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S1476927113000170 | In 1996, a trinucleotide circular code X is identified in genes of prokaryotes and eukaryotes (Arquès and Michel, 1996). In 2012, X motifs are identified in the transfer RNA (tRNA) Phe and 16S ribosomal RNA (Michel, 2012). A statistical analysis of X motifs in all available tRNAs of prokaryotes and eukaryotes in the genomic tRNA database (September 2012, http://lowelab.ucsc.edu/GtRNAdb/, Lowe and Eddy, 1997) is carried out here. For this purpose, a search algorithm of X motifs in a DNA sequence is developed. Two definitions allow to determine the occurrence probabilities of X motifs and the circular codes X, X 1 = P ( X ) and X 2 = P 2 ( X ) ( P being a circular permutation map applied on X) in a population of tRNAs. This approach identifies X motifs in the 5′ and/or 3′ regions of 16 isoaccepting tRNAs (except for the tRNAs Arg, His, Ser and Trp). The statistical analyses are performed on different and large tRNA populations according to the taxonomy (prokaryotes and eukaryotes), tRNA length and tRNA score. Finally, a circular code property observed in genes of prokaryotes and eukaryotes is identified in the 3′ regions of 19 isoaccepting tRNAs of prokaryotes and eukaryotes (except for the tRNA Leu). The identification of X motifs and a gene circular code property in tRNAs strengthens the concept proposed in Michel (2012) of a possible translation (framing) code based on a circular code. | Circular code motifs in transfer RNAs |
S1476927113000182 | Many biological processes depend on protein-based interactions, which are governed by central regions with higher binding affinities, the hot-spots. The O-ring theory or the “Water Exclusion” hypothesis states that the more deeply buried central regions are surrounded by areas, the null-spots, whose role would be to shelter the hot-spots from the bulk solvent. Although this theory is well-established for protein–protein interfaces, its applicability to other protein interfaces remains unclear. Our goal was to verify its applicability to protein–DNA interfaces. We performed Molecular Dynamics simulations in explicit solvent of several protein–DNA complexes and measured a variety of solvent accessible surface area (SASA) features, as well as, radial distribution functions of hot-spots and null-spots. Our aim was to test the influence of water in their coordination sphere. Our results show that hot-spots tend to have fewer water molecules in their neighborhood when compared to null-spots, and higher values of ΔSASA, which confirms their occlusion from solvent. This study provides evidence in support of the O-ring theory with its applicability to a new type of protein-based interface: protein–DNA. | Extending the applicability of the O-ring theory to protein–DNA complexes |
S1476927113000194 | We have developed a computer program, named PDBETA, that performs normal mode analysis (NMA) based on an elastic network model that uses dihedral angles as independent variables. Taking advantage of the relatively small number of degrees of freedom required to describe a molecular structure in dihedral angle space and a simple potential-energy function independent of atom types, we aimed to develop a program applicable to a full-atom system of any molecule in the Protein Data Bank (PDB). The algorithm for NMA used in PDBETA is the same as the computer program FEDER/2, developed previously. Therefore, the main challenge in developing PDBETA was to find a method that can automatically convert PDB data into molecular structure information in dihedral angle space. Here, we illustrate the performance of PDBETA with a protein–DNA complex, a protein–tRNA complex, and some non-protein small molecules, and show that the atomic fluctuations calculated by PDBETA reproduce the temperature factor data of these molecules in the PDB. A comparison was also made with elastic-network-model based NMA in a Cartesian-coordinate system. | Normal mode analysis based on an elastic network model for biomolecules in the Protein Data Bank, which uses dihedral angles as independent variables |
S1476927113000200 | Protein phosphorylation and O-GlcNAcylation are reciprocally regulated. As hyperphosphorylation is implicated in tau pathology, approaches have been exploited to reduce the magnitude of tau phosphorylation by increasing the level of tau O-GlcNAcylation. With mathematic models constructed to describe different kinetic scenarios, we analyzed the temporal change of an O-GlcNAcylated protein in contrast to that of the phosphorylated form upon inhibition of O-GlcNAcase (OGA). The analyses indicate that when degradation of the modified protein is negligible relative to the naked one, the magnitude of O-GlcNAcylated protein increase is proportional to the level of inhibition, while the extent of phosphorylated protein decline varies due to other factors. Furthermore, the increase of O-GlcNAcylated protein parallels with the decrease of phosphorylated form upon acute or short-term inhibition of OGA, as observed in many in vitro and short term in vivo studies. However, phosphorylated protein is predicted to return to its initial level while O-GlcNAcylated protein to achieve a higher steady level under sustained inhibition. This simulated result is in line with a recent report on long-term inhibition of OGA in transgenic mice. Noticeably, inhibition withdrawal is anticipated to cause a transient rise of phosphorylated protein. If degradation of modified proteins proceeds in addition to the naked one, the characteristic temporal profiles of each form in response to OGA inhibition would depend on the relative importance of individual degradation pathways. The models described herein may serve as a useful investigational tool that will provide insight into pharmacological intervention for tauopathies in particular and for reciprocally modulated reactions in general. | A dynamic view to the modulation of phosphorylation and O-GlcNAcylation by inhibition of O-GlcNAcase |
S1476927113000212 | The identification of protein–protein interactions (PPIs) and their networks is vitally important to systemically define and understand the roles of proteins in biological systems. In spite of development of numerous experimental systems to detect PPIs and diverse research on assessment of the quality of the obtained data, a consensus – highly reliable, almost complete – interactome of Saccharomyces cerevisiae is not presented yet. In this work, we proposed an unsupervised statistical approach to create a high-confidence yeast PPI network. For this, we assembled databases of interacting protein pairs for yeast and obtained an extremely large PPI dataset which comprises of 135154 non-redundant interactions between 6191 yeast proteins. A scoring scheme considering eight heterogeneous biological features resulted with a broad score distribution and a highly reliable network consisting of 29046 physical interactions with scores higher than the threshold value of 0.85, for which sensitivity, specificity and coverage were 86%, 68%, and 72%, respectively. We evaluated our method by comparing it with other scoring schemes and showed that reducing the noise inherent in experimental PPIs via our scoring scheme further increased the accuracy. Current study is expected to increase the efficiency of the methodologies in biological research which make use of protein interaction networks. | Assessment of high-confidence protein–protein interactome in yeast |
S1476927113000339 | In silico identification of T-cell epitopes is emerging as a new methodology for the study of epitope-based vaccines against viruses and cancer. In order to improve accuracy of prediction, we designed a novel approach, using epitope prediction methods in combination with molecular docking techniques, to identify MHC class I restricted T-cell epitopes. Analysis of the HIV-1 p24 protein and influenza virus matrix protein revealed that the present approach is effective, yielding prediction accuracy of over 80% with respect to experimental data. Subsequently, we applied such a method for prediction of T-cell epitopes in SARS coronavirus (SARS-CoV) S, N and M proteins. Based on available experimental data, the prediction accuracy is up to 90% for S protein. We suggest the use of epitope prediction methods in combination with 3D structural modelling of peptide-MHC-TCR complex to identify MHC class I restricted T-cell epitopes for use in epitope based vaccines like HIV and human cancers, which should provide a valuable step forward for the design of better vaccines and may provide in depth understanding about activation of T-cell epitopes by MHC binding peptides. | A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes |
S1476927113000340 | Genome-wide association studies, as a powerful approach for detecting common variants associated with diseases, have revealed many disease-associated loci. However, the traditional association analysis methods do not have enough power for detecting the effects of rare variants with limited sample size. As a solution to this problem, pooling rare variants by their functions into a composite variant provides an alternative way for identifying susceptible genes. In this paper, we propose a new pooling method to test the variant–disease association and to identify the functional rare variants related with the disease. Variants with smaller and larger risk measures defined as the ratio of allele frequencies between cases and controls are pooled and a chi-square test of the resultant pooled table is calculated. We vary the threshold of pooling over all possible values and use the maximal chi-square as test statistic. The maximal chi-square is in fact the global maximum over all possible poolings. Our approach is similar to the existing variable-threshold method, but we threshold on the risk measure instead of allele frequencies of controls. Simulation results show that our method performs better in both association testing and variant selection. | Rare variants analysis by risk-based variable-threshold method |
S1476927113000352 | In plants, flowering is a major biological phenomenon, which is regulated by an array of interactions occurring between biotic and abiotic factors. In our study, we have compared the expression profiles of flowering genes involved in the flowering pathway, which are influenced by conditions like photoperiod and temperature from seedling to heading developmental stages in two Oryza sativa indica varieties, viz., Zhenshan 97 and Minghui 63 using a expression network approach. Using the network expression approach, we found 17 co-expressed genes having the same expression profile pattern as three key photoperiod flowering genes Hd1, Ehd1 and Hd3a. We also demonstrated that these three co-expressed genes have a similar simulation pattern as temperature flowering genes. Based on our observations, we hypothesize that photoperiod and temperature regulate flowering pathways independently. The present study provides a basis for understanding the network of co-expressed genes involved in flowering pathway and presents a way to demonstrate the behavior of specific gene sets in specific cultivars. | Expression patterns of photoperiod and temperature regulated heading date genes in Oryza sativa |
S1476927113000364 | Malaria continues to affect millions of people annually. With the rise of drug resistant strains, the need for alternative treatments has become increasingly urgent. Recently, PfUCHL3 was identified as an essential deubiquitinating enzyme. The increasing number of drug target structures being solved has increased the feasibility of utilizing a structural comparative approach to identifying novel inhibitors. Using AutoDock Vina, we recently screened the NCI library of about 320,000 compounds against the crystal structure of PfUCHL3. The top hits were subsequently screened against its human ortholog UCHL3 as to identify compounds that could specifically target the PfUCHL3 over its human counterpart. This method was used to identify small molecule inhibitors that can preferentially inhibit the parasitic enzyme. Several compounds were identified that demonstrated significant binding affinity preference for the malaria target over the human enzyme. Two of these compounds demonstrated ng/mL activity. | A structural comparative approach to identifying novel antimalarial inhibitors |
S1476927113000376 | Hodgkin lymphoma (HL) is a special type of B cell lymphoma, arising from germinal center B-cells. Morphological and immunohistochemical features of HL as well as the spatial distribution of malignant cells differ from other lymphoma and cancer types. Sophisticated protocols for immunostaining and the acquisition of high-resolution images become routine in pathological labs. Large and daily growing databases of high-resolution digital images are currently emerging. A systematic tissue image analysis and computer-aided exploration may provide new insights into HL pathology. The automated analysis of high resolution images, however, is a hard task in terms of required computing time and memory. Special concepts and pipelines for analyzing high-resolution images can boost the exploration of image databases. In this paper, we report an analysis of digital color images recorded in high-resolution of HL tissue slides. Applying a protocol of CD30 immunostaining to identify malignant cells, we implement a pipeline to handle and explore image data of stained HL tissue images. To the best of our knowledge, this is the first systematic application of image analysis to HL tissue slides. To illustrate the concept and methods we analyze images of two different HL types, nodular sclerosis and mixed cellularity as the most common forms and reactive lymphoid tissue for comparison. We implemented a pipeline which is adapted to the special requirements of whole slide images of HL tissue and identifies relevant regions that contain malignant cells. Using a preprocessing approach, we separate the relevant tissue region from the background. We assign pixels in the images to one of the six predefined classes: Hematoxylin +, CD30+, Nonspecific red, Unstained, Background, and Low intensity, applying a supervised recognition method. Local areas with pixels assigned to the class CD30+ identify regions of interest. As expected, an increased amount of CD30+ pixels is a characteristic feature of nodular sclerosis, and the non-lymphoma cases show a characteristically low amount of CD30+ stain. Images of mixed cellularity samples include cases of high CD30+ coloring as well as cases of low CD30+ coloring. | Image database analysis of Hodgkin lymphoma |
S1476927113000388 | The quality of protein structures obtained by different experimental and ab-initio calculation methods varies considerably. The methods have been evolving over time by improving both experimental designs and computational techniques, and since the primary aim of these developments is the procurement of reliable and high-quality data, better techniques resulted on average in an evolution toward higher quality structures in the Protein Data Bank (PDB). Each method leaves a specific quantitative and qualitative “trace” in the PDB entry. Certain information relevant to one method (e.g. dynamics for NMR) may be lacking for another method. Furthermore, some standard measures of quality for one method cannot be calculated for other experimental methods, e.g. crystal resolution or NMR bundle RMSD. Consequently, structures are classified in the PDB by the method used. Here we introduce a method to estimate a measure of equivalent X-ray resolution (e-resolution), expressed in units of Å, to assess the quality of any type of monomeric, single-chain protein structure, irrespective of the experimental structure determination method. We showed and compared the trends in the quality of structures in the Protein Data Bank over the last two decades for five different experimental techniques, excluding theoretical structure predictions. We observed that as new methods are introduced, they undergo a rapid method development evolution: within several years the e-resolution score becomes similar for structures obtained from the five methods and they improve from initially poor performance to acceptable quality, comparable with previously established methods, the performance of which is essentially stable. | Estimating structure quality trends in the Protein Data Bank by equivalent resolution |
S147692711300039X | The modulation of the properties and function of cell membranes by small volatile substances is important for many biomedical applications. Despite available experimental results, molecular mechanisms of action of inhalants and organic solvents, such as acetone, on lipid membranes remain not well understood. To gain a better understanding of how acetone interacts with membranes, we have performed a series of molecular dynamics (MD) simulations of a POPC bilayer in aqueous solution in the presence of acetone, whose concentration was varied from 2.8 to 11.2mol%. The MD simulations of passive distribution of acetone between a bulk water phase and a lipid bilayer show that acetone favors partitioning into the water-free region of the bilayer, located near the carbonyl groups of the phospholipids and at the beginning of the hydrocarbon core of the lipid membrane. Using MD umbrella sampling, we found that the permeability barrier of ∼0.5kcal/mol exists for acetone partitioning into the membrane. In addition, a Gibbs free energy profile of the acetone penetration across a bilayer demonstrates a favorable potential energy well of −3.6kcal/mol, located at 15–16Å from the bilayer center. The analysis of the structural and dynamics properties of the model membrane revealed that the POPC bilayer can tolerate the presence of acetone in the concentration range of 2.8–5.6mol%. The accumulation of the higher acetone concentration of 11.2mol% results, however, in drastic disordering of phospholipid packing and the increase in the membrane fluidity. The acetone molecules push the lipid heads apart and, hence, act as spacers in the headgroup region. This effect leads to the increase in the average headgroup area per molecule. In addition, the acyl tail region of the membrane also becomes less dense. We suggest, therefore, that the molecular mechanism of acetone action on the phospholipid bilayer has many common features with the effects of short chain alcohols, DMSO, and chloroform. | Effect of acetone accumulation on structure and dynamics of lipid membranes studied by molecular dynamics simulations |
