Remaining problem cases are traced to inaccuracies in the Rosetta all-atom energy function. In five additional blind tests, SWA achieves sub-Angstrom accuracy models, including the first such success in a protein/RNA binding interface, the YbxF/kink-turn interaction in the fourth ‘RNA-puzzle’ competition. These results establish all-atom enumeration as an unusually systematic approach to ab initio protein structure modeling that can leverage high performance computing and physically realistic energy functions to more consistently achieve atomic accuracy.Consistently predicting biopolymer structure at atomic resolution from sequence alone remains a difficult problem, even for small sub-segments of large proteins. Such loop prediction challenges, which arise frequently in comparative modeling and protein design, can become intractable as loop lengths exceed 10 residues and if surrounding side-chain conformations are erased. Current approaches, such as the protein local optimization protocol or kinematic inversion closure (KIC) Monte Carlo, involve stages that coarse-grain proteins, simplifying modeling but precluding a systematic search of all-atom configurations. This article introduces an alternative modeling strategy based on a ‘stepwise ansatz’, recently developed for RNA modeling, which posits that any realistic all-atom molecular conformation can be built up by residue-by-residue stepwise enumeration. When harnessed to a dynamic-programming-like recursion in the Rosetta framework, the resulting stepwise assembly (SWA) protocol enables enumerative sampling of a 12 residue loop at a significant but achievable cost of thousands of CPU-hours. In a previously established benchmark, SWA recovers crystallographic conformations with sub-Angstrom accuracy for 19 of 20 loops, compared to 14 of 20 by KIC modeling with a comparable expenditure of computational power. Furthermore, SWA gives high accuracy results on an additional set of 15 loops highlighted in the biological literature for their irregularity or unusual length. Successes include  The method has furthermore been successful in blind tests Recently, a conceptually distinct approach to modeling macromolecule structure has arisen from efforts to predict complex RNA structures in all-atom detail Given its advantages over prior RNA modeling approaches and its assurance of complete sampling, stepwise assembly also holds promise for difficult problems in protein structure prediction. This study presents the first application of SWA to proteins, focusing on loop modeling. Protein loop modeling problems arise frequently in comparative modeling, designing new proteins, and solving or refining protein folds with limited crystallographic or NMR data, including weakly populated (‘invisible’) states cis-Pro touch turns, loops that thread through tunnels formed by the surrounding protein, and segments of unprecedented length. These cases are solved ab initio by SWA with sub-Angstrom accuracy, albeit at the expense of thousands of CPU-hours per loop. Additional atomic-accuracy results from five blind predictions, including a comparative modeling problem for a protein/RNA complex, give further support to the stepwise ansatz and its Rosetta SWA implementation. Analogous to recent successes of RNA modeling in structural biology To address the conformational sampling bottleneck, this article describes the import of stepwise assembly from RNA modeling to protein loop structure prediction in the Rosetta framework . The res1oyc), an unsolved case in recent studies NHB in 1oyc case). Furthermore, loops make few interactions with the non-polar core, arguably the best modeled region of protein structures 1oyc loop residues , which uses backbone-only hierarchical loop build-up followed by molecular mechanics force field refinement cis proline sampling, and more stringent chain closure. Even in these more extensive calculations , crystallographically observed  combinations, and closed loop geometries, sampling each amino acid at sub-Angstrom resolution requires at least tens of backbone conformers (see SI Methods), resulting in at least 1012 backbone torsional combinations for a 12-residue loop. Subsequent side-chain optimization on these backbones would require tens of millions of CPU-hours Most current loop modeling approaches that seek atomic resolution share a seemingly necessary working approximation: an initial search phase using a reduced representation with simplified or no side-chain atoms. Such coarse search phases avoid the complexity and large number of local minima inherent to all-atom representations, but fail to capture non-polar packing interactions and hydrogen bonds involving side-chain atoms, which are pervasive . This protocol has been coded into the Rosetta framework as a stepwise assembly (SWA) algorithm.Recent work in three-dimensional RNA modeling 1oyc loop. For the first five residues  implementation was used to carry out structure prediction on a benchmark set of thirty-five protein loops. Twenty of these cases were 12-residue loops used previously to test PLOP and Rosetta approaches First, examining the subset of twenty loops used in prior PLOP and Rosetta studies permitted direct comparison of SWA to these state-of-the-art methods see also . Here, tcis prolines, and reported RMSD for the very lowest energy model.] The high accuracy SWA predictions that were intractable to previous PLOP and/or Rosetta approaches included the 1oyc case described above  and 0.89 Å (best of five lowest energy structures). SWA modeling again outperformed KIC modeling overall, although not by as much as in the first 20-loop benchmark [1.9 Å (lowest energy) and 0.94 Å (best of five)]. In 8 of these 15 complex loop tests, SWA returned at least one of five lowest energy models with sub-Angstrom accuracy . High-re12–fold more accessible conformations). However, the stepwise ansatz underlying the SWA method constrains sampling to a subspace that requires only 4-fold more steps to search. For three of the six cases with lengths greater than or equal to 18 residues, the SWA method achieved sub-Angstrom accuracy. Modeling with such accuracy included two 24-residue cases. One involved a mixture of irregular, helix, and strand segments in a bacteriophage head protein . Interestingly, in two of these cases (1arp and 1huw), KIC modeling outperformed SWA modeling in terms of RMSD but gave significantly worse Rosetta energies  and not released outside their research group. The closest previously solved structure exhibited low sequence identity to the target ; and analogs of the loop regions either did not exist (loop A) or were different in sequence at all 12 positions (loop B) or at 10 positions (loops C and D) in the homologous structure. The Weis group provided a starting structure with all of these loops and all side-chains removed, and ur cases , includiur cases . In threur cases .2fc3] based on threading with HHPRED and Rosetta 3v7e; As a fifth blind test, SWA models were generated for a loop of protein YbxF This article has presented a strategy for protein structure prediction that achieves atomic accuracy on the majority of loop modeling targets through a systematic all-atom enumeration. Several of these targets were difficult or intractable with