (i.e., hsa-miR-101, hsa-miR-181d, hsa-miR-192, hsa-miR-423-3p, hsa-miR-484 and hsa-miR-98) associated with breast cancer. Conclusions Network-based ranking algorithms, which were successfully applied for either disease gene prediction or for studying social/web networks, can be also used effectively for disease microRNA prediction. | Network-based ranking methods for prediction of novel disease associated microRNAs |
S1476927115300694 | A small yet diverse xanthone library was build and computationally docked against wild type Pf-DHFR by Molegro Virtual Docker (MolDock). For analysis of results an integrated approach based on re-ranking, scaling (based on heavy atom counts), pose clustering and visual inspection was implemented. Standard methods such as self-docking (for docking), EF analysis, average rank determinations (for size normalization), and cluster quality indices (for pose clustering) were used for validation of results. Three compounds X5, X113A and X164B displayed contact footprints similar to the known inhibitors with good scores. Finally, 16 compounds were extracted from ZINC data base by similarity based screening, docking score and drug/lead likeness. Out of these 16 compounds, 11 displayed very close contact footprints to experimentally known inhibitors, indicating there potential utility in further drug discovery efforts. | A multilayer screening approach toward the discovery of novel Pf-DHFR inhibitors |
S1476927115300712 | Steroid hormones are involved on cell growth, development and differentiation. Such effects are often mediated by steroid receptors. One paradigmatic example of this coupling is the estrogen signaling pathway. Its dysregulation is involved in most tumors of the mammary gland. It is thus an important pharmacological target in breast cancer. This pathway, however, crosstalks with several other molecular pathways, a fact that may have consequences for the effectiveness of hormone modulating drug therapies, such as tamoxifen. For this work, we performed a systematic analysis of the major routes involved in crosstalk phenomena with the estrogen pathway – based on gene expression experiments (819 samples) and pathway analysis (493 samples) – for biopsy-captured tissue and contrasted in two independent datasets with in vivo and in vitro pharmacological stimulation. Our results confirm the presence of a number of crosstalk events across the estrogen signaling pathway with others that are dysregulated in different molecular subtypes of breast cancer. These may be involved in proliferation, invasiveness and apoptosis-evasion in patients. The results presented may open the way to new designs of adjuvant and neoadjuvant therapies for breast cancer treatment. | Crosstalk events in the estrogen signaling pathway may affect tamoxifen efficacy in breast cancer molecular subtypes |
S147692711530075X | Although image-based phenotypic assays are considered a powerful tool for siRNA library screening, the reproducibility and biological implications of various image-based assays are not well-characterized in a systematic manner. Here, we compared the resolution of high throughput assays of image-based cell count and typical cell viability measures for cancer samples. It was found that the optimal plating density of cells was important to obtain maximal resolution in both types of assays. In general, cell counting provided better resolution than the cell viability measure in diverse batches of siRNAs. In addition to cell count, diverse image-based measures were simultaneously collected from a single screening and showed good reproducibility in repetitions. They were classified into a few functional categories according to biological process, based on the differential patterns of hit (i.e., siRNAs) prioritization from the same screening data. The presented systematic analyses of image-based parameters provide new insight to a multitude of applications and better biological interpretation of high content cell-based assays. | Analysis of image-based phenotypic parameters for high throughput gene perturbation assays |
S1476927115300773 | GolS genes stand as potential candidate genes for molecular breeding and/or engineering programs in order for improving abiotic stress tolerance in plant species. In this study, a total of six galactinol synthase (GolS) genes/proteins were retrieved for Solanum lycopersicum and Brachypodium distachyon. GolS protein sequences were identified to include glyco_transf_8 (PF01501) domain structure, and to have a close molecular weight (36.40–39.59kDa) and amino acid length (318–347 aa) with a slightly acidic pI (5.35–6.40). The sub-cellular location was mainly predicted as cytoplasmic. S. lycopersicum genes located on chr 1 and 2, and included one segmental duplication while genes of B. distachyon were only on chr 1 with one tandem duplication. GolS sequences were found to have well conserved motif structures. Cis-acting analysis was performed for three abiotic stress responsive elements, including ABA responsive element (ABRE), dehydration and cold responsive elements (DRE/CRT) and low-temperature responsive element (LTRE). ABRE elements were found in all GolS genes, except for SlGolS4; DRE/CRT was not detected in any GolS genes and LTRE element found in SlGolS1 and BdGolS1 genes. AU analysis in UTR and ORF regions indicated that SlGolS and BdGolS mRNAs may have a short half-life. SlGolS3 and SlGolS4 genes may generate more stable transcripts since they included AATTAAA motif for polyadenylation signal POLASIG2. Seconder structures of SlGolS proteins were well conserved than that of BdGolS. Some structural divergences were detected in 3D structures and predicted binding sites exhibited various patterns in GolS proteins. | Genome-wide identification of galactinol synthase (GolS) genes in Solanum lycopersicum and Brachypodium distachyon |
S147692711530089X | Bacteria are increasingly resistant to existing antibiotics, which target a narrow range of pathways. New methods are needed to identify targets, including repositioning targets among distantly related species. We developed a novel combination of systems and structural modeling and bioinformatics to reposition known antibiotics and targets to new species. We applied this approach to Mycoplasma genitalium, a common cause of urethritis. First, we used quantitative metabolic modeling to identify enzymes whose expression affects the cellular growth rate. Second, we searched the literature for inhibitors of homologs of the most fragile enzymes. Next, we used sequence alignment to assess that the binding site is shared by M. genitalium, but not by humans. Lastly, we used molecular docking to verify that the reported inhibitors preferentially interact with M. genitalium proteins over their human homologs. Thymidylate kinase was the top predicted target and piperidinylthymines were the top compounds. Further work is needed to experimentally validate piperidinylthymines. In summary, combined systems and structural modeling is a powerful tool for drug repositioning. | A combined systems and structural modeling approach repositions antibiotics for Mycoplasma genitalium |
S1476927115300906 | Mycobacterium tuberculosis (Mtb) is the causative organism of tuberculosis. Extensively drug resistant strains and latency have posed formidable challenges in the treatment of tuberculosis. The current study addresses an alpha/beta hydrolase fold bearing enzyme, epoxide hydrolase Rv1938 from Mtb. Epoxide hydrolases are involved in detoxification processes, catabolism and regulation of signaling molecules. Using GROMACS, a 100ns Molecular Dynamics (MD) simulation was performed for Rv1938. Cavities were identified within the protein at various time frames of the simulation and their volumes were computed. During MD simulation, in addition to the substrate binding cavity, opening of two new cavities located behind the active site was observed. These cavities may be similar to the backdoor proposed for acetylcholinesterase. Structural superimposition of epoxide hydrolase from Mtb with the epoxide hydrolase of Agrobacterium radiobacter1 AD1 (Ephy) indicates that cavity1 in Mtb lies at an identical position to that of the water tunnel in Ephy. Further, docking of the substrate and an inhibitor with protein structures obtained from MD simulation at various time frames was also performed. The potential role of these cavities is discussed. | Cavities create a potential back door in epoxide hydrolase Rv1938 from Mycobacterium tuberculosis—A molecular dynamics simulation study |