S1476927113000406 | Proline is an important osmotic adjusting material greatly accumulated under drought stress and can help plant to adapt to osmotic stress. MicroRNAs (miRNAs) are small, endogenous RNAs that play important regulatory roles in plant development and stress response by negatively affecting gene expression at post-transcriptional level. Three genes of pyrroline-5-carboxylate synthetase (P5CS), pyrroline-5-carboxylate reductase (P5CR) and proline dehydrogenase (ProDH) are regulating proline metabolism. Until now, little is known about miRNAs regulating proline accumulation. In this work, in order to understand whether miRNAs related to mRNAs of enzymes to regulate proline enrichment under drought stress, we used mRNAs of related enzymes as the targets of miRNAs to search miRBase using BLAST and find many query miRNA sequences. After a range of filtering criteria, 11 known miRNAs classified into 6 miRNA families were predicted. The result from qRT-PCR assay showed that 10 out of 11 predicted miRNAs were successfully detected including 9 down-regulated miRNAs and one up-regulated miRNA. Based on expression and functional analysis, we identified miR172, miR396a, miR396c and miR4233 may regulate P5CS gene, and miR2673 and miR6461 may regulate P5CR and ProDH gene, respectively. The findings can help us make a good understanding of the roles of miRNAs in regulation of proline accumulation and provide molecular evidence for involvement process of drought tolerance in potato. | Prediction and verification of microRNAs related to proline accumulation under drought stress in potato |
S1476927113000418 | Understanding of proteins adaptive to hypersaline environment and identifying them is a challenging task and would help to design stable proteins. Here, we have systematically analyzed the normalized amino acid compositions of 2121 halophilic and 2400 non-halophilic proteins. The results showed that halophilic protein contained more Asp at the expense of Lys, Ile, Cys and Met, fewer small and hydrophobic residues, and showed a large excess of acidic over basic amino acids. Then, we introduce a support vector machine method to discriminate the halophilic and non-halophilic proteins, by using a novel Pearson VII universal function based kernel. In the three validation check methods, it achieved an overall accuracy of 97.7%, 91.7% and 86.9% and outperformed other machine learning algorithms. We also address the influence of protein size on prediction accuracy and found the worse performance for small size proteins might be some significant residues (Cys and Lys) were missing in the proteins. | Support vector machine with a Pearson VII function kernel for discriminating halophilic and non-halophilic proteins |
S147692711300042X | An attempt was made to develop a computational model based on artificial neural network and ant colony optimization to estimate the composition of medium components for maximizing the productivity of Penicillin G Acylase (PGA) enzyme from Escherichia coli DH5α strain harboring the plasmid pPROPAC. As a first step, an artificial neural network (ANN) model was developed to predict the PGA activity by considering the concentrations of seven important components of the medium. Design of experiments employing central composite design technique was used to obtain the training samples. In the second step, ant colony optimization technique for continuous domain was employed to maximize the PGA activity by finding the optimal inputs for the developed ANN model. Further, the effect of a combination of ant colony optimization for continuous domain with a preferential local search strategy was studied to analyze the performance. For a comparative study, the training samples were fed into the response surface methodology optimization software to maximize the PGA production. The obtained PGA activity (56.94U/mL) by the proposed approach was found to be higher than that of the obtained value (45.60U/mL) with the response surface methodology. The optimum solution obtained computationally was experimentally verified. The observed PGA activity (55.60U/mL) exhibited a close agreement with the model predictions. | A computational model for enhancing recombinant Penicillin G Acylase production from Escherichia coli DH5α |
S1476927113000546 | In this manuscript, we present a computational model to approximate the solutions of a partial differential equation which describes the growth dynamics of microbial films. The numerical technique reported in this work is an explicit, nonlinear finite-difference methodology which is computationally implemented using Newton's method. Our scheme is compared numerically against an implicit, linear finite-difference discretization of the same partial differential equation, whose computer coding requires an implementation of the stabilized bi-conjugate gradient method. Our numerical results evince that the nonlinear approach results in a more efficient approximation to the solutions of the biofilm model considered, and demands less computer memory. Moreover, the positivity of initial profiles is preserved in the practice by the nonlinear scheme proposed. | An efficient nonlinear finite-difference approach in the computational modeling of the dynamics of a nonlinear diffusion-reaction equation in microbial ecology |
S1476927113000558 | In this study we propose a protocol to evaluate membrane-bound cytochrome c oxidase–cytochrome c552 docking candidates. An initial rigid docking algorithm generates docking poses of the cytochrome c oxidase–cytochrome c552, candidates are then aggregated into a 512-DPPC membrane model and solvated in explicit solvent. Molecular dynamic simulations are performed to induce conformational changes to membrane-bound protein complexes. Lastly each protein–protein complex is optimized in terms of its hydrogen bond network, evaluated energetically and ranked. The protocol is directly applicable to other membrane-protein complexes, such as protein–ligand systems. | H-bond refinement for electron transfer membrane-bound protein–protein complexes: Cytochrome c oxidase and cytochrome c552 |
S147692711300056X | Reptin functions in a wide range of biological processes including chromatin remodelling, nucleolar organization and transcriptional regulation of WNT signalling. As β-catenin dependent transcriptional repression and activation events involve binding of Reptin and histone deacetylase 1 to APPL endocytic proteins, this complex has become an important target to identify molecules governing endocytic processes and WNT signalling. Here, we describe the structural basis of APPL binding to Reptin to explore their mode of binding in context with APPL1/APPL2 dimerization. There is an evidence that both PH and BAR domains of APPL proteins exhibit alternately conserved regions involved in hetero-dimerization process and our in-silico data also corroborate this fact. Moreover, APPL2PH domain binds to the BAR domain region encompassing a nuclear localization signal. We conclude that APPLPH binding to BAR domain and Reptin is mutually exclusive which regulates the nucleocytoplasmic shuttling of Reptin. Furthermore, Reptin is unable to bind with membrane-associated APPL proteins. These observations were further expanded by experimental approaches where we identified a novel point mutation D316N lying in the APPL1PH domain which resulted in a significantly reduced binding with Reptin. By luciferase assays, we observed that overexpression of APPL1D316N and APPL1WT stimulated β-catenin/TCF dependent transcriptional activity in a similar manner which suggested that binding of Reptin to APPL1 is not necessary for β-catenin dependent target gene expression. Overall, our data attempt to highlight a comparative role of APPL proteins in controlling β-catenin dependent transcription mechanism which may improve our understanding of gene regulation. | Mutually exclusive binding of APPLPH to BAR domain and Reptin regulates β-catenin dependent transcriptional events |
S1476927113000571 | Lipopolysaccharide (LPS) is an essential structural component found in Gram-negative bacteria. The molecule is comprised of a highly conserved lipid A and a variable outer core consisting of various sugars. LPS plays important roles in membrane stability in the bacterial cell and is also a potent activator of the human immune system. Despite its obvious importance, little is understood regarding the regulation of the individual enzymes involved or the pathway as a whole. LpxA and LpxC catalyze the first two steps in the LPS pathway. The reaction catalyzed by LpxA possesses a highly unfavourable equilibrium constant with no evidence of coupling to an energetically favourable reaction. In our model the presence of the second enzyme LpxC was sufficient to abate this unfavourable reaction and confirming previous studies suggesting that this reaction is the first committed step in LPS synthesis. It is believed that the protease FtsH regulates LpxC activity via cleavage. It is also suspected that the activity of FtsH is regulated by a metabolite produced by the LPS pathway; however, it is not known which one. In order to investigate these mechanisms, we obtained kinetic parameters from literature and developed estimates for other simulation parameters. Our simulations suggest that under modest increases in LpxC activity, FtsH is able to regulate the rate of product formation. However, under extreme increases in LpxC activities such as over-expression or asymmetrical cell division then FtsH activation may not be sufficient to regulate this first stage of synthesis. | A model for the proteolytic regulation of LpxC in the lipopolysaccharide pathway of Escherichia coli |
S1476927113000583 | Computational intelligence (CI) is a well-established paradigm with current systems having many of the characteristics of biological computers and capable of performing a variety of tasks that are difficult to do using conventional techniques. It is a methodology involving adaptive mechanisms and/or an ability to learn that facilitate intelligent behavior in complex and changing environments, such that the system is perceived to possess one or more attributes of reason, such as generalization, discovery, association and abstraction. The objective of this article is to present to the CI and bioinformatics research communities some of the state-of-the-art in CI applications to bioinformatics and motivate research in new trend-setting directions. In this article, we present an overview of the CI techniques in bioinformatics. We will show how CI techniques including neural networks, restricted Boltzmann machine, deep belief network, fuzzy logic, rough sets, evolutionary algorithms (EA), genetic algorithms (GA), swarm intelligence, artificial immune systems and support vector machines, could be successfully employed to tackle various problems such as gene expression clustering and classification, protein sequence classification, gene selection, DNA fragment assembly, multiple sequence alignment, and protein function prediction and its structure. We discuss some representative methods to provide inspiring examples to illustrate how CI can be utilized to address these problems and how bioinformatics data can be characterized by CI. Challenges to be addressed and future directions of research are also presented and an extensive bibliography is included. | Computational intelligence techniques in bioinformatics |
S1476927113000595 | Domains are the structural basis of the physiological functions of proteins, and the prediction of which is an advantageous process on the study of protein structure and function. This article proposes a new complete automatic prediction method, PPM-Dom (Domain Position Prediction Method), for predicting the particular positions of domains in a target protein via its atomic coordinate. The presented method integrates complex networks, community division, and fuzzy mean operator (FMO). The whole sequences are divided into potential domain regions by the complex network and community division, and FMO allows the final determination for the domain position. This method will suffice to predict regions that will form a domain structure and those that are unstructured based on completely new atomic coordinate information of the query sequence, and be able to separate different domains in the same query sequence from each other. On evaluating the performance using an independent testing dataset, PPM-Dom reached 91.41% for prediction accuracy, 96.12% for sensitivity and 92.86% for specificity. The tool bag of PPM-Dom is freely available at http://cic.scu.edu.cn/bioinformatics/PPMDom.zip. | PPM-Dom: A novel method for domain position prediction |
S1476927113000601 | The ability to analyze and compare protein–nucleic acid and protein–protein interaction interface has critical importance in understanding the biological function and essential processes occurring in the cells. Since high-resolution three-dimensional (3D) structures of biomacromolecule complexes are available, computational characterizing of the interface geometry become an important research topic in the field of molecular biology. In this study, the interfaces of a set of 180 protein–nucleic acid and protein–protein complexes are computed to understand the principles of their interactions. The weighted Voronoi diagram of the atoms and the Alpha complex has provided an accurate description of the interface atoms. Our method is implemented in the presence and absence of water molecules. A comparison among the three types of interaction interfaces show that RNA–protein complexes have the largest size of an interface. The results show a high correlation coefficient between our method and the PISA server in the presence and absence of water molecules in the Voronoi model and the traditional model based on solvent accessibility and the high validation parameters in comparison to the classical model. | Computational structure analysis of biomacromolecule complexes by interface geometry |
S1476927113000613 | The structural dynamics of the cAMP-dependent protein kinase catalytic subunit were modeled using molecular dynamics computational methods. It was shown that the structure of this protein as well as its complexes with ATP and peptide ligand PKI(5-24) consisted of a large number of rapidly inter-converting conformations which could be grouped into subsets proceeding from their similarity. This cluster analysis revealed that conformations which correspond to the “opened” and “closed” structures of the protein were already present in the free enzyme, and most surprisingly co-existed in enzyme–ATP and enzyme–PKI(5-24) complexes as well as in the ternary complex, which included both of these ligands. The results also demonstrated that the most mobile structure segments of the protein were located in the regions of substrate binding sites and that their dynamics were most significantly affected by the binding of the ATP and peptide ligand. | Computer modeling of the dynamic properties of the cAMP-dependent protein kinase catalytic subunit |
S1476927113000625 | The Lol system in Escherichia coli is involved in localization of lipoproteins and hence is essential for growth of the organism. LolA is a periplasmic chaperone that binds to outer-membrane specific lipoproteins and transports them from inner membrane to outer membrane through LolB. The hydrophobic lipid-binding cavity of LolA consists of α-helices which act as a lid in regulating the transfer of lipoproteins from LolA to LolB. The current study aims to investigate the structural changes observed in LolA during the transition from open to closed conformation in the absence of lipoprotein. Molecular dynamics (MD) simulations were carried out for two LolA crystal structures; LolA(R43L), and in silico mutated MsL43R for a simulation time of 50ns in water environment. We have performed an in silico point mutation of leucine to arginine in MsL43R to evaluate the importance of arginine to induce structural changes and impact the stability of protein structure. A complete dynamic analysis of open to closed conformation reveals the existence of two distinct levels; closing of lid and closing of entrance of hydrophobic cavity. Our analysis reveals that the structural flexibility of LolA is an important factor for its role as a periplasmic chaperone. | Understanding the lid movements of LolA in Escherichia coli using molecular dynamics simulation and in silico point mutation |
S1476927113000637 | Non-specific lipid transfer proteins (ns-LTPs), ubiquitously found in various types of plants, have been well-known to transfer amphiphilic lipids and promote the lipid exchange between mitochondria and microbody. In this study, an in silico analysis was proposed to study ns-LTP in Peganum harmala L., which may belong to ns-LTP1 family, aiming at constructing its three-dimensional structure. Moreover, we adopted MEGA to analyze ns-LTPs and other species phylogenetically, which brought out an initial sequence alignment of ns-LTPs. In addition, we used molecular docking and molecular dynamics simulations to further investigate the affinities and stabilities of ns-LTP with several ligands complexes. Taken together, our results about ns-LTPs and their ligand-binding activities can provide a better understanding of the lipid–protein interactions, indicating some future applications of ns-LTP-mediated transport. | Modeling, docking and dynamics simulations of a non-specific lipid transfer protein from Peganum harmala L. |
S1476927113000649 | The ability of Mycobacterium tuberculosis (Mtb) to survive in low oxygen environments enables the bacterium to persist in a latent state within host tissues. In vitro studies of Mtb growth have identified changes in isocitrate lyase (ICL) and malate synthase (MS) that enable bacterial persistence under low oxygen and other environmentally limiting conditions. Systems chemical biology (SCB) enables us to evaluate the effects of small molecule inhibitors not only on the reaction catalyzed by malate synthase and isocitrate lyase, but the effect on the complete tricarboxylic acid cycle (TCA) by taking into account complex network relationships within that system. To study the kinetic consequences of inhibition on persistent bacilli, we implement a systems-chemical biology (SCB) platform and perform a chemistry-centric analysis of key metabolic pathways believed to impact Mtb latency. We explore consequences of disrupting the function of malate synthase (MS) and isocitrate lyase (ICL) during aerobic and hypoxic non-replicating persistence (NRP) growth by using the SCB method to identify small molecules that inhibit the function of MS and ICL, and simulating the metabolic consequence of the disruption. Results indicate variations in target and non-target reaction steps, clear differences in the normal and low oxygen models, as well as dosage dependent response. Simulation results from singular and combined enzyme inhibition strategies suggest ICL may be the more effective target for chemotherapeutic treatment against Mtb growing in a microenvironment where oxygen is slowly depleted, which may favor persistence. | A systems chemical biology study of malate synthase and isocitrate lyase inhibition in Mycobacterium tuberculosis during active and NRP growth |