prior approaches, despite mainly involving loops excised from crystallographic models, the simplest such puzzles. The main innovation herein is a stepwise ansatz imported from RNA structure modeling. This working hypothesis posits that realistic loop structures are reachable via the residue-by-residue building of partial conformations that are themselves well-stabilized by precise hydrogen bonds and non-polar packing interactions. This ansatz underlies a Rosetta stepwise assembly (SWA) protocol and is supported by tests of the SWA algorithm on forty loop puzzles, including twenty shared with prior loop modeling benchmarks, fifteen more difficult loop cases, and five blind tests. In the majority of cases (32 of 40), including loop puzzles of unprecedented length, all the blind tests, and a comparative model , the SWA method achieved sub-Angstrom accuracy.The stepwise assembly protocol is novel in protein modeling studies: while prior efforts have proposed the build-up of short peptides or lattice models An analogy to lock-picking helps clarify the strengths and limitations of the stepwise assembly method described herein. The main innovation of the protein SWA method is the enumerative sampling of individual residue conformations, which helps guarantee precise fit of the residues' atoms into the surrounding environment. This scenario is a kind of ‘lock-and-key’ problem but differs from the previous RNA SWA method To unlock a tumbler lock, one ‘enumeratively samples’ each pin conformation through manipulation with a probing pick, until the boundaries within the pin doublet are aligned with the lock's cylindrical surface . The pinThis analogy clarifies the importance of precise fits between the loop and its surroundings for successful modeling; if such a fit is not possible, the current SWA implementation will give inaccurate solutions. In particular, SWA will have difficulty in problems where the given protein backbone (outside the loop) deviates from its actual conformation. Such scenarios are encountered in comparative modeling and protein design applications. Cases like the community-wide blind trial YbxF above  demonstrN residues, the formal size of the conformational space scales exponentially with N; the actual experimental folding times of proteins do not scale so poorly, implying the general existence of folding intermediates or pathways rather than a random walk search N. The resulting efficiency permitted the atomic resolution recovery herein of loops with lengths up to 24 residues, whereas prior work tackled loops no longer than 12 residues  tool for uncovering deficiencies in the Rosetta energy function, as in the solvent-exposed loops described above  was implemented in C++ in the Rosetta codebase and is available in Rosetta release 3.6, free to academic users at de novo from residue k to residue l, each stage of stepwise assembly involved creating models of the loop with an N-terminal fragment built forward from k–1 to residue i and a C-terminal fragment built backward from residue l+1 to j. Each stage could thus be indexed with the two residue positions . The SWA calculation proceeded recursively from stages with short fragments built into the structure towards models with longer fragments, i.e., i increasing from k–1 or j decreasing from l+1. This building corresponds to movement from the top-right to the bottom or left, respectively, in A diagram of the entire stepwise assembly (SWA) calculation is given as a directed acyclic graph (DAG) laid out in the style of a dynamic programming matrix in k–1, l+1) of the DAG in packer. This pack optimized the rotamers using simulated annealing, after precomputing pairwise energies between all potential side-chain rotamers minimizerThe first step was a ‘pre-packing’ of the side-chains of the starting model with no loop atoms, corresponding to the top-right corner  to  (downward arrows in i and ψi) and the previous, adjacent residue , permitting the discovery of configurations in which the dipeptide segment is stabilized by interactions by the new residue without requiring interactions at the previous residue. . For each φ or ψ, the sampling was a grid search from –180° to 180° in 20° increments. To keep only sterically realistic backbones, configurations in which a residue's  gave Rosetta ramachandran score greater than 0.8 Rosetta units were discarded. The ω torsion was assumed to be 180° (trans configuration), except for residues that preceded prolines, which were also sampled at 0° (cis). The number of backbone combinations varied from tens to several thousand (for segments involving glycine residues and/or residues that preceded proline).The core computation in SWA is the addition of a new residue to a model and enumeration of its backbone conformations. For additions to the i and i–1) and their potential neighbors . The side-chain sampling was carried out with the Rosetta rotamer_trials algorithm.  The searched side-chain rotamers included those listed in the backbone-dependent Rosetta rotamer library as well as additional rotamers with χ1 and χ2 shifted by ±1 standard deviation from the standard rotamer values. The discreteness of the backbone grid search and rotamer library can penalize favorable side-chain interactions due to minor clashes or slightly imperfect hydrogen bonds. Therefore, the energy function for side-chain optimization was modified from the current standard Rosetta all-atom energy function (score12) to include a lower weight on fa_rep , a higher weight on hbond_sc , and no attenuation of hydrogen bond strength at solvent-exposed residues For each combination of backbone torsion angles, the side-chains of the loop and its surroundings were optimized. Analogous to calculations in protein-protein docking i–1 and i . If the RMSD value to any lower energy clusters was less than a fine cutoff (0.10 Å), the model was considered too close to an existing representative and discarded; otherwise the model seeded a new cluster. The lowest energy 400 models after clustering were carried forward to minimization.After enumerative backbone sampling and side-chain optimization, models were clustered as follows. In order of energy, starting with the lowest energy model, the RMSD of each model to all lower energy clusters was computed; this RMSD value was calculated over N, C, Cα, and O atoms at the rebuilt residues minimizerscore12. The models were clustered as described above, and saved to disk.Minimization involved backbone torsions  at the sampled residue and torsions χ for all neighboring side-chains . This torsional optimization was performed with the non-monotone Armijo variant of BFGS minimization in the Rosetta j to the C-terminal loop fragment  where 1< (j–I–1) <3].For the final stages of SWA assembly, N-terminal and C-terminal fragments were bridged to form continuous loops with ideal backbone bond lengths and angles , and thei+1 was appended to the N-terminal fragment, and other gap residues i+2 to j–1 were prepended to the C-terminal fragment. The φ and ψ torsion angles of the first gap residue i+1 were sampled by grid search as above; and, to attempt chain closure, backbone torsions for ‘bridge’ residues i+2 up to j–1 were subjected to 1000 cycles of cyclic coordinate descent , leading to hundreds of