S1476927115300918 | Glucokinase (GK) plays a critical role in maintaining glucose homeostasis in the human liver and pancreas. In the liver, the activity of GK is modulated by the glucokinase regulatory protein (GKRP) which functions as a competitive inhibitor of glucose to bind to GK. Moreover, the inhibitory intensity of GKRP–GK is suppressed by fructose 1-phosphate (F1P), and reinforced by fructose 6-phosphate (F6P). Here, we employed a series of computational techniques to explore the interactions of fructose phosphates with GKRP. Calculation results reveal that F1P and F6P can bind to the same active site of GKRP with different binding modes, and electrostatic interaction provides a major driving force for the ligand binding. The presence of fructose phosphate severely influences the motions of protein and the conformational space, and the structural change of sugar phosphate influences its interactions with GKRP, leading to a large conformational rearrangement of loop2 in the SIS2 domain. In particular, the binding of F6P to GKRP facilitates the protruding loop2 contacting with GK to form the stable GK–GKRP complex. The conserved residues 179–184 of GKRP play a major role in the binding of phosphate group and maintaining the stability of GKRP. These results may provide deep insight into the regulatory mechanism of GKRP to the activity of GK. | Theoretical investigations on the interactions of glucokinase regulatory protein with fructose phosphates |
S147692711530092X | Motivation Protein fold space is a conceptual framework where all possible protein folds exist and ideas about protein structure, function and evolution may be analyzed. Classification of protein folds in this space is commonly achieved by using similarity indexes and/or machine learning approaches, each with different limitations. Results We propose a method for constructing a compact vector space model of protein fold space by representing each protein structure by its residues local contacts. We developed an efficient method to statistically test for the separability of points in a space and showed that our protein fold space representation is learnable by any machine-learning algorithm. Availability An API is freely available at https://code.google.com/p/pyrcc/. | Machine Learnable Fold Space Representation based on Residue Cluster Classes |
S1476927115300931 | The growth and spread of drug resistance in bacteria have been well established in both mankind and beasts and thus is a serious public health concern. Due to the increasing problem of drug resistance, control of infectious diseases like diarrhea, pneumonia etc. is becoming more difficult. Hence, it is crucial to understand the underlying mechanism of drug resistance mechanism and devising novel solution to address this problem. Multidrug And Toxin Extrusion (MATE) proteins, first characterized as bacterial drug transporters, are present in almost all species. It plays a very important function in the secretion of cationic drugs across the cell membrane. In this work, we propose SVM based method for prediction of MATE proteins. The data set employed for training consists of 189 non-redundant protein sequences, that are further classified as positive (63 sequences) set comprising of sequences from MATE family, and negative (126 sequences) set having protein sequences from other transporters families proteins and random protein sequences taken from NCBI while in the test set, there are 120 protein sequences in all (8 in positive and 112 in negative set). The model was derived using Position Specific Scoring Matrix (PSSM) composition and achieved an overall accuracy 92.06%. The five-fold cross validation was used to optimize SVM parameter and select the best model. The prediction algorithm presented here is implemented as a freely available web server MATEPred, which will assist in rapid identification of MATE proteins. | MATEPRED-A-SVM-Based Prediction Method for Multidrug And Toxin Extrusion (MATE) Proteins |
S1476927115300943 | Detection of protein complexes is very important to understand the principles of cellular organization and function. Recently, large protein–protein interactions (PPIs) networks have become available using high-throughput experimental techniques. These networks make it possible to develop computational methods for protein complex detection. Most of the current methods rely on the assumption that protein complex as a module has dense structure. However complexes have core-attachment structure and proteins in a complex core share a high degree of functional similarity, so it expects that a core has high weighted density. In this paper we present a Core-Attachment based method for protein complex detection from Weighted PPI Interactions using clustering coefficient and weighted density. Experimental results show that the proposed method, CAMWI improves the accuracy of protein complex detection. | CAMWI: Detecting protein complexes using weighted clustering coefficient and weighted density |
S1476927115300955 | The heat shock protein 90α (HSP90α) provides a promising molecular target for cancer therapy. A series of novel benzolactam inhibitors exhibited distinct inhibitory activity for HSP90α. However, the structural basis for the impact of distinct R1 substituent groups of nine benzolactam inhibitors on HSP90α binding affinities remains unknown. In this study, we carried out molecular docking, molecular dynamics (MD) simulations, and molecular mechanics and generalized Born/surface area (MM–GBSA) binding free energy calculations to address the differences. Molecular docking studies indicated that all nine compounds presented one conformation in the ATP-binding site of HSP90α N-terminal domain. MD simulations and subsequent MM–GBSA calculations revealed that the hydrophobic interactions between all compounds and HSP90α contributed the most to the binding affinity and a good linear correlation was obtained between the calculated and the experimental binding free energies (R =0.88). The per residue decomposition revealed that the most remarkable differences of residue contributions were found in the residues Ala55, Ile96, and Leu107 defining a hydrophobic pocket for the R1 group, consistent with the analysis of binding modes. This study may be helpful for the future design of novel HSP90α inhibitors. | Structural and energetic insight into the interactions between the benzolactam inhibitors and tumor marker HSP90α |
S1476927115300967 | We present the mechanism of interaction of Wnt network module, which is responsible for periodic somitogenesis, with p53 regulatory network, which is one of the main regulators of various cellular functions, and switching of various oscillating states by investigating p53–Wnt model. The variation in Nutlin concentration in p53 regulating network drives the Wnt network module to different states, stabilized, damped and sustain oscillation states, and even to cycle arrest. Similarly, the change in Axin2 concentration in Wnt could able to modulate the p53 dynamics at these states. We then solve the set of coupled ordinary differential equations of the model using quasi steady state approximation. We, further, demonstrate the change of p53 and GSK3 interaction rate, due to hypothetical catalytic reaction or external stimuli, can able to regulate the dynamics of the two network modules, and even can control their dynamics to protect the system from cycle arrest (apoptosis). | Dynamics of p53 and Wnt cross talk |