S1476927113000741 | NF-Y transcription factors encoded by HAP gene family, composed of three subunits (HAP2/NF-YA, HAP3/NF-YB and HAP5/NF-YC), are capable of transcriptional regulation of target genes with high specificity by binding to the CCAAT-containing promoter sequences. Here, we have characterized duplicated HAP genes in Selaginella moellendorffii and explored some features that might be involved in the regulation of gene expression and their function. Subsequently, the evolutionary relationships of LEC1-type of HAP3 genes have been studied starting from lycophytes to angiosperm to reveal the details of conservation and diversification of these genes during plant evolution. Computational analyses demonstrated the variation in length of cis-regulatory region of HAP3 duplicates in S. moellendorffii containing three thermodynamically stable and evolutionarily conserved RNA secondary structures. The homology modeling of NF-Y proteins, secondary structural details, DNA binding large positive patches, binding affinity of H2A–H2B interactive residues of NF-YC subunits on the duplicated NF-YB subunits, conserved domain analyses and protein structural alignments indicated that gene duplication process of HAP genes in S. moellendorffii, followed by structural diversification, provide specific hints about their functional specificity under various circumstances for the survival of this lycophytic plant. We have identified several conserved motifs in LEC1 proteins among all plant lineages during evolution. | In silico characterization and evolutionary analyses of CCAAT binding proteins in the lycophyte plant Selaginella moellendorffii genome: A growing comparative genomics resource |
S1476927113000753 | Halomonas boliviensis LC1T =DSM 15516T is a halophilic bacterium that copiously produces osmolytes and polyesters. The growth of H. boliviensis is restricted when glutamate or glutamine is not included in its culture medium. The concentration of glutamate in the medium can regulate the production of either osmolytes or polyesters. However, genomic studies on the nitrogen assimilation have not been performed on H. boliviensis and other members of the family Halomonadaceae. Glutamate metabolism in H. boliviensis was discerned based on genome sequence analysis. The genome sequences of other Halomonadaceae members revealed similar enzymes to those found in H. boliviensis. H. boliviensis and H. elongata DSM 2581T acquired distinct glutamate dehydrogenase genes through horizontal gene transfer from a different bacterium. Two alleles of glutamine synthetase could be found in H. boliviensis, one of which was obtained from a thermophilic archaeon via horizontal gene transfer. Two subunits of glutamate synthase were also present in H. boliviensis. The small β-subunit had a molecular weight of 52kDa and was phylogenetically closely affiliated to proteins of other halomonads and Gammaproteobacteria. The large (161kDa) α-subunit of the halomonads gathered in a separate phylogenetic group, hence glutamate synthase α-subunits of halomonads may be included a novel group of enzymes. Furthermore, putative enzymes obtained from the genome of H. boliviensis should permit complete glutamate metabolism. A similar metabolism should be followed by other halomonads. However, some phenotypic differences between halomonads, such as the ability to assimilate ammonia, resulted as a consequence of horizontal gene transfer. Each enzyme that forms part of the glutamate metabolism in prokaryotes evolved following a different pattern. Yet, most enzymes of halomonads diverged in phylogenetic clusters composed of Proteobacteria, as might be expected. | Genomic studies on nitrogen metabolism in Halomonas boliviensis: Metabolic pathway, biochemistry and evolution |
S1476927113000765 | Background Allergy has become a key cause of morbidity worldwide. Although many legumes (plants in the Fabaceae family) are healthy foods, they may have a number of allergenic proteins. A number of allergens have been identified and characterized in Fabaceae family, such as soybean and peanut, on the basis of biochemical and molecular biological approaches. However, our understanding of the allergens from chickpea (Cicer arietinum L.), belonging to this family, is very limited. Objective In this study, we aimed to identify putative and cross-reactive allergens from Chickpea (C. arietinum) by means of in silico analysis of the chickpea protein sequences and allergens sequences from Fabaceae family. Methods We retrieved known allergen sequences in Fabaceae family from the IUIS Allergen Nomenclature Database. We performed a protein BLAST (BLASTp) on these sequences to retrieve the similar sequences from chickpea. We further analyzed the retrieved chickpea sequences using a combination of in silico tools, to assess them for their allergenicity potential. Following this, we built structure models using FUGUE: Sequence-structure homology; these models generated by the recognition tool were viewed in Swiss-PDB viewer. Results Through this in silico approach, we identified seven novel putative allergens from chickpea proteome sequences on the basis of similarity of sequence, structure and physicochemical properties with the known reported legume allergens. Four out of seven putative allergens may also show cross reactivity with reported allergens since potential allergens had common sequence and structural features with the reported allergens. Conclusion The in silico proteomic identification of the allergen proteins in chickpea provides a basis for future research on developing hypoallergenic foods containing chickpea. Such bioinformatics approaches, combined with experimental methodology, will help delineate an efficient and comprehensive approach to assess allergenicity and pave the way for a better understanding of the biological and medical basis of the same. | Identification of putative and potential cross-reactive chickpea (Cicer arietinum) allergens through an in silico approach |
S1476927113000777 | We propose to use change points of atomic positions in the molecular dynamics trajectory as indicators of the propagating signals in protein. We designate these changes as signals because they can propagate within the molecule in the form of “perturbation wave”, transmit energy or information between different parts of protein, and serve as allosteric signals. We found that change points can distinguish between thermal fluctuations of atoms (noise) and signals in a protein despite the differences in the motility of amino acid residues. Clustering of the spatially close residues that were experiencing change points close in time, allowed us to map pathways of signal propagation in a protein at the atomic level of resolution. We propose a potential mechanism for the origin of the signal and its propagation that relies on the autonomic coherence resonance in atomic fluctuations. According to this mechanism, random synchronization of fluctuations of neighboring atoms results in a resonance, which increases amplitude of vibration of these atoms. This increase can be transmitted to the atoms colliding with the resonant atoms, leading to the propagating signal. The wavelet-based coherence analysis of the inter-atomic distances between carbon-alpha atoms and surrounding atoms for the residue pairs that belong to the same communication pathway allowed us to find time periods with temporarily locked phases, confirming the occurrence of conditions for resonance. Analysis of the mapped pathways demonstrated that they form a network that connects different regions of the protein. | Mapping the intramolecular signal propagation pathways in protein using Bayesian change point analysis of atomic motions |
S1476927113000789 | In this work, we have analyzed the influence of cation–π interactions to the stability of 59 high resolution protein–RNA complex crystal structures. The total number of Lys and Arg are similar in the dataset as well as the number of their interactions. On the other hand, the aromatic chains of purines are exhibiting more cation–π interactions than pyrimidines. 35% of the total interactions in the dataset are involved in the formation of multiple cation–π interactions. The multiple cation–π interactions have been conserved more than the single interactions. The analysis of the geometry of the cation–π interactions has revealed that the average distance (d) value falls into distinct ranges corresponding to the multiple (4.28Å) and single (5.50Å) cation–π interactions. The G–Arg pair has the strongest interaction energy of −3.68kcalmol−1 among all the possible pairs of amino acids and bases. Further, we found that the cation–π interactions due to five-membered rings of A and G are stronger than that with the atoms in six-membered rings. 8.7% stabilizing residues are involved in building cation–π interactions with the nucleic bases. There are three types of structural motifs significantly over-represented in protein–RNA interfaces: beta-turn-ir, niche-4r and st-staple. Tetraloops and kink-turns are the most abundant RNA motifs in protein–RNA interfaces. Amino acids deployed in the protein–RNA interfaces are deposited in helices, sheets and coils. Arg and Lys, involved in cation–π interactions, prefer to be in the solvent exposed surface. The results from this study might be used for structure–based prediction and as scaffolds for future protein–RNA complex design. | Cation−π interactions in high resolution protein−RNA complex crystal structures |
S1476927113000790 | Tuberculosis continues to plague the world with the World Health Organization estimating that about one third of the world's population is infected. Due to the emergence of MDR and XDR strains of TB, the need for novel therapeutics has become increasing urgent. Herein we report the results of a virtual screen of 4.1 million compounds against a promising drug target, DrpE1. The virtual compounds were obtained from the Zinc docking site and screened using the molecular docking program, AutoDock Vina. The computational hits have led to the identification of several promising lead compounds. | A large scale virtual screen of DprE1 |
S1476927113000807 | Monoamine oxidase (MAO) enzymes regulate the level of neurotransmitters by catalyzing the oxidation of various amine neurotransmitters, such as serotonin, dopamine and norepinephrine. Therefore, they are the important targets for drugs used in the treatment of depression, Parkinson, Alzeimer and other neurodegenerative disorders. Elucidation of MAO-catalyzed amine oxidation will provide new insights into the design of more effective drugs. Various amine oxidation mechanisms have been proposed for MAO so far, such as single electron transfer mechanism, polar nucleophilic mechanism and hydride mechanism. Since amine oxidation reaction of MAO takes place between cofactor flavin and the amine substrate, we focus on the small model structures mimicking flavin and amine substrates so that three model structures were employed. Reactants, transition states and products of the polar nucleophilic (proton transfer), the water-assisted proton transfer and the hydride transfer mechanisms were fully optimized employing various semi-empirical, ab initio and new generation density functional theory (DFT) methods. Activation energy barriers related to these mechanisms revealed that hydride transfer mechanism is more feasible. | A comparative computational investigation on the proton and hydride transfer mechanisms of monoamine oxidase using model molecules |
S1476927113000819 | Technological advances in cytotoxicity analysis have now made it possible to obtain real time data on changes in cell growth, morphology and cell death. This type of testing has a great potential for reducing and refining traditional in vivo toxicology tests. By monitoring the dynamic response profile of living cells via the xCELLigence real-time cell analyzer for high-throughput (RTCA HT) system, cellular changes including cell number (cell index, CI) are recorded and analyzed. A special scaled index defined as normalized cell index (NCI) is used in the analysis which reduces the influence of inter-experimental variations. To assess the extent of exposure of the tested chemicals, a two-exponent model is presented to describe rate of cell growth and death. This model is embodied in the time and concentration-dependent cellular response curves, and the parameters k 1 and k 2 in this model are used to describe the rate of cell growth and death. Based on calculated k 2 values and the corresponding concentrations, a concentration–response curve is fitted. As a result, a cytotoxicity assessment named KC 50 is calculated. The validation of the proposed method is demonstrated by exposing six cell lines to 14 chemical compounds. Our findings suggest that the proposed KC 50-based toxicity assay can be an alternative to the traditional single time-point assay such as LC 50 (the concentration at which 50% of the cells are killed). The proposed index has a potential for routine evaluation of cytotoxicities. Another advantage of the proposed index is that it extracts cytotoxicity information when CI fails to detect the low toxicity. | In vitro cytotoxicity assessment based on KC 50 with real-time cell analyzer (RTCA) assay |
S1476927113000820 | The importance of protein–protein interactions (PPIs) is becoming increasingly appreciated, as these interactions lie at the core of virtually every biological process. Small molecule modulators that target PPIs are under exploration as new therapies. One of the greatest obstacles faced in crystallographically determining the 3D structures of proteins is coaxing the proteins to form “artificial” PPIs that lead to uniform crystals suitable for X-ray diffraction. This work compares interactions formed naturally, i.e., “biological”, with those artificially formed under crystallization conditions or “non-biological”. In particular, a detailed analysis of water molecules at the interfaces of high-resolution (≤2.30Å) X-ray crystal structures of protein–protein complexes, where 140 are biological protein–protein complex structures and 112 include non-biological protein–protein interfaces, was carried out using modeling tools based on the HINT forcefield. Surprisingly few and relatively subtle differences were observed between the two types of interfaces: (i) non-biological interfaces are more polar than biological interfaces, yet there is better organized hydrogen bonding at the latter; (ii) biological associations rely more on water-mediated interactions with backbone atoms compared to non-biological associations; (iii) aromatic/planar residues play a larger role in biological associations with respect to water, and (iv) Lys has a particularly large role at non-biological interfaces. A support vector machines (SVMs) classifier using descriptors from this study was devised that was able to correctly classify 84% of the two interface types. | Unintended consequences? Water molecules at biological and crystallographic protein–protein interfaces |
S1476927113000832 | To explore the molecular mechanisms of cholangiocarcinoma (CC), microarray technology was used to find biomarkers for early detection and diagnosis. The gene expression profiles from 6 patients with CC and 5 normal controls were downloaded from Gene Expression Omnibus and compared. As a result, 204 differentially co-expressed genes (DCGs) in CC patients compared to normal controls were identified using a computational bioinformatics analysis. These genes were mainly involved in coenzyme metabolic process, peptidase activity and oxidation reduction. A regulatory network was constructed by mapping the DCGs to known regulation data. Four transcription factors, FOXC1, ZIC2, NKX2-2 and GCGR, were hub nodes in the network. In conclusion, this study provides a set of targets useful for future investigations into molecular biomarker studies. | Gene expression patterns combined with bioinformatics analysis identify genes associated with cholangiocarcinoma |
S1476927113000844 | The protein structure prediction problem is a classical NP hard problem in bioinformatics. The lack of an effective global optimization method is the key obstacle in solving this problem. As one of the global optimization algorithms, tabu search (TS) algorithm has been successfully applied in many optimization problems. We define the new neighborhood conformation, tabu object and acceptance criteria of current conformation based on the original TS algorithm and put forward an improved TS algorithm. By integrating the heuristic initialization mechanism, the heuristic conformation updating mechanism, and the gradient method into the improved TS algorithm, a heuristic-based tabu search (HTS) algorithm is presented for predicting the two-dimensional (2D) protein folding structure in AB off-lattice model which consists of hydrophobic (A) and hydrophilic (B) monomers. The tabu search minimization leads to the basins of local minima, near which a local search mechanism is then proposed to further search for lower-energy conformations. To test the performance of the proposed algorithm, experiments are performed on four Fibonacci sequences and two real protein sequences. The experimental results show that the proposed algorithm has found the lowest-energy conformations so far for three shorter Fibonacci sequences and renewed the results for the longest one, as well as two real protein sequences, demonstrating that the HTS algorithm is quite promising in finding the ground states for AB off-lattice model proteins. | Heuristic-based tabu search algorithm for folding two-dimensional AB off-lattice model proteins |
S1476927113000856 | Internal repeats in protein sequences play a significant role in the evolution of protein structure and function. Applications of different bioinformatics tools help in the identification and characterization of these repeats. In the present study, we analyzed sequence repeats in a non-redundant set of proteins available in the Protein Data Bank (PDB). We used RADAR for detecting internal repeats in a protein, PDBeFOLD for assessing structural similarity, PDBsum for finding functional involvement and Pfam for domain assignment of the repeats in a protein. Through the analysis of sequence repeats, we found that identity of the sequence repeats falls in the range of 20–40% and, the superimposed structures of the most of the sequence repeats maintain similar overall folding. Analysis sequence repeats at the functional level reveals that most of the sequence repeats are involved in the function of the protein through functionally involved residues in the repeat regions. We also found that sequence repeats in single and two domain proteins often contained conserved sequence motifs for the function of the domain. | Analysis of sequence repeats of proteins in the PDB |