thousands of models. Even larger numbers were generated at the final stage of full-length loop modeling due to the many routes to chain closure. However, these models typically spanned a very large range of energies, and SWA seeks to carry forward only the lowest-energy configurations at each rebuild stage. Thus all models for a given stage were collated, filtered to retain the 4000 lowest energy models, and then reclustered. The clustering followed the procedure described above, except that RMSDs were calculated over the entire rebuilt loop fragments and a clustering RMSD threshold of 0.25 Å was applied. The 400 lowest energy configurations were carried forward. In the final stage (full-length loop models), models were re-clustered with RMSD threshold 1.0 Å, and the five lowest energy models were taken as the SWA predictions.For a given build-up stage (l+1) is coupled to the loop conformation through the torsion φl+1. This degree of freedom was not sampled, as ‘native’ backbone hydrogen atoms were present in the benchmark starting structures. However, for the five blind tests, the hydrogens were not available a priori; they were initially placed in the starting excised structure with Reduce l+1 was not guaranteed to be in its ‘native’ position, φl+1 was sampled during build-up of residues l–1 and l.In the SWA runs for this study's 35-loop benchmark, some settings in the loop modeling were chosen so as to match prior benchmarks. First, for proteins containing disulfide bonds, these residue-residue pairings were assumed to be known [as in prior work N) with the number of residues N, rather than as O (N2), and is analogous to the recursion used previously for RNA loop modeling N) calculation was carried out first. If the energy gap between the lowest energy model and the second lowest energy model was less than 1 kBT, the calculation was assumed to not have clearly converged on a confident model, and the loop building was repeated with the full O (N2) calculation, except the very long 1rhd and 7cat test cases. Overall, 18 of 40 cases were modeled with the O (N) calculation  that some cases could be solved without carrying out the full SWA dynamic programming matrix, but instead by building from the N-terminal side , by building in separate runs from the C-terminal side , and then combining these separate solutions with chain closure to attain final models. This simplified calculation  steps with the number of loop residues N (see above) are setup with the same swa_protein_dagman.py script above, but without the last flag –loop_force_Nsquared. For blind tests, the ψ torsion for the starting loop residue and φ torsion for the ending loop residue were sampled (see above); this was accomplished by omitting the flag -disable_sampling_of_loop_takeoff.Simplified calculations that take O and for post-processing all models available for a given stage by collation into single files and removal of unused files, are available in tools/SWA_protein_python/.Each of the build steps described in protein_build.dag corresponds to a single command line using the Rosetta executable swa_protein_main.An example command-line for pre-packing the 1OYC loop modeling case is:swa_protein_main.<exe> -database <path to database> -rebuild -out:file:silent_struct_type binary -fasta 1oyc.fasta -n_sample 18 -nstruct 400 -cluster:radius 0.100 -extrachi_cutoff 0 -ex1 -ex2 -score:weights score12.wts -pack_weights pack_no_hb_env_dep.wts -in:detect_disulf false -add_peptide_plane -native 1oyc_min.pdb -superimpose_res 1-202 215-399 -fixed_res 1-202 215-399 -calc_rms_res 203-214 -jump_res 1 399 -disable_sampling_of_loop_takeoff -mute all -s1 noloop_1oyc_min.pdb -input_res1 1-202 215-399 -use_packer_instead_of_rotamer_trials -out:file:silent REGION_215_202/START_FROM_START_PDB/region_215_202_sample.outAn example command line that builds residue 206 onto the end of a N-terminal fragment already containing 203–205 is:swa_protein_main.<exe> -database <database> -rebuild -out:file:silent_struct_type binary -fasta 1oyc.fasta -n_sample 18 -nstruct 400 -cluster:radius 0.100 -extrachi_cutoff 0 -ex1 -ex2 -score:weights score12.wts -pack_weights pack_no_hb_env_dep.wts -in:detect_disulf false -add_peptide_plane -native 1oyc_min.pdb -superimpose_res 1-202 215-399 -fixed_res 1-202 215-399 -calc_rms_res 203-214 -jump_res 1 399 -disable_sampling_of_loop_takeoff -mute all -silent1 region_215_205_sample.cluster.out -tags1 S_0 -input_res1 1-205 215-399 -sample_res 205 206 -out:file:silent REGION_215_206/START_FROM_REGION_215_205_DENOVO_S_0/region_215_206_sample.outHere, the build is onto the lowest energy model (S_0) available from a previous stage that had rebuilt residues 203–205 from the N-terminal end.An example command line that builds residue 209 onto the N-terminal end of a C-terminal fragment already containing residues 210–214:swa_protein_main.<exe> -database <path to database> -rebuild -out:file:silent_struct_type binary -fasta 1oyc.fasta -n_sample 18 -nstruct 400 -cluster:radius 0.100 -extrachi_cutoff 0 -ex1 -ex2 -score:weights score12.wts -pack_weights pack_no_hb_env_dep.wts -in:detect_disulf false -add_peptide_plane -native 1oyc_min.pdb -superimpose_res 1-202 215-399 -fixed_res 1-202 215-399 -calc_rms_res 203-214 -jump_res 1 399 -disable_sampling_of_loop_takeoff -mute all -silent1 region_210_202_sample.cluster.out -tags1 S_2 -input_res1 1-202 210-399 -sample_res 209 210 -out:file:silent REGION_209_202/START_FROM_REGION_210_202_DENOVO_S_2/region_209_202_sample.outHere, the build is onto the third lowest energy model (S_2) available from a previous stage that had rebuilt residues 210-214 from the C-terminal end.An example command line that takes the lowest energy model from the ensemble that has built up both the N-terminal fragment 203–206 and C-terminal fragment 209–215, samples backbone degrees of freedom at residue 207, and closes the chain by cyclic coordinate descent (CCD) across residues 207 and 208:swa_protein_main.<exe> -database <path to database> -rebuild -out:file:silent_struct_type binary -fasta 1oyc.fasta -n_sample 18 -nstruct 400 -cluster:radius 0.100 -extrachi_cutoff 0 -ex1 -ex2 -score:weights score12.wts -pack_weights pack_no_hb_env_dep.wts -in:detect_disulf false -add_peptide_plane -native 1oyc_min.pdb -superimpose_res 1-202 215-399 -fixed_res 1-202 215-399 -calc_rms_res 203-214 -jump_res 1 399 -disable_sampling_of_loop_takeoff -mute all -silent1 region_209_206_sample.cluster.out -tags1 S_0 -input_res1 1-206 209-399 -sample_res 208 -bridge_res 207 -cutpoint_closed 207 -ccd_close -global_optimize -out:file:silent REGION_207_206/START_FROM_REGION_209_206_CLOSE_LOOP_CCD_S_0/region_207_206_sample.outAn example command line that combines the lowest energy model for N-terminal fragment 203–206 with every model for C-terminal fragment 209–215  and carries out CCD (cyclic coordinate descent) chain closure across 207 and 208:swa_protein_main.