S1476927115300992 | MicroRNAs (miRNAs) are small non-coding RNAs of ∼19–24 nucleotides (nt) in length and considered as potent regulators of gene expression at transcriptional and post-transcriptional levels. Here we report the identification and characterization of 15 conserved miRNAs belonging to 13 families from Rauvolfia serpentina through in silico analysis of available nucleotide dataset. The identified mature R. serpentina miRNAs (rse-miRNAs) ranged between 20 and 22nt in length, and the average minimal folding free energy index (MFEI) value of rse-miRNA precursor sequences was found to be –0.815kcal/mol. Using the identified rse-miRNAs as query, their potential targets were predicted in R. serpentina and other plant species. Gene Ontology (GO) annotation showed that predicted targets of rse-miRNAs include transcription factors as well as genes involved in diverse biological processes such as primary and secondary metabolism, stress response, disease resistance, growth, and development. Few rse-miRNAs were predicted to target genes of pharmaceutically important secondary metabolic pathways such as alkaloids and anthocyanin biosynthesis. Phylogenetic analysis showed the evolutionary relationship of rse-miRNAs and their precursor sequences to homologous pre-miRNA sequences from other plant species. The findings under present study besides giving first hand information about R. serpentina miRNAs and their targets, also contributes towards the better understanding of miRNA-mediated gene regulatory processes in plants. | Transcriptome-wide identification of Rauvolfia serpentina microRNAs and prediction of their potential targets |
S1476927115301018 | The presence of repetitive or non-unique DNA persisting over sizable regions of a eukaryotic genome can hinder the genome's successful de novo assembly from short reads: ambiguities in assigning genome locations to the non-unique subsequences can result in premature termination of contigs and thus overfragmented assemblies. Fungal mitochondrial (mtDNA) genomes are compact (typically less than 100kb), yet often contain short non-unique sequences that can be shown to impede their successful de novo assembly in silico. Such repeats can also confuse processes in the cell in vivo. A well-studied example is ectopic (out-of-register, illegitimate) recombination associated with repeat pairs, which can lead to deletion of functionally important genes that are located between the repeats. Repeats that remain conserved over micro- or macroevolutionary timescales despite such risks may indicate functionally or structurally (e.g., for replication) important regions. This principle could form the basis of a mining strategy for accelerating discovery of function in genome sequences. We present here our screening of a sample of 11 fully sequenced fungal mitochondrial genomes by observing where exact k-mer repeats occurred several times; initial analyses motivated us to focus on 17-mers occurring more than three times. Based on the diverse repeats we observe, we propose that such screening may serve as an efficient expedient for gaining a rapid but representative first insight into the repeat landscapes of sparsely characterized mitochondrial chromosomes. Our matching of the flagged repeats to previously reported regions of interest supports the idea that systems of persisting, non-trivial repeats in genomes can often highlight features meriting further attention. | From NGS assembly challenges to instability of fungal mitochondrial genomes: A case study in genome complexity |
S1476927115301055 | Structure of the cAMP-dependent protein kinase catalytic subunit, where the asparagine residue 326 was replaced with acrylodan-cystein conjugate to implement this fluorescence reporter group into the enzyme, was modeled by molecular dynamics (MD) method and the positioning of the dye molecule in protein structure was characterized at temperatures 300K, 500K and 700K. It was found that the acrylodan moiety, which fluorescence is very sensitive to solvating properties of its microenvironment, was located on the surface of the native protein at 300K that enabled its partial solvation with water. At high temperatures the protein structure significantly changed, as the secondary and tertiary structure elements were unfolded and these changes were sensitively reflected in positioning of the dye molecule. At 700K complete unfolding of the protein occurred and the reporter group was entirely expelled into water. However, at 500K an intermediate of the protein unfolding process was formed, where the fluorescence reporter group was directed towards the protein interior and buried in the core of the formed molten globule state. This different positioning of the reporter group was in agreement with the two different shifts of emission spectrum of the covalently bound acrylodan, observed in the unfolding process of the protein. | Computational modeling of acrylodan-labeled cAMP dependent protein kinase catalytic subunit unfolding |
S1476927115301079 | A major theme in constraint-based modeling is unifying experimental data, such as biochemical information about the reactions that can occur in a system or the composition and localization of enzyme complexes, with high-throughput data including expression data, metabolomics, or DNA sequencing. The desired result is to increase predictive capability and improve our understanding of metabolism. The approach typically employed when only gene (or protein) intensities are available is the creation of tissue-specific models, which reduces the available reactions in an organism model, and does not provide an objective function for the estimation of fluxes. We develop a method, flux assignment with LAD (least absolute deviation) convex objectives and normalization (FALCON), that employs metabolic network reconstructions along with expression data to estimate fluxes. In order to use such a method, accurate measures of enzyme complex abundance are needed, so we first present an algorithm that addresses quantification of complex abundance. Our extensions to prior techniques include the capability to work with large models and significantly improved run-time performance even for smaller models, an improved analysis of enzyme complex formation, the ability to handle large enzyme complex rules that may incorporate multiple isoforms, and either maintained or significantly improved correlation with experimentally measured fluxes. FALCON has been implemented in MATLAB and ATS, and can be downloaded from: https://github.com/bbarker/FALCON. ATS is not required to compile the software, as intermediate C source code is available. FALCON requires use of the COBRA Toolbox, also implemented in MATLAB. | A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data |
S1476927115301080 | Circular RNAs (circRNAs) were found more than 30 years ago, but have been treated as molecular flukes in a long time. Combining deep sequencing studies with bioinformatics technique, thousands of endogenous circRNAs have been found in mammalian cells, and some researchers have proved that several circRNAs act as competing endogenous RNAs (ceRNAs) to regulate gene expression. However, the mechanism by which the precursor mRNA to be transformed into a circular RNA or a linear mRNA is largely unknown. In this paper, we attempted to bioinformatically identify shared genomic features that might further elucidate the mechanism of formation and proposed a SVM-based model to distinguish circRNAs from non-circularized, expressed exons. Firstly, conformational and thermodynamic dinucleotide properties in the flanking introns were extracted as potential features. Secondly, two feature selection methods were applied to gain the optimal feature subset. Our 10-fold cross-validation results showed that the model can be used to distinguish circRNAs from non-circularized, expressed exons with an Sn of 0.884, Sp of 0.900, ACC of 0.892, MCC of 0.784, respectively. The identification results suggest that conformational and thermodynamic properties in the flanking introns are closely related to the formation of circRNAs. Datasets and the tool involved in this paper are all available at https://sourceforge.net/projects/predicircrnatool/files/. | Computational identification of circular RNAs based on conformational and thermodynamic properties in the flanking introns |