S1476927113000868 | Gamma-carboxylation, one type of post-translational modifications, is involved in many human disease. However, very few computational methods for gamma-carboxylation site prediction are available. In this paper, we develop a novel method CarboxySVM which is based on support vector machine with radial basis function kernel to identify the gamma-carboxylation sites. In this method, we combine position specific scoring matrices (PSSM)-based evolutionary conservation scores and other sequences-derived descriptors. As a result, an accuracy of 91.2% is achieved on training dataset with fivefold cross validation, and 91.8% on the independent test dataset. It is demonstrated by empirical evaluation on benchmark datasets that our method outperforms several other modern predictors. Our model reveals that evolutionary conservation is higher in carboxylation sites, compared to non-carboxylation sites. The composition of arginine in carboxylation sites is higher than that of non-carboxylation sites. CarboxySVM can be downloaded from http://code.google.com/p/gamma-carboxylation/source/browse/trunk. | Prediction of protein modification sites of gamma-carboxylation using position specific scoring matrices based evolutionary information |
S147692711300087X | In this paper, the physical and chemical characteristics, biological structure and function of a non-specific nuclease from Yersinia enterocolitica subsp. palearctica (Y. NSN) found in our group were studied using multiple bioinformatics approaches. The results showed that Y. NSN had 283 amino acids, a weight of 30,692.5ku and a certain hydrophilic property. Y. NSN had a signal peptide, no transmembrane domains and disulphide bonds. Cleavage site in Y. NSN was between pos. 23 and 24. The prediction result of the secondary structure showed Y. NSN was a coil structure-based protein. The ratio of α-helix, β-folded and random coil were 18.73%, 16.96% and 64.31%, respectively. Active sites were pos. 124, 125, 127, 157, 165 and 169. Mg2+ binding site was pos. 157. Substrate binding sites were pos. 124, 125 and 169. The analysis of multisequencing alignment and phylogenetic tree indicated that Y. NSN shared high similarity with the nuclease from Y. enterocolitica subsp. enterocolitica 8081. The enzyme activity results showed that Y. NSN was a nuclease with good thermostability. | Bioinformatics analysis of a non-specific nuclease from Yersinia enterocolitica subsp. palearctica |
S1476927113000881 | Gene regulatory networks inference is currently a topic under heavy research in the systems biology field. In this paper, gene regulatory networks are inferred via evolutionary model based on time-series microarray data. A non-linear differential equation model is adopted. Gene expression programming (GEP) is applied to identify the structure of the model and least mean square (LMS) is used to optimize the parameters in ordinary differential equations (ODEs). The proposed work has been first verified by synthetic data with noise-free and noisy time-series data, respectively, and then its effectiveness is confirmed by three real time-series expression datasets. Finally, a gene regulatory network was constructed with 12 Yeast genes. Experimental results demonstrate that our model can improve the prediction accuracy of microarray time-series data effectively. | Using gene expression programming to infer gene regulatory networks from time-series data |
S1476927113000893 | The over-expression of plant specific SnRK2 gene family members by hyperosmotic stress and some by abscisic acid is well established. In this report, we have analyzed the evolution of SnRK2 gene family in different plant lineages including green algae, moss, lycophyte, dicot and monocot. Our results provide some evidences to indicate that the natural selection pressure had considerable influence on cis-regulatory promoter region and coding region of SnRK2 members in Arabidopsis and Oryza independently through time. Observed degree of sequence/motif conservation amongst SnRK2 homolog in all the analyzed plant lineages strongly supported their inclusion as members of this family. The chromosomal distributions of duplicated SnRK2 members have also been analyzed in Arabidopsis and Oryza. Massively Parallel Signature Sequencing (MPSS) database derived expression data and the presence of abiotic stress related promoter elements within the 1kb upstream promoter region of these SnRK2 family members further strengthen the observations of previous workers. Additionally, the phylogenetic relationships of SnRK2 have been studied in all plant lineages along with their respective exon–intron structural patterns. Our results indicate that the ancestral SnRK2 gene of land plants gradually evolved by duplication and diversification and modified itself through exon–intron loss events to survive under environmental stress conditions. | Genome-wide analysis and evolutionary study of sucrose non-fermenting 1-related protein kinase 2 (SnRK2) gene family members in Arabidopsis and Oryza |
S147692711300090X | To analyze the evolutionary dynamics of a mutant population in an evolutionary experiment, it is necessary to sequence a vast number of mutants by high-throughput (next-generation) sequencing technologies, which enable rapid and parallel analysis of multikilobase sequences. However, the observed sequences include many errors of base call. Therefore, if next-generation sequencing is applied to analysis of a heterogeneous population of various mutant sequences, it is necessary to discriminate between true bases as point mutations and errors of base call in the observed sequences, and to subject the sequences to error-correction processes. To address this issue, we have developed a novel method of error correction based on the Potts model and a maximum a posteriori probability (MAP) estimate of its parameters corresponding to the “true sequences”. Our method of error correction utilizes (1) the “quality scores” which are assigned to individual bases in the observed sequences and (2) the neighborhood relationship among the observed sequences mapped in sequence space. The computer experiments of error correction of artificially generated sequences supported the effectiveness of our method, showing that 50–90% of errors were removed. Interestingly, this method is analogous to a probabilistic model based method of image restoration developed in the field of information engineering. | Probabilistic model based error correction in a set of various mutant sequences analyzed by next-generation sequencing |
S1476927113000911 | Exposure-response modeling and simulation is especially useful in oncology as it permits to predict and design un-experimented clinical trials as well as dose selection. Dendritic cells (DC) are the most effective immune cells in the regulation of immune system. To activate immune system, DCs may be matured by many factors like bacterial CpG-DNA, Lipopolysaccharaide (LPS) and other microbial products. In this paper, a model based on artificial neural network (ANN) is presented for analyzing the dynamics of antitumor vaccines using empirical data obtained from the experimentations of different groups of mice treated with DCs matured by bacterial CpG-DNA, LPS and whole lysate of a Gram-positive bacteria Listeria monocytogenes. Also, tumor lysate was added to DCs followed by addition of maturation factors. Simulations show that the proposed model can interpret the important features of empirical data. Owing to the nonlinearity properties, the proposed ANN model has been able not only to describe the contradictory empirical results, but also to predict new vaccination patterns for controlling the tumor growth. For example, the proposed model predicts an exponentially increasing pattern of CpG-matured DC to be effective in suppressing the tumor growth. | Modeling of tumor growth in dendritic cell-based immunotherapy using artificial neural networks |
S1476927113000923 | Various subsets of self-avoiding walks naturally appear when investigating existing methods designed to predict the 3D conformation of a protein of interest. Two such subsets, namely the folded and the unfoldable self-avoiding walks, are studied computationally in this article. We show that these two sets are equal and correspond to the whole n-step self-avoiding walks for n ≤14, but that they are different for numerous n ≥108, which are common protein lengths. Concrete counterexamples are provided and the computational methods used to discover them are completely detailed. A tool for studying these subsets of walks related to both pivot moves and protein conformations is finally presented. | Computational investigations of folded self-avoiding walks related to protein folding |
S1476927113000935 | In this work, we have analyzed the influence of halogen bonding to the stability of 44 complexes of proteins and non-natural amino acids. Fluorine- and chlorine-containing non-natural amino acids are more prevalent in the dataset, and an even larger number of contacts made by iodine-containing ligands are found. Only few halogen bonds with the hydroxyl oxygens and carboxylate side chains are found in the dataset. Halogen bonds with the nitrogen-containing side chains have higher occurrence than other acceptors. Backbone carbonyl oxygens and nitrogens are to a substantial extent involved in our dataset. We have observed a small percentage of interactions involving water as hydrogen bond donors. Additionally, most of the interacting residues comprising the interfaces also show a great degree of conservation. There is a clear interaction hot spot at distances of 3.5–3.7Å and Θ 1 angles of 100–120°. There is also a cluster of contacts featuring short distances (2.6–2.9Å) but only nearly optimal Θ 1 angles (140–160°). 51.3% of stabilizing residues are involved in building halogen bonds with the non-natural amino acids. We discovered three types of structural motifs significantly over-represented: beta-turn-ir, beta-turn-il and niche-4r. The halogen-bonding statistics of the dataset do not show any preference for α-helices (36%), β-sheets (36%), or turns/coils (28%) structures. Most of the amino acid residues that were involved in halogen bonds prefer to be in the solvent excluded environment (buried). Furthermore, we have shown that in amino acid–protein complexes halogen atoms can sometimes be involved in hydrogen bonding interactions with hydrogen bonding-donors. The results from this study might be used for the rational design of halogenated ligands as inhibitors and drugs, and in biomolecular engineering. | Halogen bonding in complexes of proteins and non-natural amino acids |
S1476927113000947 | In the field of drug discovery, it is particularly important to discover bioactive compounds through high-throughput virtual screening. The maximum common substructure-based (MCS) algorithm is a promising method for the virtual screening of drug candidates. However, in practical applications, there is always a trade-off between efficiency and accuracy. In this paper, we optimized this method by running time evaluation using essential drugs defined by WHO and FDA-approved small-molecule drugs. The amount of running time allocated to the MCS-based virtual screening was varied, and statistical analysis was conducted to study the impact of computation running time on the screening results. It was determined that the running time efficiency can be improved without compromising accuracy by setting proper running time thresholds. In addition, the similarity of compound structures and its relevance to biological activity are analyzed quantitatively, which highlight the applicability of the MCS-based methods in predicting functions of small molecules. 15–30s was established as a reasonable range for selecting a candidate running time threshold. The effect of CPU speed is considered and the conclusion is generalized. The potential biological activity of small molecules with unknown functions can be predicted by the MCS-based methods. | The optimization of running time for a maximum common substructure-based algorithm and its application in drug design |
S1476927113000959 | RNA tertiary interactions or tertiary motifs are conserved structural patterns formed by pairwise interactions between nucleotides. They include base-pairing, base-stacking, and base-phosphate interactions. A-minor motifs are the most common tertiary interactions in the large ribosomal subunit. The A-minor motif is a nucleotide triple in which minor groove edges of an adenine base are inserted into the minor groove of neighboring helices, leading to interaction with a stabilizing base pair. We propose here novel features for identifying and predicting A-minor motifs in a given three-dimensional RNA molecule. By utilizing the features together with machine learning algorithms including random forests and support vector machines, we show experimentally that our approach is capable of predicting A-minor motifs in the given RNA molecule effectively, demonstrating the usefulness of the proposed approach. The techniques developed from this work will be useful for molecular biologists and biochemists to analyze RNA tertiary motifs, specifically A-minor interactions. | Novel features for identifying A-minors in three-dimensional RNA molecules |
S1476927113000960 | Preclinical data and tumor specimen studies report that AKT kinases are related to many human cancers. Therefore, identification and development of small molecule inhibitors targeting AKT and its signaling pathway can be therapeutic in treatment of cancer. Numerous studies report inhibitors that target the ATP-binding pocket in the kinase domains, but the similarity of this site, within the kinase family makes selectivity a major problem. The sequence identity amongst PH domains is significantly lower than that in kinase domains and developing more selective inhibitors is possible if PH domain is targeted. This in silico screening study is the first time report toward the identification of potential allosteric inhibitors expected to bind the cavity between kinase and PH domains of Akt1. Structural information of Akt1 was used to develop structure-based pharmacophore models comprising hydrophobic, acceptor, donor and ring features. The 3D structural information of previously identified allosteric Akt inhibitors obtained from literature was employed to develop a ligand-based pharmacophore model. Database was generated with drug like subset of ZINC and screening was performed based on 3D similarity to the selected pharmacophore hypotheses. Binding modes and affinities of the ligands were predicted by Glide software. Top scoring hits were further analyzed considering 2D similarity between the compounds, interactions with Akt1, fitness to pharmacophore models, ADME, druglikeness criteria and Induced-Fit docking. Using virtual screening methodologies, derivatives of 3-methyl-xanthine, quinoline-4-carboxamide and 2-[4-(cyclohexa-1,3-dien-1-yl)-1H-pyrazol-3-yl]phenol were proposed as potential leads for allosteric inhibition of Akt1. | Targeting the Akt1 allosteric site to identify novel scaffolds through virtual screening |
S1476927113000972 | Computational blind docking approach was used for mapping of possible binding sites in L-type pyruvate kinase subunit for peptides, RRASVA and the phosphorylated derivative RRAS(Pi)VA, which model the phosphorylatable N-terminal regulatory domain of the enzyme. In parallel, the same docking analysis was done for both substrates of this enzyme, phosphoenolpyruvate (PEP) and adenosine diphosphate (ADP), and for docking of fructose 1,6-bisphosphate (FBP), which is the allosteric activator of the enzyme. The binding properties of the entire surface of the protein were scanned and several possible binding sites were identified in domains A and C of the protein, while domain B revealed no docking sites for peptides or for substrates or the allosteric regulator. It was found that the docking sites of different ligands were partially overlapping, pointing to the possibility that some regulatory effects, observed in the case of L-type pyruvate kinase, may be caused by the competition of different ligands for the same binding sites. | Computational simulation of ligand docking to L-type pyruvate kinase subunit |
S1476927113001084 | The long perceived notion that G-Protein Coupled Receptors (GPCRs) function in monomeric form has recently been changed by the description of a number of GPCRs that are found in oligomeric states. The mechanism of GPCR oligomerization, and its effect on receptor function, is not well understood. In the present study, coarse grained molecular dynamics (CGMD) approach was adopted for studying the self-assembly process of the human GPCR, β2-adrenergic receptor (β2-AR), for which several experimental evidences of the dimerization process and its effect on cellular functions are available. Since the crystal structure of β2-AR lacks the third intracellular loop, initially it was modelled and simulated using restrained MD in order to get a stable starting conformation. This structure was then converted to CG representation and 16 copies of it, inserted into a hydrated lipid bilayer, were simulated for 10μs using the MARTINI force field. At the end of 10μs, oligomers of β2-AR were found to be formed through the self-assembly mechanism which were further validated through various analyses of the receptors. The lipid bilayer analysis also helped to quantify this assembly mechanism. In order to identify the domains which are responsible for this oligomerization, a reverse transformation of the CG system back to all-atom structure and simulated annealing run were carried out at the end of 10μs CGMD run. Analysis of the all-atom dimers thus obtained, revealed that TM1/TM1, H8/H8, TM1/TM5 and TM6/TM6 regions formed most of the dimerization surfaces, which is in accordance with some of the experimental observations and recent simulation results. | Multiscale modelling to understand the self-assembly mechanism of human β2-adrenergic receptor in lipid bilayer |
S147692711300114X | Cse1p and Xpot are two karyopherin proteins that transport the corresponding cargos during the nucleocytoplasmic transport. We utilized Elastic Network Model (ENM) and Finite Element Analysis (FEA) to study their conformational dynamics. These dynamics were interpreted by their intrinsic modes that played key roles in the flexibility of karyopherins, which further affected the binding affinities. The findings included that it was the karyopherin's versatile conformations composed of the same superhelices of HEAT repeats that produced different degrees of functional flexibilities. We presented evidence that these coarse-grained methods could help to elucidate the biological function behind the structures of the two karyopherins. | The intrinsic dynamics of Cse1p and Xpot elucidated by coarse-grained models |