<exe> –database <path to database> -rebuild -out:file:silent_struct_type binary -fasta 1oyc.fasta -n_sample 18 -nstruct 400 -cluster:radius 0.100 -extrachi_cutoff 0 -ex1 -ex2 -score:weights score12.wts -pack_weights pack_no_hb_env_dep.wts -in:detect_disulf false -add_peptide_plane -native 1oyc_min.pdb -superimpose_res 1-202 215-399 -fixed_res 1-202 215-399 -calc_rms_res 203-214 -jump_res 1 399 -disable_sampling_of_loop_takeoff -mute all -silent1 region_209_202_sample.cluster.out -tags1 S_0 -input_res1 1-202 209-399 -silent2 region_215_206_sample.cluster.out -input_res2 1-206 215-399 -bridge_res 207 208 -cutpoint_closed 206 -ccd_close -global_optimize -out:file:silent REGION_207_206/START_FROM_REGION_209_202_REGION_215_206_CLOSE_LOOP_CCD_S_0/region_207_206_sample.outAn example command line for clustering the lowest energy 4000 models available for the N-terminal fragment 203–205:swa_protein_main.<exe> -cluster_test -silent_read_through_errors -in:file:silent REGION_215_205/start_from_region_215_204_denovo_sample.low4000.out -in:file:silent_struct_type binary -database <path to database> -cluster:radius 0.25 -calc_rms_res 203-214 -out:file:silent region_215_205_sample.cluster.out -nstruct 400 -score_diff_cut 10.000 -working_res 1-205 215-399de novo models, with the same bond lengths and angles as used in the modeling.To help assess efficiency of conformational sampling, the all-atom Rosetta energies of crystallographic loops were obtained through two strategies. Generally, crystallographic loops contain minor steric clashes that are penalized by the Rosetta energy function, and these conformations need to be subjected to local optimization to permit comparison to idealize application), and the resulting idealized loop conformation was grafted into the same side-chain pre-packed structure as used in the SWA runs above. The loop and all its neighbors were subjected to combinatorial optimization by the packer and then all loop torsions and all side-chain torsions in the loop and surrounding residues were subjected to continuous minimization as above. As above, keeping the backbone outside the loop residue rigorously fixed requires a formal chainbreak within the loop, which remains closed during minimization due to the linear_chainbreak term. For each potential chainbreak location , 20 runs were carried out. The command line used was the following:The first ‘idealize-and-optimize’ strategy mimicked that from ref. swa_protein_main.<exe> –database <path to database> -rebuild -out:file:silent_struct_type binary -fasta 1oyc.fasta -n_sample 18 -nstruct 400 -extrachi_cutoff 0 -ex1 -ex2 -score:weights score12.wts -pack_weights pack_no_hb_env_dep.wts -in:detect_disulf false -add_peptide_plane -native 1oyc_min.pdb -superimpose_res 1-202 215-399 -fixed_res 1-202 215-399 -calc_rms_res 203-214 -jump_res 1 399 -disable_sampling_of_loop_takeoff -silent1 region_215_202_sample.cluster.out -tags S_0 -input_res1 1-202 215-399 -cutpoint_closed 214 -global_optimize -out:file:silent MINIMIZE_NATIVE/12/1oyc_minimize_native.out -cluster:radius 0.0 -s2 1oyc_min_idealize.pdb -input_res2 202-215 -slice_res2 202-215A second ‘native SWA’ strategy was used to optimize the loop conformation around the crystallographic loop. In this strategy, the entire SWA calculation after the initial side-chain prepacking was repeated, but at each sampling step, models were only carried forward if their backbone RMSD to the crystallographic loop was less than 2.0 Å. In addition, Rosetta coordinate constraints at loop Cα atoms were implemented with the following Python script command line:generate_CA_constraints.py 1oyc.pdb –cst_res 203-214 –coord_cst –anchor_res 1 –fade > 1oyc_coordinate2.0.cstThe script is available in tools/SWA_protein_python/. These constraints applied a penalty for each Cα atom deviating further than 2.0 Å from the crystallographic position, rising to a maximum of 10.0 kBT for deviations of 4.0 Å; the functional form was a cubic spline with zero derivative at 2.0 Å and 4.0 Å (the fade function in Rosetta). These constraints were activated in SWA runs by including flags -rmsd_screen 2.0 and -cst_file 1oyc_coordinate2.0.cst in swa_protein_main command lines above. In all tested loops, the SWA-native strategy gave models within 1.0 Å Cα RMSD to the crystallographic loop with lower energies than the idealize-and-optimize strategy ; the SWA-native values are thus reported in To permit comparison of the SWA approach to a prior state-of-the-art method, the KIC (kinematic closure) loop modeling method in Rosetta was repeated on the 20-protein PLOP/Rosetta benchmark. The following command line was used:loopmodel.<exe> -database <path to database> -loops:remodel perturb_kic -loops:refine refine_kic -loops:input_pdb region_FINAL.out.1.pdb -in:file:native 1oyc_min.pdb -loops:loop_file 1oyc.loop -loops:max_kic_build_attempts 10000 -in:file:fullatom -out:file:fullatom -out:prefix 1oyc -out:pdb -ex1 -ex2 -ex1aro -extrachi_cutoff 0 -out:nstruct 1000 -out:file:silent_struct_type binary -out:file:silent 1oyc_kic.out -fix_ca_bond_angles -kic_use_linear_chainbreak -allow_omega_move -sample_omega_at_pre_prolines.The command line is identical to that used previously, except for some additional terms to ensure a fair comparison to the SWA modeling above. The flag -fix_ca_bond_angles retains N–Cα–C bond angles at ideal values defined by Rosetta; sampling these angles did not improve accuracy in prior work The KIC loop modeling method requires an input starting structure with a loop pre-built, and this loop defines the fixed bond lengths and angles used in the run. Rather than using the crystallographic loops Figure S1Energy vs. RMSD plots at intermediate stages of stepwise assembly build-up. Build-up in panels (a) to (k) corresponds to residue-by-residue path in 1oyc loop (residues 203–214). RMSD over all backbone heavy-atoms  is shown on x-axis, using the corresponding loop fragments in the crystallographic loop as a reference. In each panel, symbol with black outline marks the specific model that eventually leads to the final lowest energy model in (k).(EPS)Click here for additional data file.Figure S2Energy vs. RMSD summaries of modeling runs for 20-loop PLOP/Rosetta benchmark. Rosetta all-atom energy values and loop Cα RMSDs are plotted for models from kinematic closure Monte Carlo ; stepwise assembly with O(N) simple calculation (red); stepwise assembly with full O(N2) build-up (pink); crystallographic loops optimized by SWA re-building with constraints (blue); and crystallographic loops optimized by idealizing, re-packing, and continuous minimization (green).(ZIP)Click here for additional data file.Figure S3Energy vs. RMSD summaries of modeling runs for 15 difficult and 5 blind test cases. Rosetta all-atom energy values and loop Cα RMSDs are plotted for models from models from kinematic closure Monte Carlo ; stepwise assembly with O(N) simple calculation (red); stepwise assembly with full O (N2) build-up (pink); crystallographic loops optimized by SWA re-building with constraints (blue); and crystallographic loops optimized by idealizing, re-packing, and continuous minimization (green). For 3v7e , optimized crystallographic energies are not presented, since loop building was carried out on a comparative model; and energies between KIC and SWA cannot be compared as RNA was not included in the former case.(ZIP)Click here for additional data file.Table S1Comparison of all loop modeling methods in 20-residue PLOP/Rosetta benchmark.(PDF)Click here for additional data file.Table S2Energy comparisons to determine convergence and conformational sampling efficiency.(PDF)Click here for additional data file.