S1476927115301092 | The sudden emergence of a human infecting strain of H7N9 influenza virus in China in 2013 leading to fatalities in about 30% of the cases has caused wide concern that additional mutations in the strain leading to human to human transmission could lead to a deadly pandemic. It may happen in a short time span as the outbreak of H7N9 is more and more recurrent, which implies that H7N9 evolution is speeding up. H7N9 flu strains were not known to infect humans before this attack in China in February 2013 and it was solely an avian strain. While currently available drugs such as oseltamivir have been found to be largely effective against the H7N9, albeit with recent reported cases of development of resistance to the drug, there is a necessity to identify alternatives to combat this disease, especially if it assumes pandemic proportions. In our work, we have tried to investigate for the genetic changes in hemagglutinin (HA) protein sequence that lead to human infection by an avian infecting virus and identify possible peptide targets to design vaccines to control this upcoming risk. We identified three highly conserved regions in all H7 subtypes, of which one particular immunogenic surface exposed region was found to be well conserved in all human infecting H7N9 strains (accessed up to 27th March 2014). Compared to H7N9 avian strains, we identified two mutations in this conserved region at the receptor binding site of all post-February 2013 human-infecting H7N9China hemagglutinin protein sequences. One of the mutations is very close (3.6Å) to the hemagglutinin sialic acid binding pocket that may lead to better binding to human host’s sialic acid due to the changes in hydrophobicity of the microenvironment of the binding site. We found that the peptide region with these mutational changes that are specific for human infecting H7N9 virus possess the possibility of being used as target for a peptide vaccine. | H7N9 influenza outbreak in China 2013: In silico analyses of conserved segments of the hemagglutinin as a basis for the selection of peptide vaccine targets |
S1476927115301109 | Genetic evolution of carbonic anhydrase enzyme provides an interesting instance of functional similarity in spite of structural diversity of the members of a given family of enzymes. Phylogenetic analysis of α-, β- and γ-carbonic anhydrase was carried out to determine the evolutionary relationships among various members of the family with the enzyme marking its presence in a wide range of cellular and chromosomal locations. The presence of more than one class of enzymes in a particular organism was revealed by phylogenetic time tree. The evolutionary relationships among the members of animal, plant and microbial kingdom were developed. The study revises a long-established notion of kingdom-specificity of the different classes of carbonic anhydrases and provides a new version of the presence of multiple classes of carbonic anhydrases in a single organism and the presence of a given class of carbonic anhydrase across different kingdoms. | On origin and evolution of carbonic anhydrase isozymes: A phylogenetic analysis from whole-enzyme to active site |
S1476927115301122 | To better understand how enzyme localization affects enzyme activity we studied the cellular localization of the glycosyltransferase MurG, an enzyme necessary for cell wall synthesis at the spore during sporulation in the bacterium Bacillus subtilis. During sporulation MurG was gradually enriched to the membrane at the forespore and point mutations in a MurG helical domain disrupting its localization to the membrane caused severe sporulation defects, but did not affect localization nor caused detectable defects during exponential growth. We found that this localization is dependent on the phospholipid cardiolipin, as in strains where the cardiolipin-synthesizing genes were deleted, MurG levels were diminished at the forespore. Furthermore, in this cardiolipin-less strain, MurG localization during sporulation was rescued by external addition of purified cardiolipin. These results support localization as a critical factor in the regulation of proper enzyme function and catalysis. | Enzyme function is regulated by its localization |
S1476927115301250 | Tertiary protein structure prediction is one of the most challenging problems in structural bioinformatics. Despite the advances in algorithm development and computational strategies, predicting the folded structure of a protein only from its amino acid sequence remains as an unsolved problem. We present a new computational approach to predict the native-like three-dimensional structure of proteins. Conformational preferences of amino acid residues and secondary structure information were obtained from protein templates stored in the Protein Data Bank and represented as an Angle Probability List. Two knowledge-based prediction methods based on Genetic Algorithms and Particle Swarm Optimization were developed using this information. The proposed method has been tested with twenty-six case studies selected to validate our approach with different classes of proteins and folding patterns. Stereochemical and structural analysis were performed for each predicted three-dimensional structure. Results achieved suggest that the Angle Probability List can improve the effectiveness of metaheuristics used to predicted the three-dimensional structure of protein molecules by reducing its conformational search space. | APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction |
S1476927115301286 | A number of methods have been proposed in the literature of protein–protein interaction (PPI) network analysis for detection of clusters in the network. Clusters are identified by these methods using various graph theoretic criteria. Most of these methods have been found time consuming due to involvement of preprocessing and post processing tasks. In addition, they do not achieve high precision and recall consistently and simultaneously. Moreover, the existing methods do not employ the idea of core-periphery structural pattern of protein complexes effectively to extract clusters. In this paper, we introduce a clustering method named CPCA based on a recent observation by researchers that a protein complex in a PPI network is arranged as a relatively dense core region and additional proteins weakly connected to the core. CPCA uses two connectivity criterion functions to identify core and peripheral regions of the cluster. To locate initial node of a cluster we introduce a measure called DNQ (Degree based Neighborhood Qualification) index that evaluates tendency of the node to be part of a cluster. CPCA performs well when compared with well-known counterparts. Along with protein complex gold standards, a co-localization dataset has also been used for validation of the results. | Core and peripheral connectivity based cluster analysis over PPI network |
S147692711530133X | Genome-wide association studies and other genetic analyses have identified a large number of genes and variants implicating a variety of disease etiological mechanisms. It is imperative for the study of human diseases to put these genetic findings into a coherent functional context. Here we use system biology tools to examine disease connections of five master genes for CD4+ T cell subtypes (TBX21, GATA3, RORC, BCL6, and FOXP3). We compiled a list of genes functionally interacting (protein–protein interaction, or by acting in the same pathway) with the master genes, then we surveyed the disease connections, either by experimental evidence or by genetic association. Embryonic lethal genes (also known as essential genes) are over-represented in master genes and their interacting genes (55% versus 40% in other genes). Transcription factors are significantly enriched among genes interacting with the master genes (63% versus 10% in other genes). Predicted haploinsufficiency is a feature of most these genes. Disease-connected genes are enriched in this list of genes: 42% of these genes have a disease connection according to Online Mendelian Inheritance in Man (OMIM) (versus 23% in other genes), and 74% are associated with some diseases or phenotype in a Genome Wide Association Study (GWAS) (versus 43% in other genes). Seemingly, not all of the diseases connected to genes surveyed were immune related, which may indicate pleiotropic functions of the master regulator genes and associated genes. | A survey of disease connections for CD4+ T cell master genes and their directly linked genes |