S1476927113001151 | Sequence subgrouping for a given sequence set can enable various informative tasks such as the functional discrimination of sequence subsets and the functional inference of unknown sequences. Because an identity threshold for sequence subgrouping may vary according to the given sequence set, it is highly desirable to construct a robust subgrouping algorithm which automatically identifies an optimal identity threshold and generates subgroups for a given sequence set. To meet this end, an automatic sequence subgrouping method, named ‘Subgrouping Automata’ was constructed. Firstly, tree analysis module analyzes the structure of tree and calculates the all possible subgroups in each node. Sequence similarity analysis module calculates average sequence similarity for all subgroups in each node. Representative sequence generation module finds a representative sequence using profile analysis and self-scoring for each subgroup. For all nodes, average sequence similarities are calculated and ‘Subgrouping Automata’ searches a node showing statistically maximum sequence similarity increase using Student's t-value. A node showing the maximum t-value, which gives the most significant differences in average sequence similarity between two adjacent nodes, is determined as an optimum subgrouping node in the phylogenetic tree. Further analysis showed that the optimum subgrouping node from SA prevents under-subgrouping and over-subgrouping. | Subgrouping Automata: Automatic sequence subgrouping using phylogenetic tree-based optimum subgrouping algorithm |
S1476927113001163 | Methicillin resistant Staphylococcus aureus (MRSA) causes serious infections in humans and becomes resistant to a number of antibiotics. Due to the emergence of antibiotic resistance strains, there is an essential need to develop novel drug targets to address the challenge of multidrug-resistant bacteria. In current study, the idea was to utilize the available genome or proteome in a subtractive genome analyses protocol to identify drug targets within two of the MRSA types, i.e., MRSA ST398 and MRSA 252. Recently, the use of subtractive genomic approaches helped in the identification and characterization of novel drug targets of a number of pathogens. Our protocol involved a similarity search between pathogen and host, essentiality study using the database of essential genes, metabolic functional association study using Kyoto Encyclopedia of Genes and Genomes database (KEGG), cellular membrane localization analysis and Drug Bank database. Functional family characterizations of the identified non homologous hypothetical essential proteins were done by SVMProt server. Druggability potential of each of the identified drug targets was also evaluated by Drug Bank database. Moreover, metabolic pathway analysis of the identified druggable essential proteins with KEGG revealed that the identified proteins are participating in unique and essential metabolic pathways amongst MRSA strains. In short, the complete proteome analyses by the use of advanced computational tools, databases and servers resulted in identification and characterization of few nonhomologous/hypothetical and essential proteins which are not homologous to the host genome. Therefore, these non-homologous essential targets ensure the survival of the pathogen and hence can be targeted for drug discovery. | Identification and characterization of potential drug targets by subtractive genome analyses of methicillin resistant Staphylococcus aureus |
S1476927113001175 | Recognition of protein fold types is an important step in protein structure and function predictions and is also an important method in protein sequence-structure research. Protein fold type reflects the topological pattern of the structure's core. Now there are three methods of protein structure prediction, comparative modeling, fold recognition and de novo prediction. Since comparative modeling is limited by sequence similarity and there is too much workload in de novo prediction, fold recognition has the greatest potential. In order to improve recognition accuracy, a recognition method based on functional domain composition is proposed in this paper. This article focuses on the 124 fold types which have more than 2 samples in LIFCA database. We apply the functional domain composition to predict the fold types of a protein or a domain. In order to evaluate our method and its sensibility to the samples involving SCOP family divided, we tested our results from different aspects. The average sensitivity, specificity and Matthew's correlation coefficient (MCC) of the 124 fold types were found to be 94.58%, 99.96% and 0.91, respectively. Our results indicate that the functional domain composition method is a very promising method for protein fold recognition. And though based on simple classification rules, LIFCA database can grasp the functional features of different proteins, reflecting the corresponding relation between protein structure and function. | Protein fold recognition based on functional domain composition |
S1476927113001187 | Estrogen receptor status and the pathologic response to preoperative chemotherapy are two important indicators of chemotherapeutic sensitivity of tumors in breast cancer, which are used to guide the selection of specific regimens for patients. Microarray-based gene expression profiling, which is successfully applied to the discovery of tumor biomarkers and the prediction of drug response, was suggested to predict the cancer outcomes using the gene signatures differentially expressed between two clinical states. However, many false positive genes unrelated to the phenotypic differences will be involved in the lists of differentially expressed genes (DEGs) when only using the statistical methods for gene selection, e.g. Student's t test, and subsequently affect the performance of the predictive models. For the purpose of improving the prediction of clinical outcomes, we optimized the selection of DEGs by using a combined strategy, for which the DEGs were firstly identified by the statistical methods, and then filtered by a similarity profiling approach that used for candidate gene prioritization. In our study, we firstly verified the molecular functions of the DEGs identified by the combined strategy with the gene expression data generated in the microarray experiments of Si-Wu-Tang, which is a popular formula in traditional Chinese medicine. The results showed that, for Si-Wu-Tang experimental data set, the cancer-related signaling pathways were significantly enriched by gene set enrichment analysis when using the DEG lists generated by the combined strategy, confirming the potentially cancer-preventive effect of Si-Wu-Tang. To verify the performance of the predictive models in clinical application, we used the combined strategy to select the DEGs as features from the gene expression data of the clinical samples, which were collected from the breast cancer patients, and constructed models to predict the chemotherapeutic sensitivity of tumors in breast cancer. After refining the DEG lists by a similarity profiling approach, the Matthew's correlation coefficients of predicting estrogen receptor status and the pathologic response to preoperative chemotherapy with the DEGs selected by the fold change ranking were 0.770 and 0.428, respectively, and were 0.748 and 0.373 with the DEGs selected by SAM, respectively, which were generally higher than those achieved with unrefined DEG lists and those achieved by the candidate models in the second phase of Microarray Quality Control project (0.732 and 0.301, respectively). Our results demonstrated that the strategy of integrating the statistical methods with the gene prioritization methods based on similarity profiling was a powerful tool for DEG selection, which effectively improved the performance of prediction models in clinical applications and can guide the personalized chemotherapy better. | Improving the prediction of chemotherapeutic sensitivity of tumors in breast cancer via optimizing the selection of candidate genes |
S1476927114000024 | In biophysics, the structural prediction of protein–protein complexes starting from the unbound form of the two interacting monomers is a major difficulty. Although current computational docking protocols are able to generate near-native solutions in a reasonable time, the problem of identifying near-native conformations from a pool of solutions remains very challenging. In this study, we use molecular dynamics simulations driven by a collective reaction coordinate to optimize full hydrogen bond networks in a set of protein–protein docking solutions. The collective coordinate biases the system to maximize the formation of hydrogen bonds at the protein–protein interface as well as all over the structure. The reaction coordinate is therefore a measure for docking poses affinity and hence is used as scoring function to identify near-native conformations. | Collective variable driven molecular dynamics to improve protein–protein docking scoring |
S1476927114000036 | The paper studies proteins with domains PF00480 or PF14340, as well as some other poorly characterized proteins, encoded by genes associated with leader peptide genes containing a tract of cysteine codons. Such proteins are hypothetically regulated with cysteine-dependent transcription attenuation, namely the Rho-dependent or classic transcription attenuation. Cysteine is an important structural amino acid in various proteins and is required for synthesis of many sulfur-containing compounds, such as methionine, thiamine, glutathione, taurine and the lipoic acid. Earlier a few species of mycobacteria were predicted by the authors to have cysteine-dependent regulation of operons containing the cysK gene. In Escherichia coli this regulation is absent, and the same operon is regulated by the CysB transcription activator. The paper also studies Rho-dependent and classic transcription regulations in all annotated genes of mycobacteria available in GenBank and their orthologs in Actinomycetales. We predict regulations for many genes involved in sulfur metabolism and transport of sulfur-containing compounds; these regulations differ considerably among species. On the basis of predictions, we assign a putative role to proteins encoded by the regulated genes with unknown function, and also describe the structure of corresponding regulons, predict the lack of such regulations for many genes. Thus, all proteins with the uncharacterized Pfam domains PF14340 and PF00480, as well as some others, are predicted to be involved in sulfur metabolism. We also surmise the affinity of some transporters to sulfur-containing compounds. The obtained results considerably extend earlier large-scale studies of Rho-dependent and classic transcription attenuations. | Gene expression regulation of the PF00480 or PF14340 domain proteins suggests their involvement in sulfur metabolism |
S1476927114000073 | There are many algorithms for detecting epistatic interactions in GWAS. However, most of these algorithms are applicable only for detecting two-locus interactions. Some algorithms are designed to detect only two-locus interactions from the beginning. Others do not have limits to the order of interactions, but in practice take very long time to detect higher order interactions in real data of GWAS. Even the better ones take days to detect higher order interactions in WTCCC data. We propose a fast algorithm for detection of high order epistatic interactions in GWAS. It runs k-means clustering algorithm on the set of all SNPs. Then candidates are selected from each cluster. These candidates are examined to find the causative SNPs of k-locus interactions. We use mutual information from information theory as the measure of association between genotypes and phenotypes. We tested the power and speed of our method on extensive sets of simulated data. The results show that our method has more or equal power, and runs much faster than previously reported methods. We also applied our algorithm on each of seven diseases in WTCCC data to analyze up to 5-locus interactions. It takes only a few hours to analyze 5-locus interactions in one dataset. From the results we make some interesting and meaningful observations on each disease in WTCCC data. In this study, a simple yet powerful two-step approach is proposed for fast detection of high order epistatic interaction. Our algorithm makes it possible to detect high order epistatic interactions in GWAS in a matter of hours on a PC. | Fast detection of high-order epistatic interactions in genome-wide association studies using information theoretic measure |
S1476927114000085 | A big challenge in pharmacology is the understanding of the underlying mechanisms that cause drug-induced adverse reactions (ADRs), which are in some cases similar to each other regardless of different drug indications, and are in other cases different regardless of same drug indications. The FDA Adverse Event Reporting System (FAERS) provides a valuable resource for pharmacoepidemiology, the study of the uses and the effects of drugs in large human population. However, FAERS is a spontaneous reporting system that inevitably contains noise that deviates the application of conventional clustering approaches. By performing a biclustering analysis on the FAERS data we identified 163 biclusters of drug-induced adverse reactions, counting for 691ADRs and 240 drugs in total, where the number of ADR occurrences are consistently high across the associated drugs. Medically similar ADRs are derived from several distinct indications for use in the majority (145/163=88%) of the biclusters, which enabled us to interpret the underlying mechanisms that lead to similar ADRs. Furthermore, we compared the biclusters that contain same drugs but different ADRs, finding the cases where the populations of the patients were different in terms of age, sex, and body weight. We applied a biclustering approach to catalogue the relationship between drugs and adverse reactions from a large FAERS data set, and demonstrated a systematic way to uncover the cases different drug administrations resulted in similar adverse reactions, and the same drug can cause different reactions dependent on the patients’ conditions. | Pharmacoepidemiological characterization of drug-induced adverse reaction clusters towards understanding of their mechanisms |
S1476927114000115 | Protein methylation is a kind of post-translational modification (PTM), and typically takes place on lysine and arginine amino acid residues. Protein methylation is involved in many important biological processes, and most recent studies focused on lysine methylation of histones due to its critical roles in regulating transcriptional repression and activation. Histones possess highly conserved sequences and are homologous in most species. However, there is much less sequence conservation among non-histone proteins. Therefore, mechanisms for identifying lysine-methylated sites may greatly differ between histones and non-histone proteins. Nevertheless, this point of view was not considered in previous studies. Here we constructed two support vector machine (SVM) models by using lysine-methylated data from histones and non-histone proteins for predictions of lysine-methylated sites. Numerous features, such as the amino acid composition (AAC) and accessible surface area (ASA), were used in the SVM models, and the predictive performance was evaluated using five-fold cross-validations. For histones, the predictive sensitivity was 85.62% and specificity was 80.32%. For non-histone proteins, the predictive sensitivity was 69.1% and specificity was 88.72%. Results showed that our model significantly improved the predictive accuracy of histones compared to previous approaches. In addition, features of the flanking region of lysine-methylated sites on histones and non-histone proteins were also characterized and are discussed. A gene ontology functional analysis of lysine-methylated proteins and correlations of lysine-methylated sites with other PTMs in histones were also analyzed in detail. Finally, a web server, MethyK, was constructed to identify lysine-methylated sites. MethK now is available at http://csb.cse.yzu.edu.tw/MethK/. | Identification and characterization of lysine-methylated sites on histones and non-histone proteins |
S1476927114000140 | A new homology model of cyclohexanone monooxygenase (CHMO) from Acinetobacter calcoaceticus is derived based on multiple templates, and in particular the crystal structure of CHMO from Rhodococcus sp. The derived model was fully evaluated, showing that the quality of the new structure was improved over previous models. Critically, the nicotinamide cofactor is included in the model for the first time. Analysis of several molecular dynamics snapshots of intermediates in the enzymatic mechanism led to a description of key residues for cofactor binding and intermediate stabilization during the reaction, in particular Arg327 and the well known conserved motif (FxGxxxHxxxW) in Baeyer–Villiger monooxygenases, in excellent agreement with known experimental and computational data. | Improved homology model of cyclohexanone monooxygenase from Acinetobacter calcoaceticus based on multiple templates |
S1476927114000152 | In this paper, we present a new statistical pattern recognition method for classifying cytotoxic cellular responses to toxic agents. The advantage of the proposed method is to quickly assess the toxicity level of an unclassified toxic agent on human health by bringing cytotoxic cellular responses with similar patterns (mode of action, MoOA) into the same class. The proposed method is a model-based hierarchical classification approach incorporating principal component analysis (PCA) and functional data analysis (FDA). The cytotoxic cell responses are represented by multi-concentration time-dependent cellular response profiles (TCRPs) which are dynamically recorded by using the xCELLigence real-time cell analysis high-throughput (RTCA HT) system. The classification results obtained using our algorithm show satisfactory discrimination and are validated using biological facts by examining common chemical mechanisms of actions with treatment on human hepatocellular carcinoma cells (HepG2). | Mode of action classification of chemicals using multi-concentration time-dependent cellular response profiles |
S1476927114000164 | In order to develop potential ligands to HIV-1 antibody 2G12 toward HIV-1 vaccine, binding mechanisms of the antibody 2G12 with the glycan ligand of d-mannose and d-fructose were theoretically examined. d-Fructose, whose molecular structure is slightly different from d-mannose, has experimentally shown to have stronger binding affinity to the antibody than that of d-mannose. To clarify the nature of d-fructose's higher binding affinity over d-mannose, we studied interaction between the monosaccharides and the antibody using ab initio fragment molecular orbital (FMO) method considering solvation effect as implicit model (FMO-PCM) as well as explicit water model. The calculated binding free energies of the glycans were qualitatively well consistent with the experimentally reported order of their affinities with the antibody 2G12. In addition, the FMO-PCM calculation elucidated the advantages of d-fructose over d-mannose in the solvation energy as well as the entropic contribution term obtained by MD simulations. The effects of explicit water molecules observed in the X-ray crystal structure were also scrutinized by means of FMO methods. Significant pair interaction energies among d-fructose, amino acids, and water molecules were uncovered, which indicated contributions from the water molecules to the strong binding ability of d-fructose to the antibody 2G12. These FMO calculation results of explicit water model as well as implicit water model indicated that the strong binding of d-fructose over d-mannose was due to the solvation effects on the d-fructose interaction energy. | Affinity of HIV-1 antibody 2G12 with monosaccharides: A theoretical study based on explicit and implicit water models |