It presently has 115 members. An interesting recent interaction is presented herewith.] serves science well, it does not serve philosophy well at all, for it is not forwarded in its line of enquiry in a fruitful way.But per se. I admire it as the highest form of intellectual enquiry. But it will not do for this highest form to look askance at the obvious that empirical science is producing.Richard, I have no quibbles with philosophy Let me put it a nutshell. Let’s try and answer these questionsPhilosophers are mainly discussing brain functions. How many of them have even a working knowledge of its structure - neuroanatomy, neurophysiology; leave alone its pathology?Tell me why is there no entry ‘brain’ in any encyclopaedia or dictionary of philosophy? [Correct me if I am wrong.]http://en.wikipedia.org/wiki/Brain] and more specifically ‘Human Brain’ [see http://en.wikipedia.org/wiki/Human_brain]. There are concepts here that may appear technical, but one can at least start; and what one doesn’t understand one can ask from scientific colleagues in the field of brain biology.How many have even tried to read the entry ‘brain’ in an encyclopaedia Tell me why is there no entry ‘brain’ in any encyclopaedia or dictionary of philosophy? [Correct me if I am wrong.][Have you found any that has an entry?]http://en.wikipedia.org/wiki/Brain] and more specifically ‘Human Brain’ [see http://en.wikipedia.org/wiki/Human_brain]. There are concepts here that may appear technical, but one can at least start; and what one doesn’t understand one can ask from scientific colleagues in the field of brain biology.How many [philosophers] have even tried to read the entry ‘brain’ in an encyclopaedia Care to opine?Have you made a New Year resolution you will study the brain?Happy New Year.Ajai31 Dec 2010__________________________________________________________________________________________________________________________<meta@rraz.net> wrote:On Fri, 31/12/10, Richard Godwin Sorry, but I still think you don’t make your point. Often discussions of brain functions are accompanied with brain areas or regions, such as primarily the pre-frontal cortex. But if not, then what difference does it make? The brain functions in such and such a way; what difference does it make to point out the pre-frontal cortex and associated regions, such as the amygdala and gray matter? Why should philosophers discuss the anatomy of the brain? If they don’t discuss brain structure, etc., that doesn’t mean they have no knowledge of it, or that they can’t answer questions specific to it. Pathology is important and is mentioned when appropriate, such as the cognitive effects of brain injuries.Almost all philosophers teach and write on “philosophy of mind.” That covers the brain, especially for physicalists. As to an encyclopaedia, why not look under “mind”, an overall subject including the brain? Or “biology”, or “physical”?Perhaps you can explain why any discussion of brain functions requires knowledge of the brain’s structure.Richard.__________________________________________________________________________________________________________________________From: Ajai Singh To: Mind_Brain_Consciousness@yahoogroups.comSent: Thursday, December 30, 2010 5:32 PMI think I replied to that, Richard.‘One may say one can very well do thinking without understanding the structure that causes it. I can very well drive a car without understanding its structure. But if I am describing the functions of an entity, I have to know its strucure as well, or at least have a working knowledge. If I am describing the functions of a car, I cannot claim any level of expertise unless I have at least a working knowledge of its structure.about thinking. Hence, they fall in both categories.‘Philosophers are not only thinking, they are also talking That is why I am urging my philosopher friends of the second category to get a working knowledge of the brain; and include that in their syllabi, their dictionaries and their encyclopaedias.’Happy New Year.Ajai1 Jan 2011.__________________________________________________________________________________________________________________________From Richard Godwin: meta@rraz.net Date: Sat, 1 Jan 2011 10:12:01 -0700Your car analogy doesn’t work quite well enough. With the car there are so many completely different elements, providing completely different functions (such as “engine” and “wheel”), that the analogy breaks down. There are several identified regions in the brain, and they work neuronal networks through chemical reactions toward any given function. I think philosophers do recognize different regions in the brain, but without scientific knowledge of precisely how those networks perform a task through which regions. But why should that matter? Simply the brain functions through networks of neurons with chemical impulses in different regions of the brain. Shouldn’t that be sufficient for the task of philosophy?Expertise in what? If you mean philosophers should be brain experts, then you might be right. But that is the provenance of science, not philosophy. You want a philosopher also to be a scientist, right? Why?Give us an example how poor philosophical reasoning is caused by lack of knowledge of the precise structure of the brain.I’m just trying to get at the root of your problem. So far, simply you have not made your case.Thank you,Richard.__________________________________________________________________________________________________________________________<vgaryali@hotmail.com> wrote:On Sun, 2/1/11, veena garyali I have been following this discussion with great interest. I am not a philosopher but do read a reasonable amount. I tend to agree with Richard. I really do not understand why Ajai is insisting that a philosopher should know the details of brain structure. The fact is that the essential importance of the brain is a given. There can be no thought, feeling or discussion without the brain and that too the human brain. Without being said it is understood. Philosophers focus on what comes after that- different world view, the meaning attributed to certain things, and so much else. Do they need to stipulate in the beginning that they know about the existence of the brain? It is like when you go to testify in the court both attorneys generally agree on the expert’s basic qualifications and they stipulate it without going into details. I am by no means an expert and may be I am missing some deeper meaning, but I fail to see the connection.Veena__________________________________________________________________________________________________________________________<mensanamonographs@yahoo.co.uk> Sunday, 2 January, 2011 20:34 wrote:Ajai Singh Why should philosophers study the brain?I am happy Richard and Veena are unhappy with what is presented until now.Because that lets me proceed with a few observations to clarify why I suggested what I did to my philosophers friends.Let us study a few concepts that have engaged philosophers. To put them in simple