S1476927115301365 | The metabolic role of 6-phosphogluconate dehydrogenase (gnd) under anaerobic conditions with respect to succinate production in Escherichia coli remained largely unspecified. Herein we report what are to our knowledge the first metabolic gene knockout of gnd to have increased succinic acid production using both glucose and glycerol substrates in E. coli. Guided by a genome scale metabolic model, we engineered the E. coli host metabolism to enhance anaerobic production of succinic acid by deleting the gnd gene, considering its location in the boundary of oxidative and non-oxidative pentose phosphate pathway. This strategy induced either the activation of malic enzyme, causing up-regulation of phosphoenolpyruvate carboxylase (ppc) and down regulation of phosphoenolpyruvate carboxykinase (ppck) and/or prevents the decarboxylation of 6 phosphogluconate to increase the pool of glyceraldehyde-3-phosphate (GAP) that is required for the formation of phosphoenolpyruvate (PEP). This approach produced a mutant strain BMS2 with succinic acid production titers of 0.35gl−1 and 1.40gl−1 from glucose and glycerol substrates respectively. This work further clearly elucidates and informs other studies that the gnd gene, is a novel deletion target for increasing succinate production in E. coli under anaerobic condition using glucose and glycerol carbon sources. The knowledge gained in this study would help in E. coli and other microbial strains development for increasing succinate production and/or other industrial chemicals. | Model-guided metabolic gene knockout of gnd for enhanced succinate production in Escherichia coli from glucose and glycerol substrates |
S1476927115301444 | Structural class characterizes the overall folding type of a protein or its domain. Many methods have been proposed to improve the prediction accuracy of protein structural class in recent years, but it is still a challenge for the low-similarity sequences. In this study, we introduce a feature extraction technique based on auto cross covariance (ACC) transformation of position-specific score matrix (PSSM) to represent a protein sequence. Then support vector machine-recursive feature elimination (SVM-RFE) is adopted to select top K features according to their importance and these features are input to a support vector machine (SVM) to conduct the prediction. Performance evaluation of the proposed method is performed using the jackknife test on three low-similarity datasets, i.e., D640, 1189 and 25PDB. By means of this method, the overall accuracies of 97.2%, 96.2%, and 93.3% are achieved on these three datasets, which are higher than those of most existing methods. This suggests that the proposed method could serve as a very cost-effective tool for predicting protein structural class especially for low-similarity datasets. | A highly accurate protein structural class prediction approach using auto cross covariance transformation and recursive feature elimination |
S1476927115301456 | The intrinsically disordered proteins consist of partially structured regions linked to the unstructured stretches, which consequently form the transient and dynamic conformational ensembles. They undergo disorder to order transition upon binding their partners. Intrinsic disorder is attributed to histones H1, perceived as assemblers of chromatin structure and the regulators of DNA and proteins activity. In this work, the comparison of intrinsic disorder abundance in the histone H1 subtypes was performed both by the analysis of their amino acid composition and by the prediction of disordered stretches, as well as by identifying molecular recognition features (MoRFs) and ANCHOR protein binding regions (APBR) that are responsible for recognition and binding. Both human and model organisms—animals, plants, fungi and protists—have H1 histone subtypes with the properties typical of disordered state. They possess a significantly higher content of hydrophilic and charged amino acid residues, arranged in the long regions, covering over half of the whole amino acid residues in chain. Almost complete disorder corresponds to histone H1 terminal domains, including MoRFs and ANCHOR. Those motifs were also identified in a more ordered histone H1 globular domain. Compared to the control (globular and fibrous) proteins, H1 histones demonstrate the increased folding rate and a higher proportion of low-complexity segments. The results of this work indicate that intrinsic disorder is an inherent structural property of histone H1 subtypes and it is essential for establishing a protein conformation which defines functional outcomes affecting on DNA- and/or partner protein-dependent cell processes. | Abundance of intrinsic structural disorder in the histone H1 subtypes |
S1476927115301523 | Single nucleotide polymorphisms (SNPs) in transcription factor binding sites (TFBSs) within gene promoter region or enhancers can modify the transcription rate of genes related to complex diseases. These SNPs can be called regulatory SNPs (rSNPs). Data compiled from recent projects, such as the 1000 Genomes Project and ENCODE, has revealed essential information used to perform in silico prediction of the molecular and biological repercussions of SNPs within TFBS. However, most of these studies are very limited, as they only analyze SNPs in coding regions or when applied to promoters, and do not integrate essential biological data like TFBSs, expression profiles, pathway analysis, homotypic redundancy (number of TFBSs for the same TF in a region), chromatin accessibility and others, which could lead to a more accurate prediction. Our aim was to integrate different data in a biologically coherent method to analyze the proximal promoter regions of two antimicrobial peptide genes, DEFB1 and CAMP, that are associated with tuberculosis (TB) and HIV/AIDS. We predicted SNPs within the promoter regions that are more likely to interact with transcription factors (TFs). We also assessed the impact of homotypic redundancy using a novel approach called the homotypic redundancy weight factor (HWF). Our results identified 10 SNPs, which putatively modify the binding affinity of 24 TFs previously identified as related to TB and HIV/AIDS expression profiles (e.g. KLF5, CEBPA and NFKB1 for TB; FOXP2, BRCA1, CEBPB, CREB1, EBF1 and ZNF354C for HIV/AIDS; and RUNX2, HIF1A, JUN/AP-1, NR4A2, EGR1 for both diseases). Validating with the OregAnno database and cell-specific functional/non functional SNPs from additional 13 genes, our algorithm performed 53% sensitivity and 84.6% specificity to detect functional rSNPs using the DNAseI-HUP database. We are proposing our algorithm as a novel in silico method to detect true functional rSNPs in antimicrobial peptide genes. With further improvement, this novel method could be applied to other promoters in order to design probes and to discover new drug targets for complex diseases. | Predicting functional regulatory SNPs in the human antimicrobial peptide genes DEFB1 and CAMP in tuberculosis and HIV/AIDS |
S1476927115301535 | Eukaryotic protein kinases represent one of the largest gene families involved in diverse regulatory functions. WNK (With No Lysine) kinases are members of ser/thr protein kinase family, which lack conserved catalytic lysine (K) residue at protein kinase subdomain II and is replaced by either asparagine, serine or glycine residues. They are involved in regulation of flowering time, circadian rhythms and abiotic stresses in Arabidopsis thaliana. In the present study, we have identified 9 members of WNK in rice, showed resemblance to Arabidopsis and human WNK and clustered into five main clades phylogenetically. The predicted genes structure, bonafide conserved signature motif and domains strongly support their identity, as members of WNK kinase family. We have analyzed their chromosomal distribution, physio-chemical properties, subcellular localizations and cis-elements in the promoter regions in silico. Further, transcript analysis of OsWNK by qRT-PCR revealed their differential regulation in tissue specific and abiotic stresses libraries. In conclusion, the identification of nine OsWNK and transcript level expression pattern under abiotic stress using qRT-PCR in rice will significantly contribute towards the understanding of WNK genes in monocots and thus provide a set up for functional genomics studies of WNK protein kinases. | Genome-wide identification and expression analysis of WNK kinase gene family in rice |