S1476927114000292 | Human endogenous retroviruses (HERVs) have been found to act as etiological cofactors in several chronic diseases, including cancer, autoimmunity and neurological dysfunction. Immunosuppressive domain (ISD) is a conserved region of transmembrane protein (TM) in envelope gene (env) of retroviruses. In vitro and vivo, evidence has shown that retroviral TM is highly immunosuppressive and a synthetic peptide (CKS-17) that shows homology to ISD inhibits immune function. ISD is probably a potential pathogenic element in HERVs. However, only less than one hundred ISDs of HERVs have been annotated by researchers so far, and universal software for domain prediction could not achieve sufficient accuracy for specific ISD. In this paper, a computational model is proposed to identify ISD in HERVs based on genome sequences only. It has a classification accuracy of 97.9% using Jack-knife test. 117 HERVs families were scanned with the model, 1002 new putative ISDs have been predicted and annotated in the human chromosomes. This model is also applicable to search for ISDs in human T-lymphotropic virus (HTLV), simian T-lymphotropic virus (STLV) and murine leukemia virus (MLV) because of the evolutionary relationship between endogenous and exogenous retroviruses. Furthermore, software named ISDTool has been developed to facilitate the application of the model. Datasets and the software involved in the paper are all available at https://sourceforge.net/projects/isdtool/files/ISDTool-1.0. | ISDTool: A computational model for predicting immunosuppressive domain of HERVs |
S1476927114000334 | Epitopes are immunogenic regions in antigen protein. Prediction of B-cell epitopes is critical for immunological applications. B-cell epitopes are categorized into linear and conformational. The majority of B-cell epitopes are conformational. Several machine learning methods have been proposed to identify conformational B-cell epitopes. However, the quality of these methods is not ideal. One question is whether or not the prediction of conformational B-cell epitopes can be improved by using ensemble methods. In this paper, we propose an ensemble method, which combined 12 support vector machine-based predictors, to predict the conformational B-cell epitopes, using an unbound dataset. AdaBoost and resampling methods are used to deal with an imbalanced labeled dataset. The proposed method achieves AUC of 0.642–0.672 on training dataset with 5-fold cross validation and AUC of 0.579–0.604 on test dataset. We also find some interesting results with the bound and unbound datasets. Epitopes are more accessible than non-epitopes, in bound and unbound datasets. Epitopes are also preferred in beta-turn, in bound and unbound datasets. The flexibility and polarity of epitopes are higher than non-epitopes. In a bound dataset, Asn (N), Glu (E), Gly (G), Lys (K), Ser (S), and Thr (T) are preferred in epitope regions, while Ala (A), Leu (L) and Val (V) are preferred in non-epitope regions. In the unbound dataset, Glu (E) and Lys (K) are preferred in epitope sites, while Leu (L) and Val (V) are preferred in non-epitiopes sites. | An ensemble method for prediction of conformational B-cell epitopes from antigen sequences |
S1476927114000346 | A CIS (common insertion site) indicates a genome region that is hit more frequently by retroviral insertions than expected by chance. Such a region is strongly related to cancer gene loci, which leads to the detection of cancer genes. An algorithm for detecting CISs should satisfy the following: (1) it does not require any prior knowledge of underlying insertion distribution; (2) it can resolve the insertion biases caused by hotspots; (3) it can detect CISs of any biological width; (4) it can identify noises resulting from statistic mistakes and non-CIS insertions; and (5) it can identify the widths of CISs as accurately as possible. We develop a method to resolve these difficulties. We verify a region's significance from two perspectives: distribution width and distribution depth. The former indicates how many insertions in a region while the latter evaluates the insertion distribution across the tumors in a region. We compare our method with kernel density estimation and sliding window on the simulated data, showing that our method not only identifies cancer-related insertions effectively, but also filters noises correctly. The experiments on the real data show that taking insertion distribution into account can highlight significant CISs. We detect 53 novel CISs, some of which have been proven correct by the biological literature. | Determining common insertion sites based on retroviral insertion distribution across tumors |
S1476927114000358 | Lysyl oxidase homolog 2 (LOXL2), also known as lysyl oxidase-like protein 2 is recently been explored as regulator of carcinogenesis and has been shown to be involved in tumor progression and metastasis of several carcinomas. Therefore LOXL2 has been considered as potential therapeutic target. Doing so, its inhibitors as new chemotherapeutic lead molecules: 4-amino-5-(2-hydroxyphenyl)-1,2,4-triazol-3-thione (2a) and 4-(2-hydroxybenzalidine) amine-5-(2-hydroxy) phenyl-1,2,4-triazole-3-thiol (2b) are synthesized by fusion method (refluxed at 160°C). Spectral analysis of these triazole derivatives are characterized by FTIR and NMR. Active binding sites and quality of the LOXL2 model is assessed by Ramachandran plots and finally drug–target analysis is performed by computational virtual screening tools. Compounds 2a and 2b showed optimum target binding affinity with −6.2kcal/mol and −8.9kcal/mol binding energies. This insilico study will add to our understanding of the drug designing and development, and to target cancer-causing proteins more precisely and quickly than before. | Insilico study of anti-carcinogenic lysyl oxidase-like 2 inhibitors |
S1476927114000498 | The Mediator, a conserved multisubunit protein complex in eukaryotic organisms, regulates gene expression by bridging sequence-specific DNA-binding transcription factors to the general RNA polymerase II machinery. In yeast, Mediator complex is organized in three core modules (head, middle and tail) and a separable ‘CDK8 submodule’ consisting of four subunits including Cyclin-dependent kinase CDK8 (CDK8), Cyclin C (CycC), MED12, and MED13. The 3-D structure of human CDK8–CycC complex has been recently experimentally determined. To take advantage of this structure and the improved theoretical calculation methods, we have performed molecular dynamic simulations to study dynamics of CDK8 and two CDK8 point mutations (D173A and D189N), which have been identified in human cancers, with and without full length of the A-loop, as well as the binding between CDK8 and CycC. We found that CDK8 structure gradually loses two helical structures during the 50-ns molecular dynamic simulation, likely due to the presence of the full-length A-loop. In addition, our studies showed the hydrogen bond occupation of the CDK8 A-loop increases during the first 20-ns MD simulation and stays stable during the later 30-ns MD simulation. Four residues in the A-loop of CDK8 have high hydrogen bond occupation, while the rest residues have low or no hydrogen bond occupation. The hydrogen bond dynamic study of the A-loop residues exhibits three types of changes: increasing, decreasing, and stable. Furthermore, the 3-D structures of CDK8 point mutations D173A, D189N, T196A and T196D have been built by molecular modeling and further investigated by 50-ns molecular dynamic simulations. D173A has the highest average potential energy, while T196D has the lowest average potential energy, indicating that T196D is the most stable structure. Finally, we calculated theoretical binding energy of CDK8 and CycC by MM/PBSA and MM/GBSA methods, and the negative values obtained from both methods demonstrate stability of CDK8–CycC complex. Taken together, these analyses will improve our understanding of the exact functions of CDK8 and the interaction with its partner CycC. | All-atomic molecular dynamic studies of human CDK8: Insight into the A-loop, point mutations and binding with its partner CycC |
S1476927114000504 | We describe a new mathematical method for finding very diverged short tandem repeats containing a single indel. The method involves comparison of two frequency matrices: a first matrix for a subsequence before shift and a second one for a subsequence after it. A measure of comparison is based on matrix similarity. The approach developed was applied to analysis of the genomes of Caenorhabditis elegans, Drosophila melanogaster and Saccharomyces cerevisiae. They were investigated regarding the presence of tandem repeats having repeat length equal to 2 – 11 nucleotides except equal to 3, 6 and 9 nucleotides. A number of phase shift regions for these genomes was approximately 2.2×104, 1.5×104 and 1.7×102, respectively. Type I error was less than 5%. The mean length of fuzzy periodicity and phase shift regions was about 220 nucleotides. The regions of fuzzy periodicity having single insertion or deletion occupy substantial parts of the genomes: 5%, 3% and 0.3%, respectively. Only less than 10% of these regions have been detected previously. That is, the number of such regions in the genomes of C. elegans, D. melanogaster and S. cerevisiae is dramatically higher than it has been revealed by any known methods. We suppose that some found regions of fuzzy periodicity could be the regions for protein binding. | Investigation of phase shifts for different period lengths in the genomes of C. elegans, D. melanogaster and S. cerevisiae |
S1476927114000516 | A number of diseases including sepsis, rheumatoid arthritis, diabetes, cardiovascular diseases and hyperinflammatory immune disorders have been associated with Toll like receptor (TLR) 2 and TLR4. Endogenous adaptor protein known as MyD88 adapter-like protein (MAL) bind exclusively to the cytosolic portions of TLR2 and TLR4 to initiate downstream signalling. Brutons tyrosine kinase (BTK) and protein kinase C delta (PKCδ) have been implicated to phosphorylate MAL and activate it to initiate downstream signalling. BTK has been associated with phosphorylation at positions Tyr86 and Tyr106, necessary for the activation of MAL but definite residual target of PKCδ in MAL is still to be explored. To produce a better understanding of the functional domains involved in the formation of MAL–kinase complexes, computer-aided studies were used to characterize the protein–protein interactions (PPIs) of phosphorylated BTK and PKCδ with MAL. Docking and physicochemical studies indicated that BTK was involved in close contact with Tyr86 and Tyr106 of MAL whereas PKCδ may phosphorylate Tyr106 only. Moreover, the electrostatics charge distribution of binding interfaces of BTK and PKCδ were distinct but compatible with respective regions of MAL. Our results implicate that position of Tyr86 is specifically phosphorylated by BTK whereas Tyr106 can be phosphorylated by competitive action of both BTK and PKCδ. Additionally, the residues of MAL which are necessary for interaction with TLR2, TLR4, MyD88 and SOCS-1 also play their roles in maintaining interaction with kinases and can be targeted in future to reduce TLR2 and TLR4 induced pathological responses. | Structural evaluation of BTK and PKCδ mediated phosphorylation of MAL at positions Tyr86 and Tyr106 |
S1476927114000528 | To clarify the molecular evolution and characteristic of beta estrogen receptor (ERβ) gene in Jining Gray goat in China, the entire ERβ gene from Jining Gray goat ovary was amplified, identified and sequenced, and the gene sequences were compared with those of other animals. Functional structural domains and variations in DNA binding domains (DBD) and ligand binding domains (LBD) between Jining Gray goat and Boer goat were analyzed. The results indicate that the ERβ gene in Jining Gray goat includes a 1584bp sequence with a complete open-reading-frame (ORF), encoding a 527 amino acid (aa) receptor protein. Compared to other species, the nucleotide homology is 73.9–98.9% and the amino acid homology is 79.5–98.5%. The main antigenic structural domains lie from the 97th aa to the 286th aa and from the 403rd aa to the 527th aa. The hydrophilicity and the surface probability of the structural domains are distributed throughout a range of amino acids. There are two different amino acids in the DBD and three different amino acids in the LBD between Jining Gray and Boer goats, resulting in dramatically different spatial structures for ERβ protein. These differences may explain the different biological activities of ERβ between the two goat species. This study firstly acquired the whole ERβ gene sequence of Jining Gray goat with a complete open reading frame, and analyzed its gene evolutionary relationship and predicted its mainly functional structural domains, which may very help for further understanding the genome evolution and gene diversity of goat ERβ. | Gene cloning, homology comparison and analysis of the main functional structure domains of beta estrogen receptor in Jining Gray goat |
S147692711400053X | Inferring transcriptional regulatory interactions between transcription factors (TFs) and their targets has utmost importance for understanding the complex regulatory mechanisms in cellular system. In this paper, we introduced a computational method to predict regulatory interactions in Arabidopsis based on gene expression data and sequence information. Support vector machine (SVM) and Jackknife cross-validation test were employed to perform our method on a collected dataset including 178 positive samples and 1068 negative samples. Results showed that our method achieved an overall accuracy of 98.39% with the sensitivity of 94.88%, and the specificity of 93.82%, which suggested that our method can serve as a potential and cost-effective tool for predicting regulatory interactions in Arabidopsis. | A computational method of predicting regulatory interactions in Arabidopsis based on gene expression data and sequence information |
S1476927114000656 | Aspirin (ASA) is a commonly used nonsteroidal anti-inflammatory drug (NSAID), which exerts its therapeutic effects through inhibition of cyclooxygenase (COX) isoform 2 (COX-2), while the inhibition of COX-1 by ASA leads to apparent side effects. In the present study, the relationship between COX-1 non-synonymous single nucleotide polymorphisms (nsSNPs) and aspirin related side effects was investigated. The functional impacts of 37 nsSNPs on aspirin inhibition potency of COX-1 with COX-1/aspirin molecular docking were computationally analyzed, and each SNP was scored based on DOCK Amber score. The data predicted that 22 nsSNPs could reduce COX-1 inhibition, while 15 nsSNPs showed increasing inhibition level in comparison to the regular COX-1 protein. In order to perform a comparing state, the Amber scores for two Arg119 mutants (R119A and R119Q) were also calculated. Moreover, among nsSNP variants, rs117122585 represented the closest Amber score to R119A mutant. A separate docking computation validated the score and represented a new binding position for ASA that acetyl group was located within the distance of 3.86Å from Ser529 OH group. This could predict an associated loss of activity of ASA through this nsSNP variant. Our data represent a computational sub-population pattern for aspirin COX-1 related side effects, and provide basis for further research on COX-1/ASA interaction. | A computational prospect to aspirin side effects: Aspirin and COX-1 interaction analysis based on non-synonymous SNPs |
S1476927114000668 | Rotavirus, the major cause of infantile nonbacterial diarrhea, was found to be associated with development of diabetes-associated auto-antibodies. In our study we tried to find out further potential autoimmune threats of this virus using bioinformatics approach. We took rotaviral proteins to study similarity with Homo sapiens proteome and found most conserved structural protein VP6 matches at two regions with ryanodine receptor, an autoimmune target associated with myasthenia gravis. Myasthenia gravis, a chronic neurodegenerative autoimmune disorder with no typical known reason, is characterized by fluctuating muscle weakness which is typically enhanced during muscular effort. Affected patients generate auto antibodies against mainly acetyl choline receptor and sarcoplasmic reticulum calcium-release channel protein ryanodine receptor. Further, we observed that two regions which matched with ryanodine receptor remain conserved in all circulating rotaviral strains and showed significant antigenecity with respect to myasthenia gravis associated HLA haplotypes. Overall, our study detected rotaviral VP6 as a potential threat for myasthenia gravis and enlighten an area of virus associated autoimmune research. Myasthenia gravis Acetylcholine receptor Ryanodine receptor Inusulinoma antigen 2 Glutamic acid decarboxylase Immune Epitope Database and Analysis Resource Major histocompatibility complex Artificial neural network Stabilized matrix method | In silico study of potential autoimmune threats from rotavirus infection |
S147692711400067X | Receptor-like kinase (RLKs) is an important member in protein kinase family which is widely involved in plant growth, development and defense responses. It is significant to analyze the kinase structure and evolution of pollen RLKs in order to study their mechanisms. In our study, 64 and 73 putative pollen RLKs were chosen from maize and Arabidopsis. Phylogenetic analysis showed that the pollen RLKs were conservative and might had existed before divergence between monocot and dicot which were mainly concentrated in RLCK-VII and LRR-III two subfamilies. Chromosomal localization and gene duplication analysis showed the expansion of pollen RLKs were mainly caused by segmental duplication. By calculating Ka/Ks value of extracellular domain, intracellular domain and kinase domain in pollen RLKs, we found that the pollen RLKs duplicated genes had mainly experienced the purifying selection, while maize might have experienced weaker purifying selection. Meanwhile, extracellular domain might have experienced stronger diversifying selection than intracellular domain in both species. Estimation of duplication time showed that the duplication events of Arabidopsis have occurred approximately between 18 and 69 million years ago, compared to 0.67–170 million years ago of maize. | Structure and evolution analysis of pollen receptor-like kinase in Zea mays and Arabidopsis thaliana |