terms:Mind-body dualism states the mind is separate from the body.Descartes is credited with stating that mind and body, or consciousness and stuff, interact in the pineal gland.In the mind-body problem, the idealist view is that the mind alone is real.In the problem of personal identity, questions asked are: can a mind animate several bodies? Can several minds animate one body? Can mind exist without a body at all?In Indian thought, manas [roughly translated as mind] is regarded as an internal sense organ.And so on, and so forth.If one has a working knowledge of the brain structure and related functions, one realises that, with regard to each of the points above:Mind is a function of the brain, and brain is not at all separate from the body.The pineal gland is not responsible for any such interaction between physical and mental activity, or mind and body.The idealist view that mind alone is real is really not talking of mind as a function of the brain at all, but as some metaphysical entity. Because if they were to study the brain and its structure and related functions, they would immediately realise that the thought, ‘The mind alone is real’ is itself a product of their functioning brain, and they would rather say, ‘My brain functions are surely real, even if there may be doubt whether the external world is real or not’. If their brain were not functioning, they would not come to either conclusion, for whatever they are worth.What are we talking of as ‘mind’ here? If even an elementary study of brain is done, and if we accept mind to be a collection of brain functions, we would understand the ridiculous nature of these reflections with mind as the focus - the reflection is not at fault, the entity ‘mind’ as the focus is the culprit.Can a mind animate several bodies, or several minds animate one body? Manas or Mind as an internal sense organ in earlier Indian thought. An organ? If so, it must be present somewhere in the body? One thinks they meant the brain, which was understandably presented as an ‘internal sense organ’, given the extant nature of their understanding then.A clear understanding of brain structure and related functions makes many of these so called profound problems just evaporate into thin air. And saves precious energies to further more substantial enquiries in the field.That is why I recommend greater need to study the brain in all its nuances - not just its gross structure, but its function, its neurophysiology, its neurochemistry.In fact, if a philosopher/s were to sincerely study all these, and also empathetically study the different reflections on ‘mind’ and ‘consciousness’ that philosophers down the centuries have given, in the East and the West, and honestly co-relate them, he would be able to present the most comprehensive understanding of the topic, maybe even a final grand theory that settles matters, once and for all.Also, if a philosopher-scientist were to study and grasp the workings of the brain, and top it up with a comprehensive understanding of the great mass of knowledge that the great masters of thought - philosophers of the East and West - have bequeathed humanity, and co-relate them in a comprehensive manner, a similar grand theory that settles matters would result.When I urge my philosopher friends to study the brain, I do so because I believe they have greater potential to give such a theory. If they continue to live in denial, the scientists are on course, and may pip them to the post.And, of course, if a scientist-philosopher takes it up, and can get over his denial of philosophy because of their often ‘amorphous’ formulations, he may beat both the philosophers and the scientists to the post too.That is the game, that is the final goal, friends. We are playing for really large stakes.That is why I suggest philosophers plug that loophole in their study - neglecting the brain.And that is why I also now suggest scientists plug their loophole - neglecting the philosophy of mind.And if the two can synergise efforts, the goal can be achieved in half the time.Ready?Happy New Year once again. And kindly once again pardon the long post.Ajai2 Jan 2011Ajai Singh,mensanamonographs@yahoo.co.ukE-mail:


There was an error in the accession number in Materials and Methods.EBV and KSHV dataset can be found under the Study Accession number: ERP001026.Individual Sample Accessions are as follows:1208_JSC1_SN_KS - ERS0748231212_HBL6_SN_KS - ERS0748241210_JSC1_SN_EB - ERS0748251215_HBL6_SN_EB - ERS074833


An evaluation of the Bruker SMART X2S for the collection of crystallographic diffraction data, structure solution and refinement is carried out with a variety of materials with different electron densities, presenting some of the successes and challenges of automation in chemical crystallography. An evaluation of the Bruker SMART X2S for the collection of crystallographic diffraction data, structure solution and refinement is carried out with a variety of materials with different electron densities, presenting some of the successes and challenges of automation in chemical crystallography. For the third experiment, the crystal is sufficiently far from the centre of the mount that it is precessing in and out of the beam. Thus, the symmetry-equivalent reflections do not match, the Laue check fails and the larger centred unit cell is not identified. The checkCIF output highlights the missed symmetry, as well as the high R            int and final R values, and should alert the nonspecialist to the fact that there is a problem.The first two experiments have similar data, both of which are perfectly acceptable for publication: a slight increase in In summary, the large beam size means that alignment is not as critically important as on other instruments, particularly in the horizontal direction, although for short data collection times and good quality data, it is still important.3.N-cyano-S-benzyl-S-(2-fluorophenyl)sulfilimine, (2), synthesized , (4) and (11) are novel (see the supplementary information for their synthesis). Literature methods were used for the synthesis of (5) . The polarity of compound (9), as evidenced by the Flack –(14) an incorrect structure was obtained. The data quality seems fine, with no evidence of twinning or disorder, so what types of issues have occurred? The errors involve differentiation between atoms that differ by one electron, e.g. N and O atoms are reversed in (12) .Compounds (2)–(14) are anhydrous samples, without any solvent present. The effectiveness of the system for crystals containing more than one compound was investigated: a hydrate, a solvate and a cocrystal, (15)–(17)  and (17) were assigned correctly. For (16), a C and an N atom were misassigned, as discussed in §7.13H10OS, was incorrectly input as the sulfoxide, C13H10O2S, and (10), C15H12N2O, was input as C27H22N2O2S. The hydrate (16) and solvate (15) were input as the pure material. The software is robust and coped with an incorrect molecular formula in the majority of cases, with 80% of samples obtaining the