S1476927115301547 | Background Thiopurine S-methyltransferase (TPMT) detoxifies thiopurine drugs which are used for treatment of various diseases including inflammatory bowel disease (IBD), and hematological malignancies. Individual variation in TPMT activity results from mutations in TPMT gene. In this study, the effects of all the known missense mutations in TPMT enzyme were studied at the sequence and structural level Methods A broad set of bioinformatic tools was used to assess all the known missense mutations affecting enzyme activity. The effects of these mutations on protein stability, aggregation propensity, and residue interaction network were analyzed. Results Our results indicate that the missense mutations have diverse effects on TPMT structure and function. Stability and aggregation propensities are affected by various mutations. Several mutations also affect residues in ligand binding site. Conclusions In vitro study of missense mutation is laborious and time-consuming. However, computational methods can be used to obtain information about effects of missense mutations on protein structure. In this study, the effects of most of the mutations on enzyme activity could be explained by computational methods. Thus, the present approach can be used for understanding the protein structure-function relationships. | Structural and functional impact of missense mutations in TPMT: An integrated computational approach |
S1476927115301572 | Among computationally predicted and experimentally validated plant miRNAs, several are conserved across species boundaries in the plant kingdom. In this study, a combined experimental-in silico computational based approach was adopted for the identification and characterization of miRNAs in Humulus lupulus (hop), which is widely cultivated for use by the brewing industry and apart from, used as a medicinal herb. A total of 22 miRNAs belonging to 17 miRNA families were identified in hop following comparative computational approach and EST-based homology search according to a series of filtering criteria. Selected miRNAs were validated by end-point PCR and quantitative reverse transcription-polymerase chain reaction (qRT-PCR), confirmed the existence of conserved miRNAs in hop. Based on the characteristic that miRNAs exhibit perfect or nearly perfect complementarity with their targeted mRNA sequences, a total of 47 potential miRNA targets were identified in hop. Strikingly, the majority of predicted targets were belong to transcriptional factors which could regulate hop growth and development, including leaf, root and even cone development. Moreover, the identified miRNAs may also be involved in other cellular and metabolic processes, such as stress response, signal transduction, and other physiological processes. The cis-regulatory elements relevant to biotic and abiotic stress, plant hormone response, flavonoid biosynthesis were identified in the promoter regions of those miRNA genes. Overall, findings from this study will accelerate the way for further researches of miRNAs, their functions in hop and shows a path for the prediction and analysis of miRNAs to those species whose genomes are not available. | Computational exploration of microRNAs from expressed sequence tags of Humulus lupulus, target predictions and expression analysis |
S1476927115301584 | Drug resistant tuberculosis has threatened all the advances that have been made in TB control at the global stage in the last few decades. DNA gyrase enzymes are an excellent target for antibacterial drug discovery as they are involved in essential functions like DNA replication. Here we report, a successful application of high throughput virtual screening (HTVS) to identify an inhibitor of Mycobacterium DNA gyrase targeting the wild type and the most prevalent three double mutants of quinolone resistant DNA gyrase namely A90V+D94G, A74S+D94G and A90V+S91P. HTVS of 179.299 compounds gave five compounds with significant binding affinity. Extra presicion (XP) docking and MD simulations gave a clear view of their interaction pattern. Among them, chebulinic acid (CA), a phytocompound obtained from Terminalia chebula was the most potent inhibitor with significantly high XP docking score, −14.63, −16.46, −15.94 and −15.11 against wild type and three variants respectively. Simulation studies for a period of 16ns indicated stable DNA gyrA–CA complex formation. This stable binding would result in inhibition of the enzyme by two mechanisms. Firstly, binding of CA causes displacement of catalytic Tyr129 away from its target DNA-phosphate molecule from 1.6Å to 3.8–7.3Å and secondly, by causing steric hindrance to the binding of DNA strand at DNA binding site of enzyme. The combined effect would result in loss of cleavage and religation activity of enzyme leading to bactericidal effect on tuberculosis. This phytocompound displays desirable quality for carrying forward as a lead compound for anti-tuberculosis drug development. The results presented here are solely based on computations and need to be validated experimentally in order to assert the proposed mechanism of action. | Identification of chebulinic acid as potent natural inhibitor of M. tuberculosis DNA gyrase and molecular insights into its binding mode of action |
S1476927115301651 | Mycoplasma pneumoniae type 2a strain 309 is a simplest known bacterium and is the primary cause of community acquired pneumonia in the children. It mainly causes severe atypical pneumonia as well as several other non-pulmonary manifestations such as neurological, hepatic, hemolytic anemia, cardiac diseases and polyarthritis. The size of M. pneumoniae genome (Accession number: NC_016807.1) is relatively smaller as compared to other bacteria and contains 707 functional proteins, in which 204 are classified as hypothetical proteins (HPs) because of the unavailability of experimentally validated functions. The functions of the HPs were predicted by integrating a variety of protein classification systems, motif discovery tools as well as methods that are based on characteristic features obtained from the protein sequence and metabolic pathways. The probable functions of 83HPs were predicted successfully. The accuracy of the diverse tools used in the adopted pipeline was evaluated on the basis of statistical techniques of Receiver Operating Characteristic (ROC), which indicated the reliability of the functional predictions. Furthermore, the virulent HPs present in the set of 83 functionally annotated proteins were predicted by using the Bioinformatics tools and the conformational behaviours of the proteins with highest virulence scores were studied by using the molecular dynamics (MD) simulations. This study will facilitate in the better understanding of various drug resistance and pathogenesis mechanisms present in the M. pneumoniae and can be utilized in designing of better therapeutic agents. | In silico approaches for the identification of virulence candidates amongst hypothetical proteins of Mycoplasma pneumoniae 309 |
S1476927115301705 | Multiple ligand simultaneous docking, a computational approach is used to study the concurrent interactions between substrate and the macromolecule binding together in the presence of an inhibitor. The present investigation deals with the study of the effect of different inhibitors on binding of substrate to the protein Polyphenoloxidase (PPO). The protein was isolated from Mucuna pruriens and confirmed as tyrosinases involved in l-DOPA production. The activity was measured using different inhibitors at different concentrations taking catechol as substrate. A high-throughput binding study was conducted to compare the binding orientations of individual ligands and multiple ligands employing Autodock 4.2. The results of single substrate docking showed a better binding of urea with the binding energy of −3.48kJmol−1 and inter molecular energy of −3.48kJmol−1 while the results of MLSD revealed that ascorbic acid combined with the substrate showed better inhibition with a decreased binding energy of −2.37kJmol−1. | Multiple ligand simultaneous docking (MLSD): A novel approach to study the effect of inhibitors on substrate binding to PPO |