S1476927114000681 | Long noncoding RNAs (lncRNAs) play essential regulatory roles in the human cancer genome. Many identified lncRNAs are transcribed by RNA polymerase II in which they are polyadenylated, whereby the long intervening noncoding RNAs (lincRNAs) have been widely used for the researches of lncRNAs. To date, the mechanism of lincRNAs polyadenylation related to cancer is rarely fully understood yet. In this paper, first we reported a comprehensive map of global lincRNAs polyadenylation sites (PASs) in five human cancer genomes; second we proposed a grouping method based on the pattern of genes expression and the manner of alternative polyadenylation (APA); third we investigated the distribution of motifs surrounding PASs. Our analysis reveals that about 70% of PASs are located in the sense strand of lincRNAs. Also more than 90% PASs in the antisense strand of lincRNAs are located in the intron regions. In addition, around 40% of lincRNA genes with PASs has APA sites. Four obvious motifs i.e., AATAAA, TTTTTTTT, CCAGSCTGG, and RGYRYRGTGG were detected in the sequences surrounding PASs in the normal and cancer tissues. Furthermore, a novel algorithm was proposed to recognize the lincRNAs PASs of tumor tissues based on support vector machine (SVM). The algorithm can achieve the accuracies up to 96.55% and 89.48% for identification the tumor lincRNAs PASs from the non-polyadenylation sites and the non-lincRNA PASs, respectively. | Genome-wide identification and predictive modeling of lincRNAs polyadenylation in cancer genome |
S1476927114000802 | A translation (framing) code based on the circular code was proposed in Michel (2012) with the identification of X circular code motifs (X motifs shortly) in the bacterial rRNA of Thermus thermophilus, in particular in the ribosome decoding center. Three classes of X motifs are now identified in the rRNAs of bacteria Escherichia coli and Thermus thermophilus, archaea Pyrococcus furiosus, nuclear eukaryotes Saccharomyces cerevisiae, Triticum aestivum and Homo sapiens, and chloroplast Spinacia oleracea. The universally conserved nucleotides A1492 and A1493 in all studied rRNAs (bacteria, archaea, nuclear eukaryotes, and chloroplasts) belong to X motifs (called m AA). The conserved nucleotide G530 in rRNAs of bacteria and archaea belongs to X motifs (called m G). Furthermore, the X motif m G is also found in rRNAs of nuclear eukaryotes and chloroplasts. Finally, a potentially important X motif, called m, is identified in all studied rRNAs. With the available crystallographic structures of the Protein Data Bank PDB, we also show that these X motifs m AA, m G, and m belong to the ribosome decoding center of all studied rRNAs with possible interaction with the mRNA X motifs and the tRNA X motifs. The three classes of X motifs identified here in rRNAs of several and different organisms strengthen the concept of translation code based on the circular code. | Circular code motifs in the ribosome decoding center |
S1476927114000814 | Adhesion of uropathogenic E. coli (UPEC) to uroepithelial cell receptors is facilitated through the lectin domain of FimH adhesin. In the current study, Molecular Dynamics (MD) simulations were performed for the lectin domain of FimH from UPEC J96. The high affinity state lectin domain was found to be stable and rigid during the simulations. Further, based on conserved subsequences around one of the disulfide forming cysteines, two sequence motifs were designed. An immunoinformatics approach was utilized to identify linear and discontinuous epitopes for the lectin domain of FimH. We propose that the accessibility of predicted epitopes should also be assessed in a dynamic aqueous environment to evaluate the potential of vaccine candidates. Since MD simulation data enables assessing the accessibility in a dynamic environment, we evaluated the accessibility of the top ranked discontinuous and linear epitopes using structures obtained at every nanosecond (ns) in the 1–20ns MD simulation timeframe. Knowledge gained in this study has a potential utility in the design of vaccine candidates for Urinary Tract Infection (UTI). | Molecular dynamics simulations of lectin domain of FimH and immunoinformatics for the design of potential vaccine candidates |
S1476927114000930 | Selecting the values of parameters used by de novo genomic assembly programs, or choosing an optimal de novo assembly from several runs obtained with different parameters or programs, are tasks that can require complex decision-making. A key parameter that must be supplied to typical next generation sequencing (NGS) assemblers is the k-mer length, i.e., the word size that determines which de Bruijn graph the program should map out and use. The topic of assembly selection criteria was recently revisited in the Assemblathon 2 study (Bradnam et al., 2013). Although no clear message was delivered with regard to optimal k-mer lengths, it was shown with examples that it is sometimes important to decide if one is most interested in optimizing the sequences of protein-coding genes (the gene space) or in optimizing the whole genome sequence including the intergenic DNA, as what is best for one criterion may not be best for the other. In the present study, our aim was to better understand how the assembly of unicellular fungi (which are typically intermediate in size and complexity between prokaryotes and metazoan eukaryotes) can change as one varies the k-mer values over a wide range. We used two different de novo assembly programs (SOAPdenovo2 and ABySS), and simple assembly metrics that also focused on success in assembling the gene space and repetitive elements. A recent increase in Illumina read length to around 150bp allowed us to attempt de novo assemblies with a larger range of k-mers, up to 127bp. We applied these methods to Illumina paired-end sequencing read sets of fungal strains of Paracoccidioides brasiliensis and other species. By visualizing the results in simple plots, we were able to track the effect of changing k-mer size and assembly program, and to demonstrate how such plots can readily reveal discontinuities or other unexpected characteristics that assembly programs can present in practice, especially when they are used in a traditional molecular microbiology laboratory with a ‘genomics corner’. Here we propose and apply a component of a first pass validation methodology for benchmarking and understanding fungal genome de novo assembly processes. | The complex task of choosing a de novo assembly: Lessons from fungal genomes |
S1476927114001017 | Predicting essential proteins is highly significant because organisms can not survive or develop even if only one of these proteins is missing. Improvements in high-throughput technologies have resulted in a large number of available protein–protein interactions. By taking advantage of these interaction data, researchers have proposed many computational methods to identify essential proteins at the network level. Most of these approaches focus on the topology of a static protein interaction network. However, the protein interaction network changes with time and condition. This important inherent dynamics of the protein interaction network is overlooked by previous methods. In this paper, we introduce a new method named CDLC to predict essential proteins by integrating dynamic local average connectivity and in-degree of proteins in complexes. CDLC is applied to the protein interaction network of Saccharomyces cerevisiae. The results show that CDLC outperforms five other methods (Degree Centrality (DC), Local Average Connectivity-based method (LAC), Sum of ECC (SoECC), PeC and Co-Expression Weighted by Clustering coefficient (CoEWC)). In particular, CDLC could improve the prediction precision by more than 45% compared with DC methods. CDLC is also compared with the latest algorithm CEPPK, and a higher precision is achieved by CDLC. CDLC is available as Supplementary materials. The default settings of active threshold and alpha-parameter are 0.8 and 0.1, respectively. | A new method for predicting essential proteins based on dynamic network topology and complex information |
S1476927114001029 | Identification of small-molecule compounds that can bind specifically and stably to protein targets of biological interest is a challenge task in structure-based drug design. Traditionally, several fast approaches such as empirical scoring functions and free energy analysis have been widely used to fulfill for this purpose. In the current study, we raised the rigorous quantum mechanics/molecular mechanics in combination with semi-empirical Poisson–Boltzmann/surface area (QM/MM–PB/SA) as an efficient strategy to characterize the intermolecular interaction between Akt kinase and its small-molecule ligands, although this hybrid approach is computationally expensive as compared to those empirical methods. In a round of experimental activity reproduction test based on a set of known Akt–inhibitor complexes, QM/MM–PB/SA has been shown to perform much better than two widely used scoring functions as well as the sophisticated MM-PB/SA analysis with or without improvement by molecular dynamics (MD) simulations. Next, the QM/MM–PB/SA was employed to screen for strong Akt binders from an apigenin analogue set. Consequently, four compounds, namely apigenin, quercetin, gallocatechin and myricetin, were suggested to have high binding potency to Akt active site. A further kinase assay was conducted to determine the inhibitory activity of the four promising candidates against Akt kinase, resulting in IC50 values of 38.4, 67.5, 157.1 and 25.5nM, respectively. | QM/MM–PB/SA scoring of the interaction strength between Akt kinase and apigenin analogues |
S1476927114001030 | Background Many structural centrality measures were proposed to predict putative disease genes on biological networks. Closeness is one of the best-known structural centrality measures, and its effectiveness for disease gene prediction on undirected biological networks has been frequently reported. However, it is not clear whether closeness is effective for disease gene prediction on directed biological networks such as signaling networks. Results In this paper, we first show that closeness does not significantly outperform other well-known centrality measures such as Degree, Betweenness, and PageRank for disease gene prediction on a human signaling network. In addition, we observed that prediction accuracy by the closeness measure was worse than that by a reachability measure, but closeness could efficiently predict disease genes among a set of genes with the same reachability value. Based on this observation, we devised a novel structural measure, hierarchical closeness, by combining reachability and closeness such that all genes are first ranked by the degree of reachability and then the tied genes are further ranked by closeness. We discovered that hierarchical closeness outperforms other structural centrality measures in disease gene prediction. We also found that the set of highly ranked genes in terms of hierarchical closeness is clearly different from that of hub genes with high connectivity. More interestingly, these findings were consistently reproduced in a random Boolean network model. Finally, we found that genes with relatively high hierarchical closeness are significantly likely to encode proteins in the extracellular matrix and receptor proteins in a human signaling network, supporting the fact that half of all modern medicinal drugs target receptor-encoding genes. Conclusion Taken together, hierarchical closeness proposed in this study is a novel structural measure to efficiently predict putative disease genes in a directed signaling network. | Hierarchical closeness efficiently predicts disease genes in a directed signaling network |
S1476927114001042 | We used an evolutionary genomics approach to identify genes that are under lineage-specific positive selection in six species of the genus Bacteroides, including three strains of pathogenic Bacteroides fragilis. Using OrthoMCL, we identified 1275 orthologous gene clusters present in all eight Bacteroides genomes. A total of 52 genes were identified as under positive selection in the branch leading to the B. fragilis lineage, including a number of genes encoding cell surface proteins such as TonB-dependent receptor. Three-dimensional structural mapping of positively selected sites indicated that many residues under positive selection occur in the extracellular loops of the proteins. The adaptive changes in these positively selected genes might be related to dynamic interactions between the host immune systems and the surrounding intestinal environment. | Genome-wide evidence of positive selection in Bacteroides fragilis |
S1476927114001054 | Identification of DNA-binding proteins is essential in studying cellular activities as the DNA-binding proteins play a pivotal role in gene regulation. In this study, we propose newDNA-Prot, a DNA-binding protein predictor that employs support vector machine classifier and a comprehensive feature representation. The sequence representation are categorized into 6 groups: primary sequence based, evolutionary profile based, predicted secondary structure based, predicted relative solvent accessibility based, physicochemical property based and biological function based features. The mRMR, wrapper and two-stage feature selection methods are employed for removing irrelevant features and reducing redundant features. Experiments demonstrate that the two-stage method performs better than the mRMR and wrapper methods. We also perform a statistical analysis on the selected features and results show that more than 95% of the selected features are statistically significant and they cover all 6 feature groups. The newDNA-Prot method is compared with several state of the art algorithms, including iDNA-Prot, DNAbinder and DNA-Prot. The results demonstrate that newDNA-Prot method outperforms the iDNA-Prot, DNAbinder and DNA-Prot methods. More specific, newDNA-Prot improves the runner-up method, DNA-Prot for around 10% on several evaluation measures. The proposed newDNA-Prot method is available at http://sourceforge.net/projects/newdnaprot/ | newDNA-Prot: Prediction of DNA-binding proteins by employing support vector machine and a comprehensive sequence representation |
S1476927114001066 | A large challenge in the post-genomic era is to obtain the quantitatively dynamic interactive information of the important constitutes of underlying systems. The S-system is a dynamic and structurally rich model that determines the net strength of interactions between genes and/or proteins. Good generation characteristics without the need for prior information have allowed S-systems to become one of the most promising canonical models. Various evolutionary computation technologies have recently been developed for the identification of system parameters and skeletal-network structures. However, the gaps between the truncated and preserved terms remain too small. Additionally, current research methods fail to identify the structures of high dimensional systems (e.g., 30 genes with 1800 connections). Optimization technologies should converge fast and have the ability to adaptively adjust the search. In this study, we propose a seeding-inspired chemotaxis genetic algorithm (SCGA) that can force evolution to adjust the population movement to identify a favorable location. The seeding-inspired training strategy is a method to achieve optimal results with limited resources. SCGA introduces seeding-inspired genetic operations to allow a population to possess competitive power (exploitation and exploration) and a winner-chemotaxis-induced population migration to force a population to repeatedly tumble away from an attractor and swim toward another attractor. SCGA was tested on several canonical biological systems. SCGA not only learned the correct structure within only one to three pruning steps but also ensures pruning safety. The values of the truncated terms were all smaller than 10−14, even for a thirty-gene system. | Seeding-inspired chemotaxis genetic algorithm for the inference of biological systems |
S1476927114001078 | Protein nitration is an important post-translational modification regulating protein structure and function, especially for heme proteins. Myoglobin (Mb) is an ideal protein model for investigating the structure and function relationship of heme proteins. With limited structural information available for nitrated heme proteins from experiments, we herein performed a molecular dynamics study of human Mb with successive nitration of Tyr103, Tyr146, Trp7 and Trp14. We made a detailed comparison of protein motions, intramolecular contacts and internal cavities of nitrated Mbs with that of native Mb. It showed that although nitration of both Tyr103 and Tyr146 slightly alters the local conformation of heme active site, further nitration of both Trp7 and Trp14 shifts helix A apart from the rest of protein, which results in altered internal cavities and forms a water channel, representing an initial stage of Mb unfolding. The computational study provides an insight into the nitration of heme proteins at an atomic level, which is valuable for understanding the structure and function relationship of heme proteins in non-native states by nitration. | Computational insight into nitration of human myoglobin |
S147692711400108X | Background Bacillus anthracis is a gram positive, spore forming, rod shaped bacteria which is the etiologic agent of anthrax – cutaneous, pulmonary and gastrointestinal. A recent outbreak of anthrax in a tropical region uncovered natural and in vitro resistance against penicillin, ciprofloxacin, quinolone due to over exposure of the pathogen to these antibiotics. This fact combined with the ongoing threat of using B. anthracis as a biological weapon proves that the identification of new therapeutic targets is urgently needed. Methods In this computational approach various databases and online based servers were used to detect essential proteins of B. anthracis A0248. Protein sequences of B. anthracis A0248 strain were retrieved from the NCBI database which was then run in CD-hit suite for clustering. NCBI BlastP against the human proteome and similarity search against DEG were done to find out essential human non-homologous proteins. Proteins involved in unique pathways were analyzed using KEGG genome database and PSORTb, CELLO v.2.5, ngLOC – these three tools were used to deduce putative cell surface proteins. Results Successive analysis revealed 116 proteins to be essential human non-homologs among which 17 were involved in unique metabolic pathways and 28 were predicted as membrane associated proteins. Both types of proteins can be exploited as they are unlikely to have homologous counterparts in the human host. Conclusion Being human non-homologous, these proteins can be targeted for potential therapeutic drug development in future. Targets on unique metabolic and membrane-bound proteins can block cell wall synthesis, bacterial replication and signal transduction respectively. | Identification of potential drug targets by subtractive genome analysis of Bacillus anthracis A0248: An in silico approach |