correct structure.The effect of inputting the incorrect molecular formula was investigated, since it is necessary to input a formula at the start of the experiment, and in some cases the identity of the crystal may not be known. For example, (5), CThere is one issue that causes inconvenience. The system does not update the CIF and report files with the molecular formula based on the structure obtained, but instead uses the formula input by the user. This requires manual refinement for cases where the submitted formula is different from the structure obtained.8.Kα source for (2), (8), (11), (12), (16) and (18), which include samples of both good and poor crystal quality. The synthesis of (18), 4-methyl-N-phenylbenzenesulfonamide, has been described by Massah et al. etc. In our experience, those users who are familiar with the checkCIF output after an experienced crystallographer has finalized a crystal structure are asking more questions about the checkCIF output they obtain from the SMART X2S. Novice users are also asking similar questions and some of these questions are about the technique itself. This is a major advantage of the output from the instrument, in that it does seem to be increasing awareness of crystallography among the synthetic chemists.The checkCIF output allows fast diagnosis of any issues in the experiment. Inputting an incorrect formula at the start of the experiment, for example, will immediately become obvious from the checkCIF output because of differences in formula, density APEX2 software suite  than the rest of the data set (<5), owing to the beam stop blocking or partially blocking the correct measurement of these reflections in some orientations. This is not unusual for chemical crystallography and omitting these from the latter cycles of refinement would be useful, although it may be better not to do this in a routine manner.In some experiments, In summary, the SMART X2S as an instrument has allowed chemists with no crystallography experience to obtain crystallographic data for novel compounds. The instrument has greatly increased the use of crystallography in the department, with little training required to operate a user-friendly and easy-to-use instrument.supplementary information and have been deposited with the Cambridge Crystallographic Data Centre (CCDC) for the novel crystals (2)–(8), (11) and (14)–(18). [The following computer programs were used in the refinement: APEX2, GIS, SADABS and SAINT (Bruker (2009SHELXS97 and SHELXL97 (Sheldrick, 2008PLATON (Spek, 2009The CIF data (Hall & McMahon, 2006uker 2009, SHELXS910.1107/S0021889810042561/kk5074sup1.cif            Crystal structure: contains datablocks Compound_1_Below, Compound_1_Centre, Compound_1_Side, Compound_2_APEX, Compound_2_CCDC, Compound_2_Run_1, Compound_2_Run_2, Compound_2_Run_3, Compound_2_Run_4, Compound_3_CCDC, Compound_3_X2S, Compound_4_CCDC, Compound_4_X2S, Compound_5_CCDC, Compound_5_X2S, Compound_6_CCDC, Compound_6_X2S, Compound_7_CCDC, Compound_7_X2S, Compound_8_APEX, Compound_8_CCDC, Compound_8_X2S, Compound_9_X2S, Compound_10_X2S, Compound_11_APEX, Compound_11_CCDC, Compound_11_X2S, Compound_12_APEX, Compound_12_Manual, Compound_12_X2S, Compound_13_Manual, Compound_13_X2S, Compound_14_CCDC, Compound_14_Manual, Compound_14_X2S, Compound_15_CCDC, Compound_15_Manual, Compound_15_X2S, Compound_16_APEX, Compound_16_CCDC, Compound_16_Manual, Compound_16_X2S, Compound_17_CCDC, Compound_17_X2S, Compound_18_APEX, Compound_18_CCDC, Compound_18_Manual, Compound_18_X2S, global. DOI: 10.1107/S0021889810042561/kk50742_CCDCsup2.hkl            Structure factors: contains datablocks 2_CCDC. DOI: 10.1107/S0021889810042561/kk50743_CCDCsup3.hkl            Structure factors: contains datablocks 3_CCDC. DOI: 10.1107/S0021889810042561/kk50744_CCDCsup4.hkl            Structure factors: contains datablocks 4_CCDC. DOI: 10.1107/S0021889810042561/kk50745_CCDCsup5.hkl            Structure factors: contains datablocks 5_CCDC. DOI: 10.1107/S0021889810042561/kk50746_CCDCsup6.hkl            Structure factors: contains datablocks 6_CCDC. DOI: 10.1107/S0021889810042561/kk50747_CCDCsup7.hkl            Structure factors: contains datablocks 7_CCDC. DOI: 10.1107/S0021889810042561/kk50748_CCDCsup8.hkl            Structure factors: contains datablocks 8_CCDC. DOI: 10.1107/S0021889810042561/kk507411_CCDCsup9.hkl            Structure factors: contains datablocks 11_CCDC. DOI: 10.1107/S0021889810042561/kk507414_CCDCsup10.hkl            Structure factors: contains datablocks 14_CCDC. DOI: 10.1107/S0021889810042561/kk507415_CCDCsup11.hkl            Structure factors: contains datablocks 15_CCDC. DOI: 10.1107/S0021889810042561/kk507416_CCDCsup12.hkl            Structure factors: contains datablocks 16_CCDC. DOI: 10.1107/S0021889810042561/kk507417_CCDCsup13.hkl            Structure factors: contains datablocks 17_CCDC. DOI: 10.1107/S0021889810042561/kk507418_CCDCsup14.hkl            Structure factors: contains datablocks 18_CCDC. DOI: 10.1107/S0021889810042561/kk5074sup15.pdf            Supplementary material file. DOI: Figures, tables and supplementary information


A complete macromolecule modeling package must be able to solve the simplest structure prediction problems. Despite recent successes in high resolution structure modeling and design, the Rosetta software suite fares poorly on small protein and RNA puzzles, some as small as four residues. To illustrate these problems, this manuscript presents Rosetta results for four well-defined test cases: the 20-residue mini-protein Trp cage, an even smaller disulfide-stabilized conotoxin, the reactive loop of a serine protease inhibitor, and a UUCG RNA tetraloop. In contrast to previous Rosetta studies, several lines of evidence indicate that conformational sampling is not the major bottleneck in modeling these small systems. Instead, approximations and omissions in the Rosetta all-atom energy function currently preclude discriminating experimentally observed conformations from de novo models at atomic resolution. These molecular “puzzles” should serve as useful model systems for developers wishing to make foundational improvements to this powerful modeling suite. It may seem self-evident that one should start modeling with the very smallest known sequences that take on well-defined 3D structures. For several reasons, such a completely reductionist approach has not been the mainstream strategy in the Rosetta community. First, in early days, most cases of naturally occurring ultra-small proteins  were considered irregular and perhaps ill-defined, lacking the clear α or β secondary structure and hydrophobic cores that are the hallmarks of larger protein domains. Thus, initial Rosetta studies from the mid-1990s focused on 50- to 100- residue protein sequences that formed regular, clearly well-defined structures de novo modeling of short irregular loops and small proteins regularly appear as sub-problems in blind prediction targets and in the design of catalytic sites, conformationally switchable segments, and structured