S1476927115301717 | Antimicrobial peptides have emerged as new therapeutic agents for fighting multi-drug-resistant bacteria. However, the process of optimizing peptide antimicrobial activity and specificity using large peptide libraries is both tedious and expensive. Therefore, computational techniques had to be applied for process optimization. In this work, the representation of the molecular structure of peptides (mastoparan analogs) by a sequence of amino acids has been used to establish quantitative structure—activity relationships (QSARs) for their antibacterial activity. The data for the studied peptides were split three times into the training, calibration and test sets. The Monte Carlo method was used as a computational technique for QSAR models calculation. The statistical quality of QSAR for the antibacterial activity of peptides for the external validation set was: n =7, r 2 =0.8067, s =0.248 (split 1); n =6, r 2 =0.8319, s =0.169 (split 2); and n =6, r 2 =0.6996, s =0.297 (split 3). The stated statistical parameters favor the presented QSAR models in comparison to 2D and 3D descriptor based ones. The Monte Carlo method gave a reasonably good prediction for the antibacterial activity of peptides. The statistical quality of the prediction is different for three random splits. However, the predictive potential is reasonably well for all cases. The presented QSAR modeling approach can be an attractive alternative of 3D QSAR at least for the described peptides. | QSAR modeling of the antimicrobial activity of peptides as a mathematical function of a sequence of amino acids |
S1476927115301821 | Lipocalins are short in sequence length and perform several important biological functions. These proteins are having less than 20% sequence similarity among paralogs. Experimentally identifying them is an expensive and time consuming process. The computational methods based on the sequence similarity for allocating putative members to this family are also far elusive due to the low sequence similarity existing among the members of this family. Consequently, the machine learning methods become a viable alternative for their prediction by using the underlying sequence/structurally derived features as the input. Ideally, any machine learning based prediction method must be trained with all possible variations in the input feature vector (all the sub-class input patterns) to achieve perfect learning. A near perfect learning can be achieved by training the model with diverse types of input instances belonging to the different regions of the entire input space. Furthermore, the prediction performance can be improved through balancing the training set as the imbalanced data sets will tend to produce the prediction bias towards majority class and its sub-classes. This paper is aimed to achieve (i) the high generalization ability without any classification bias through the diversified and balanced training sets as well as (ii) enhanced the prediction accuracy by combining the results of individual classifiers with an appropriate fusion scheme. Instead of creating the training set randomly, we have first used the unsupervised Kmeans clustering algorithm to create diversified clusters of input patterns and created the diversified and balanced training set by selecting an equal number of patterns from each of these clusters. Finally, probability based classifier fusion scheme was applied on boosted random forest algorithm (which produced greater sensitivity) and K nearest neighbour algorithm (which produced greater specificity) to achieve the enhanced predictive performance than that of individual base classifiers. The performance of the learned models trained on Kmeans preprocessed training set is far better than the randomly generated training sets. The proposed method achieved a sensitivity of 90.6%, specificity of 91.4% and accuracy of 91.0% on the first test set and sensitivity of 92.9%, specificity of 96.2% and accuracy of 94.7% on the second blind test set. These results have established that diversifying training set improves the performance of predictive models through superior generalization ability and balancing the training set improves prediction accuracy. For smaller data sets, unsupervised Kmeans based sampling can be an effective technique to increase generalization than that of the usual random splitting method. | Maximizing lipocalin prediction through balanced and diversified training set and decision fusion |
S1476927115301833 | Background Chloride Intracellular Channels (CLICs) are contributing to the regulation of multiple cellular functions. CLICs have been found over-expressed in several malignancies, and therefore they are currently considered as potential drug targets. The goal of our study was to assess the gene expression levels of the CLIC’s 1–6 in malignant pleural mesothelioma (MPM) as compared to controls. Methods We used gene expression data from a publicly available microarray dataset comparing MPM versus healthy tissue in order to investigate the differential expression profile of CLIC 1-6. False discovery rates were calculated and the interactome of the significantly differentially expressed CLICs was constructed and Functional Enrichment Analysis for Gene Ontologies (FEAGO) was performed. Results In MPM, the gene expressions of CLIC3 and CLIC4 were significantly increased compared to controls (p =0.001 and p <0.001 respectively). A significant positive correlation between the gene expressions of CLIC3 and CLIC4 (p =0.0008 and Pearson’s r =0.51) was found. Deming regression analysis provided an association equation between the CLIC3 and CLIC4 gene expressions: CLIC3=4.42CLIC4–10.07. Conclusions Our results indicate that CLIC3 and CLIC4 are over-expressed in human MPM. Moreover, their expressions correlate suggesting that they either share common gene expression inducers or that their products act synergistically. FAEGO showed that CLIC interactome might contribute to TGF beta signaling and water transport. | Transcriptional over-expression of chloride intracellular channels 3 and 4 in malignant pleural mesothelioma |
S1476927115301882 | It has been previously shown that the inhibition of mitogen activated protein kinase kinase (MEK) contributes to apoptosis and suppression of different cancer cells. Correspondingly, a number of MEK1/2 inhibitors have been designed and evaluated since 2001. However, they did not satisfy essential pharmacokinetic (PK) and pharmacodynamic (PD) properties thus, almost most of them were terminated in pre-clinical or clinical studies. This study aims to design new specific MEK1/2 inhibitors with improved PK/PD profiles to be used as alternative cancer medications. In first part of this study, a comprehensive screening, for the first time, was done on well-known MEK1/2 inhibitors using a number of computational programs such as AutoDock Tools 4.2 (ADT) and AutoDock Vina. Therefore a valuable training dataset as well as a reliable pharmacophore model were provided which were then used to design new inhibitors. According to the results of training dataset, Trametinib was determined as the best inhibitor provided, so far. So, Trametinib was used as the lead structure to design new inhibitors in this study. In second part of this investigation, a set of new allosteric MEK1/2 inhibitors were designed significantly improving the binding energy as well as the ADMET properties, suggesting more specific and stable ligand-receptor complexes. Consequently, the structures 14 and 15 of our inhibitors, as the most potent structures, are great substituents for Trametinib to be used and evaluated in clinical trials as alternative cancer drugs. | In silico investigation of new binding pocket for mitogen activated kinase kinase (MEK): Development of new promising inhibitors |