S1476927114001091 | Phylogenetic trees are typically constructed using genetic and genomic data, and provide robust evolutionary relationships of species from the genomic point of view. We present an application of network motif mining and analysis of metabolic pathways that when used in combination with phylogenetic trees can provide a more complete picture of evolution. By using distributions of three-node motifs as a proxy for metabolic similarity, we analyze the ancestral origin of Eukaryotic organelles from the metabolic point of view to illustrate the application of our motif mining and analysis network approach. Our analysis suggests that the hypothesis of an early proto-Eukaryote could be valid. It also suggests that a δ- or ϵ-Proteobacteria may have been the endosymbiotic partner that gave rise to modern mitochondria. Our evolutionary analysis needs to be extended by building metabolic network reconstructions of species from the phylum Crenarchaeota, which is considered to be a possible archaeal ancestor of the eukaryotic cell. In this paper, we also propose a methodology for constructing phylogenetic trees that incorporates metabolic network signatures to identify regions of genomically-estimated phylogenies that may be spurious. We find that results generated from our approach are consistent with a parallel phylogenetic analysis using the method of feature frequency profiles. | Metabolic network motifs can provide novel insights into evolution: The evolutionary origin of Eukaryotic organelles as a case study |
S1476927114001108 | The interaction between barrier-to-autointegration factor dimer (BAF2) and LEM domain of emerin (EmLEM) was studied by molecular simulation methods. Nonspecific fragment of double-strand DNA molecule was docked with each chain of BAF2 by ZDOCK program. The model of DNA2:BAF2:EmLEM was thus constructed. The mutant Gly25Glu of BAF2 was manually constructed to explore the detailed effect of the mutation on the binding of BAF2 and EmLEM. It has been experimentally suggested that point mutation Gly25Glu can disturb the binding between BAF2 and EmLEM. Then, molecular dynamics (MD) simulations were performed on DNA2:BAF2(WT):EmLEM and DNA2:BAF2(MT):EmLEM complexes. 30ns trajectories revealed that the trajectory fluctuations of MT complex are more violent than that of the WT complex. Further, the binding free energy analysis showed that the electronegative residues Asp57, Glu61 and Asp65 from chain A, glu36 from chain B of BAF2 mainly contribute to interact with EmLEM. Besides, a stable π–π stack between trp62 and phe39 from BAF2(WT) chain B is destroyed by Glu25 in BAF2(MT). As a result, trp62 forms an interaction with glu25, and phe39 converts to strengthen affinity to EmLEM. On the other hand, Trp62 from chain A also forms a strong interaction with MT Glu25. Thus, with the docking of DNA, BAF2(MT) has higher affinity with EmLEM than BAF2(WT). | Molecular simulation investigation on the interaction between barrier-to-autointegration factor dimer or its Gly25Glu mutant and LEM domain of emerin |
S147692711400111X | Reconstructions of genome-scale metabolic networks from different organisms have become popular in recent years. Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes. In previous studies, optimization algorithms have been implemented to identify the near-optimal sets of knockout genes for improving metabolite production. However, previous works contained premature convergence and the stop criteria were not clear for each case. Therefore, this study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux balance analysis (ACOFBA) to predict near optimal sets of gene knockouts in an effort to maximize growth rates and the production of certain metabolites. Here, we present a case study that uses Baker’s yeast, also known as Saccharomyces cerevisiae, as the model organism and target the rate of vanillin production for optimization. The results of this study are the growth rate of the model organism after gene deletion and a list of knockout genes. The ACOFBA algorithm was found to improve the yield of vanillin in terms of growth rate and production compared with the previous algorithms. | Identification of gene knockout strategies using a hybrid of an ant colony optimization algorithm and flux balance analysis to optimize microbial strains |
S1476927114001121 | The MYB proteins comprise one of the largest families of plant transcription factors (TFs) and many of MYB families, which play essential roles in plant growth, development and respond to environmental stresses, and have yet been identified in plant. Previous research has shown that miR159 family members repressed the conserved plant R2R3 MYB domain TFs in model plants. In the present research, we identified three potato novel miR159 family members named as stu-miR159a, stu-miR159b and stu-miR159c based on bioinformatics analysis. Target prediction showed that they have a bite sit on the three GAMyb-like genes (StGAMyb-like1, StGAMyb-like2.1 and StGAMyb-like2.2) of potato. Those GAMyb-like genes also have been selected and cloned from potato, which belong to R2R3 MYB domain TFs. We further measured expressional levels of stu-miR159s and potato GAMyb-like genes during the different periods of drought treated samples using quantitative real-time PCR (qRT-PCR). The results showed that they had a opposite expression pattern, briefly, three stu-miR159 members showed similar expressional trends which were significantly decreased expression after experiencing 25 days of drought stress treatment, while the potato GAMyb-like family members were greatly increased. Therefore, we suggested that stu-miR159s negatively regulated the expression of potato GAMyb-like genes which responsible for drought stress. The findings can facilitate functional studies of miRNAs in plants and provide molecular evidence for involvement process of drought tolerance in potato. | Identification of miR159s and their target genes and expression analysis under drought stress in potato |
S1476927114001248 | A long standing problem in structural bioinformatics is to determine the three-dimensional (3-D) structure of a protein when only a sequence of amino acid residues is given. Many computational methodologies and algorithms have been proposed as a solution to the 3-D Protein Structure Prediction (3-D-PSP) problem. These methods can be divided in four main classes: (a) first principle methods without database information; (b) first principle methods with database information; (c) fold recognition and threading methods; and (d) comparative modeling methods and sequence alignment strategies. Deterministic computational techniques, optimization techniques, data mining and machine learning approaches are typically used in the construction of computational solutions for the PSP problem. Our main goal with this work is to review the methods and computational strategies that are currently used in 3-D protein prediction. | Three-dimensional protein structure prediction: Methods and computational strategies |
S147692711400125X | Organisms thriving at extreme cold surroundings are called as psychrophiles and they present a wealth of knowledge about sequence adjustments in proteins that had occurred during the adaptation to low temperatures. In this paper, we propose a new cascading model to investigate the basis for psychrophilicity. In this model, a superior classifier was used to discriminate psychrophilic from mesophilic protein sequences, and then the PART rule generating algorithm was applied on the input instances that are correctly classified by the classifier, to generate human interpretable rules. These derived rules were further validated on a structural dataset and finally analyzed to discover the underlying biological basis about the psychrophilicity. In this study, we have used one of the key features of psychrophilic proteins accountable for remaining functional in extreme cold temperature surroundings i.e., global patterns of amino acid composition as the input features. The rotation forest classifier outperformed all the other classifiers with maximum accuracy of 70.5% and maximum AUC of 0.78. The effect of sequence length on the classification accuracy was also investigated. The analysis of the derived rules and interpretation of the analyzed results had revealed some interesting phenomena such as the amino acids A, D, G, F, and S are over-represented, and T is under-represented in psychrophilic proteins. These findings augment the existing domain knowledge for psychrophilic sequence features. | Inferring biological basis about psychrophilicity by interpreting the rules generated from the correctly classified input instances by a classifier |
S1476927114001261 | Dengue virus can ignite both protective and pathogenic responses in human. The pathogenesis is related with modified functioning of our immune system during infection. Pattern recognition receptors like Toll like receptor 3 is vital for the induction of innate immunity in case of Dengue infection. Toll like receptor 3 induces TRIF mediated activation of Type 1 interferons and Fc receptor mediated induction of cytokines. Interferons have been related with clearance of Dengue virus but it has adopted modified regulatory mechanisms to counter this effect. SOCS protein is also induced due to the interferon and cytokine mediated signalling which can subsequently play its part in the regulation of interferon and cytokine production. Our hypothesis in this study relates the pathogenesis of Dengue virus with the SOCS mediated inhibition of our innate immunity. We used the qualitative formalism of René Thomas to model the biological regulatory network of Toll like receptor 3 mediated signalling pathway in an association with pathogenesis of dengue. Logical parameters for the qualitative modelling were inferred using a model checking approach implemented in SMBioNet. A linear hybrid model, parametric linear hybrid automaton, was constructed to incorporate the activation and inhibition time delays in the qualitative model. The qualitative model captured all the possible expression dynamics of the proteins in the form of paths, some of which were observed as abstract cycles (representing homoeostasis) and diverging paths towards stable states. The analysis of the qualitative model highlighted the importance of SOCS protein in elevating propagation of dengue virus through inhibition of type 1 interferons. Detailed qualitative analysis of regulatory network endorses our hypothesis that elevated levels of cytokine subsequently induce SOCS expression which in turn results into the continuous down-regulation of Toll like receptor 3 and interferon. This may result into the Dengue pathogenesis during the stage of immunosuppression. Further analysis with HyTech (HYbrid TECHnology) tool provided us with the real-time constraints (delay constraints) of the proteins involved in the cyclic paths of the regulatory network backing the evidence provided by the qualitative analysis. The HyTech results also suggest that the role of SOCS is vital in homoeostasis. | On the modelling and analysis of the regulatory network of dengue virus pathogenesis and clearance |
S1476927114001273 | Apoptosis is a strictly organized course which keeps the healthy survival/death equilibrium. Disregulation in apoptosis may lead autoimmunity or cancer, but increased apoptosis can lead degenerative diseases. Studies during the last several years have identified numerous affected miRNAs in association with apoptosis, their target genes and biological functions, and possible drug interventions. Polymorphisms in miRNA genes or miRNA target sites (miRSNPs) can modify miRNA action. While polymorphisms in miRNA genes are relatively rare, SNPs in miRNA-binding sites in target genes are more frequent. Several studies have shown that SNPs in miRNA target sites enhance or weaken the interaction between miRNA and its target transcripts and are associated with cancers and other diseases. We aimed to identify miRSNPs on executioner caspase, CASP3 gene (caspase-3) and SNPs in miRNA genes targeting 3′UTR of CASP3 and assessing the impact of these miRSNPs and SNPs of miRNA genes targeting 3′UTR of CASP3 with respect to apoptosis. We identified 89 different miRNA binding sites (for 43 different miRNAs) and 16 different SNPs in binding sites of miRNA in the 3′UTR of the CASP3 gene. Also, 2 SNPs (rs372435266 and rs190144655) were found on this miRNA′ genomic sequence. One of them crossmatched with a SNP in the 3′UTR of CASP3 that we found formerly. This miRNA was miR-4802-3p. Besides, miR-4802-3p targets three other apoptosis related genes, XIAP, IL1A and SOX2. This means that miR-4802-3p may also have a critical effect on apoptosis via different pathways other than caspase-3. We can therefore conclude that this is the first study proving a strong association between miR-4802-3p and apoptosis upon computational targetting analysis. | Computational analysis of 3′UTR region of CASP3 with respect to miRSNPs and SNPs in targetting miRNAs |
S1476927114001285 | Programmed cell death or apoptosis plays a vital physiological role in the development and homeostasis. Any discrepancy in apoptosis may trigger testicular and neurodegenerative diseases, ischemic damage, autoimmune disorders and many types of cancer. Tcte3 (T-complex testis expressed 3) is an accessory component of axonemal and cytoplasmic dynein which expresses predominantly in meiotic and postmeiotic germ cells. It plays an essential role during spermatogenesis; however, to explore its diverse and complex functioning in male germ cell apoptosis, requires further prosecution. Here, 2D-gel electrophoresis, mass spectrometry and qRT–PCR analyses were performed to elucidate the differential expression of genes, in both wild-type and homozygous Tcte3-3 mice. We observed an increased expression of Tcte3 in homozygotes as compared to wild-type testes. Perpetually, an increased expression of Anxa5 and Pebp1, while a lower expression of Rsph1 was detected in Tcte3-3 −/− mice. We propose that over-expression of Pebp1 and Anxa5 in Tcte3-3 −/− testes might be due to increased apoptosis. To evaluate this possibility, testes specific microarray data set extracted from NCBI gene ontology omnibus (GEO) was used to cluster the possible co-expression partners of Tcte3. Further functional coherence of compiled candidate genes was monitored computationally by studying the common TFBS overlapped at the regulatory regions. Differential expression of Tcte3-3 and its involvement in apoptosis may provide a basis for the investigation of transcriptional specificities of other Tcte3 paralogs (Tcte3-1 and Tcte3-2). A complete understanding of controlling factors which have implications in regulating tissue-specific Tcte3 expression would provide additional insights into the gene control events. The collective knowledge may prove useful for the development of novel therapeutic regimen and would open new avenues in defining selective roles of Tcte3 in germ cell development. | Disruption of murine Tcte3-3 induces tissue specific apoptosis via co-expression of Anxa5 and Pebp1 |
S1476927114001297 | In this study, the structural and dynamic properties of two major porins (OmpF and OmpC) in Escherichia coli are investigated using molecular dynamics (MD) simulations. Both porins have the extracellular loop L3 folded halfway through the pore to form a constriction area. The solute influx and efflux are controlled by the L3 movement. E296 and D312 in OmpF and homologous D299 and D315 in OmpC located on the barrel wall are found to play a key role in L3 gating activity. All possible charged states of both E296(D299) and D312(315) are applied in this study to observe changes in overall structure and especially L3 movement. The results show that different protonation states of both residues cause the large-scale deviations in structure and pore cavity especially in OmpF. Fully charged E296(D299) and D312(315) increase the protein flexibility significantly. Deprotonating at least one of E296(D299) and D312(315) helps to fasten L3 to the barrel wall and maintain pore size. Lacking of interactions with D312(315) can lead to the pore closure in OmpF. Comparing with OmpC, not only is OmpF less stable, but it is also more sensitive to the charge states of both E296(D299) and D312(315). | How do the protonation states of E296 and D312 in OmpF and D299 and D315 in homologous OmpC affect protein structure and dynamics? Simulation studies |
S1476927114001303 | In order to extract phylogenetic information from DNA sequences, the new normalized k-word average relative distance is proposed in this paper. The proposed measure was tested by discriminate analysis and phylogenetic analysis. The phylogenetic trees based on the Manhattan distance measure are reconstructed with k ranging from 1 to 12. At the same time, a new method is suggested to reduce the matrix dimension, can greatly lessen the amount of calculation and operation time. The experimental assessment demonstrated that our measure was efficient. What's more, comparing with other methods’ results shows that our method is feasible and powerful for phylogenetic analysis. | A novel k-word relative measure for sequence comparison |
S1476927114001431 | Protein p7 of HCV is a 63 amino acid channel forming membrane protein essential for the progression of viral infection. With this momentousness, p7 emerges as an important target for antiviral therapy. A series of small molecule drugs, such as amantadine, rimantadine, amiloride, hexamethylene amiloride, NN-DNJ and BIT225 have been found to affect the channel activity. These compounds are docked against monomeric and hexameric structures of p7 taken at various time steps from a molecular dynamics simulation of the protein embedded in a hydrated lipid bilayer. The energetics of binding identifies the guanidine based ligands as the most potent ligands. The adamantanes and NN-DNJ show weaker binding energies. The lowest energy poses are those at the site of the loop region for the monomer and hexamer. For the latter, the poses show a tendency of the ligand to face the lumen of the pore. The mode of binding is that of a balance between hydrophobic interactions and hydrogen bond formation with backbone atoms of the protein. | Docking assay of small molecule antivirals to p7 of HCV |
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