S1476927115302048 | Introduction PLA2G7 encodes Lp-PLA2 having role in the formation of atherosclerotic plaques by catalyzing its substrate, phosphatydilcholine (PC), to be pro-inflammatory substances. The increased risk for coronary artery disease (CAD) in Asian population has been related with this enzyme. 279Val→Phe variant was reported to have a protective role against CAD due to, in part, secretion defect or loss of enzymatic function. Therefore, We study folding kinetics and enzyme-substrate interaction in 279Val→Phe by using clinical and computational biology approach. Methods Polymorphisms were detected by genotyping among 103 acute myocardial infarction patients and 37 controls. Folding Lp-PLA2 was simulated using GROMACS software by assessing helicity, hydrogen bond formation and stability. The interactions of Lp-PLA2 and its substrate were simulated using Pyrx software followed by molecular dynamics simulation using YASARA software. Result Polymorphism of 279Val→Phe was represented by the change of nucleotide from G to T of 994th PLA2G7 gene. The folding simulation suggested a decreased percentage of α-helix, hydrogen bond formation, hydrogen bond stability and hydrophobicity in 279Val→Phe. The PC did not interact with active site of 279Val→Phe as paradoxically observed in 279 valine. 279Val→Phe polymorphism is likely to cause unstable binding to the substrate and decrease the enzymatic activity as observed in molecular dynamics simulations. The results of our computational biology study supported a protected effect of 279Val→Phe Polymorphism showed by the odd ratio for MI of 0.22 (CI 95% 0.035–1.37) in this study. Conclusion 279Val→Phe Polymorphism of Lp-PLA2 may lead to decrease the enzymatic activity via changes of folding kinetics and recognition to its substrate. | 279Val→Phe Polymorphism of lipoprotein-associated phospholipase A2 resulted in changes of folding kinetics and recognition to substrate |
S1476927115302140 | Cancer is a group of diseases that causes millions of deaths worldwide. Among cancers, Solid Tumors (ST) stand-out due to their high incidence and mortality rates. Disruption of cell–cell adhesion is highly relevant during tumor progression. Epithelial-cadherin (protein: E-cadherin, gene: CDH1) is a key molecule in cell–cell adhesion and an abnormal expression or/and function(s) contributes to tumor progression and is altered in ST. A systematic study was carried out to gather and summarize current knowledge on CDH1/E-cadherin and ST using bioinformatics resources. The DisGeNET database was exploited to survey CDH1-associated diseases. Reported mutations in specific ST were obtained by interrogating COSMIC and IntOGen tools. CDH1 Single Nucleotide Polymorphisms (SNP) were retrieved from the dbSNP database. DisGeNET analysis identified 609 genes annotated to ST, among which CDH1 was listed. Using CDH1 as query term, 26 disease concepts were found, 21 of which were neoplasms-related terms. Using DisGeNET ALL Databases, 172 disease concepts were identified. Of those, 80 ST disease-related terms were subjected to manual curation and 75/80 (93.75%) associations were validated. On selected ST, 489 CDH1 somatic mutations were listed in COSMIC and IntOGen databases. Breast neoplasms had the highest CDH1-mutation rate. CDH1 was positioned among the 20 genes with highest mutation frequency and was confirmed as driver gene in breast cancer. Over 14,000 SNP for CDH1 were found in the dbSNP database. This report used DisGeNET to gather/compile current knowledge on gene-disease association for CDH1/E-cadherin and ST; data curation expanded the number of terms that relate them. An updated list of CDH1 somatic mutations was obtained with COSMIC and IntOGen databases and of SNP from dbSNP. This information can be used to further understand the role of CDH1/E-cadherin in health and disease. | CDH1/E-cadherin and solid tumors. An updated gene-disease association analysis using bioinformatics tools |
S1476927115302152 | Parameter estimation for models with intrinsic stochasticity poses specific challenges that do not exist for deterministic models. Therefore, specialized numerical methods for parameter estimation in stochastic models have been developed. Here, we study whether dedicated algorithms for stochastic models are indeed superior to the naive approach of applying the readily available least squares algorithm designed for deterministic models. We compare the performance of the recently developed multiple shooting for stochastic systems (MSS) method designed for parameter estimation in stochastic models, a stochastic differential equations based Bayesian approach and a chemical master equation based techniques with the least squares approach for parameter estimation in models of ordinary differential equations (ODE). As test data, 1000 realizations of the stochastic models are simulated. For each realization an estimation is performed with each method, resulting in 1000 estimates for each approach. These are compared with respect to their deviation to the true parameter and, for the genetic toggle switch, also their ability to reproduce the symmetry of the switching behavior. Results are shown for different set of parameter values of a genetic toggle switch leading to symmetric and asymmetric switching behavior as well as an immigration-death and a susceptible-infected-recovered model. This comparison shows that it is important to choose a parameter estimation technique that can treat intrinsic stochasticity and that the specific choice of this algorithm shows only minor performance differences. original data (red), stochastic simulations with the MSS estimates (blue) and stochastic simulations with the LSQ estimates (dashed blue). Parameter name (column 1), true parameter value (column 2), averages of MSS estimates (column 3): Mean = 1 N ∑ i = 1 N θ ˆ i MSS , average relative errors of MSS method (column 4): ARE = 1 N ∑ i = 1 N | θ ˆ i MSS − θ 0 | θ 0 , average of the LSQ estimates (column 5): Mean = 1 N ∑ i = 1 N θ ˆ i LSQ , average relative errors of the LSQ method (column 6): ARE = 1 N ∑ i = 1 N | θ ˆ i LSQ − θ 0 | θ 0 , average of the SDE estimates (column 7): Mean = 1 N ∑ i = 1 N θ ˆ i SDE , average relative errors of the SDE method (column 8): ARE = 1 N ∑ i = 1 N | θ ˆ i SDE − θ 0 | θ 0 . | Comparison of approaches for parameter estimation on stochastic models: Generic least squares versus specialized approaches |
S1476927115302322 | Phytase is an enzyme which catalyzes the total hydrolysis of phytate to less phosphorylated myo-inositol derivatives and inorganic phosphate and digests the undigestable phytate part present in seeds and grains and therefore provides digestible phosphorus, calcium and other mineral nutrients. Phytases are frequently added to the feed of monogastric animals so that bioavailability of phytic acid-bound phosphate increases, ultimately enhancing the nutritional value of diets. The Bacillus phytase is very suitable to be used in animal feed because of its optimum pH with excellent thermal stability. Present study is aimed to perform an in silico comparative characterization and functional analysis of phytases from Bacillus amyloliquefaciens to explore physico-chemical properties using various bio-computational tools. All proteins are acidic and thermostable and can be used as suitable candidates in the feed industry. | Computational based functional analysis of Bacillus phytases |
S147692711530236X | The chaetognaths constitute a small and enigmatic phylum of little marine invertebrates. Both nuclear and mitochondrial genomes have numerous originalities, some phylum-specific. Until recently, their mitogenomes seemed containing only one tRNA gene (trnMet), but a recent study found in two chaetognath mitogenomes two and four tRNA genes. Moreover, apparently two conspecific mitogenomes have different tRNA gene numbers (one and two). Reanalyses by tRNAscan-SE and ARWEN softwares of the five available complete chaetognath mitogenomes suggest numerous additional tRNA genes from different types. Their total number never reaches the 22 found in most other invertebrates using that genetic code. Predicted error compensation between codon-anticodon mismatch and tRNA misacylation suggests translational activity by tRNAs predicted solely according to secondary structure for tRNAs predicted by tRNAscan-SE, not ARWEN. Numbers of predicted stop-suppressor (antitermination) tRNAs coevolve with predicted overlapping, frameshifted protein coding genes including stop codons. Sequence alignments in secondary structure prediction with non-chaetognath tRNAs suggest that the most likely functional tRNAs are in intergenic regions, as regular mt-tRNAs. Due to usually short intergenic regions, generally tRNA sequences partially overlap with flanking genes. Some tRNA pairs seem templated by sense-antisense strands. Moreover, 16S rRNA genes, but not 12S rRNAs, appear as tRNA nurseries, as previously suggested for multifunctional ribosomal-like protogenomes. | Cryptic tRNAs in chaetognath mitochondrial genomes |
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