peptides. Predicting the structural features of these small systems and sub-systems – and even modeling the fine energetic balance between alternative structures – is no longer something to be avoided but instead a key goal of many Rosetta developers.These historical reasons to avoid small systems are no longer as relevant. First, since the late 1990s, several very small protein systems have been discovered or engineered and then clearly demonstrated to attain precisely defined 3D structures  greater than 3 kcal/mol.) Note that the focus herein will be on recovering high-resolution features of the experimental models; thus an acceptable Cα RMSD should be 1 Å or lower, comparable to the differences between structures solved in different crystallographic space groups or with different binding partners. Further, the puzzle descriptions include discussion of side-chain conformations deemed experimentally stable and important for each molecule's fold and function.Each of the selected systems has been extensively characterized by numerous experimental structural and energetic methods. In particular, in each case, the free energy associated with the experimental conformation has been measured to be at least 3 kcal/mol more stable than the ensemble of unstructured states at room temperature.  community. The Trp cage is a particularly well-characterized mini-protein with a length of 20 residues, engineered by truncating and optimizing exendin-4 from gila monster saliva de novo modeling, on the other hand, fails to solve this problem. While occasionally sampling a near-native conformation, Rosetta's fragment-assembly/all-atom-refinement protocol (“abrelax”) favors a tight cluster of structures with a backbone within 2 Å Cα RMSD of the native conformation but with the molecule's central tryptophan side-chain in an incorrect rotamer  mini-proteins. The α-conotoxin GI, isolated from the fish-hunting marine snail de novo modeling (abrelax) gives low energy models that disagree with the crystal structure in all non-helical regions (not shown). The Stepwise Assembly algorithm yields even lower energy models that are still highly discrepant  strategies versus StepWise protocols.]Excision of this segment and subsequent Rosetta de novo.  than the optimized native models. Thus, at least for these four small puzzles, it is the poor discrimination of the Rosetta all-atom energy function that emerges as the critical problem. In previous studies, the difficulty of conformational sampling for larger problems as well the greater energy gaps attained in those problems likely masked flaws in the Rosetta energy function , these efforts have led to few substantial improvements in benchmarks or actual changes in the main codebase. One potential barrier is that the physics of solvation, H-bonds, and screened electrostatic interactions are strongly coupled to each other, and indeed are reflected in partly unified terms in most other molecular modeling force fields ; this code will be gladly provided to other academic users upon request.We summarize sequences and Rosetta command-lines for each of the four puzzles herein. Most of the calculations in the paper were carried out with Rosetta release 3.2; and all calculations will be implemented in the next Rosetta release. Remaining models were generated with the Rosetta codebase in the Das lab branch (revision number 40197), available to Rosetta developers  did not give significantly lower energies. Both of these standard command lines are also executable with Rosetta release 3.2.A total of 20,000 models were generated for both i,j] of the target sequence onto clustered conformational ensembles  derived from subfragments  and . Each “step” involves exhaustively sampling φ/ψ in 20° increments, repacking side-chains, and minimizing. An example command-line for the step building subfragment Independent modeling runs were carried out with a novel StepWise Assembly (SWA) method. A full benchmark of this method is under preparation. Briefly, the method recursively builds each subfragment [stepwise_protein_test.<exe> -database <path to rosetta_database> -rebuild -out:file:silent_struct_type binary -fasta 2jof.fasta -n_sample 18 -nstruct 100 -cluster:radius 0.100 -extrachi_cutoff 0 -ex1 -ex2 -score:weights score12.wts -pack_weightspack_no_hb_env_dep.wts -add_peptide_plane -native 2jof.pdb -mute all -silent1 region_4_5_sample.cluster.out -tags1 S_0 -input_res1 4 5 -sample_res 3 4 -out:file:silent REGION_3_5/START_FROM_REGION_4_5_DENOVO_S_0/region_3_5_sample.out [SWA]2 resource. Optimized native conformations were also estimated with the StepWise Assembly method. To ensure a fair comparison, the entire calculation was repeated, but using Rosetta atom-pair constraints (with the Rosetta smoothed step function “fade”) to keep models with inter-residue Cα-Cα distances within ±1 Å and the tryptophan rotamer in the native conformation. Explicitly, an example command line is:A complete directed acyclic graph (DAG) of the rebuild and clustering steps, along with associated commands in Condor format, was automatically generated by a master Python script. The script and a resulting example DAG are provided via Rosetta protocol capture (see below). The DAG was computed via DAGMAN with the Condor computing platform or with in-house Python scripts  on the LSF queuing platform on 200 to 400 cores on Stanford's BioXstepwise_protein_test.<exe> -database <path to rosetta_database> -rebuild -out:file:silent_struct_type binary -fasta 2jof.fasta -n_sample 18 -nstruct 100 -cluster:radius 0.100 -extrachi_cutoff 0 -ex1 -ex2 -score:weights score12.wts -pack_weightspack_no_hb_env_dep.wts -add_peptide_plane -native 2jof.pdb -mute all -silent1 region_4_5_sample.cluster.out -tags1 S_0 -input_res1 4 5 -sample_res 3 4 -out:file:silent REGION_3_5/START_FROM_REGION_4_5_DENOVO_S_0/region_3_5_sample.out-cst_file 2jof_native_CA_CA_trp.cst [SWA NATIVE]2jof_native_CA_CA_trp.cst is provided by Rosetta protocol capture.and the constraint file ECCNPACGRHYSC. The methods for de novo modeling α-conotoxin were essentially the same as for Trp cage. However, the following command lines need to be run from the Das lab branch, which disables complications in disulfide input/output and scoring in the Rosetta release 3.2.The modeled sequence was: AbinitioRelax.<exe> -database <path to rosetta_database> -fasta 1not_.fasta -frag3 aa1not_03_05.200_v1_3 -frag9 aa1not_09_05.200_v1_3 -out:file:silent 1not_abrelax_CST_increase_cycles_no_hb_env_dep.out -out:file:silent_struct_type binary -nstruct 400 -cst_file 1not_native_disulf_CEN.cst -abinitio:relax -cst_fa_file 1not_native_disulf.cst -native 1not.pdb -increase_cycles 10 -score:weights score12_no_hb_env_dep.wts -ex1 -ex2 -extrachi_cutoff 0 [ABRELAX]andrelax.<exe> -database <path to rosetta_database>-s idealize_1not.pdb -fasta 1not_.fasta -frag3 aa1not_03_05.200_v1_3 -frag9 aa1not_09_05.200_v1_3 -out:file:silent 1not_native_relax.out -out:file:silent_struct_type binary -nstruct 200 -abinitio:relax -cst_fa_file 1not_native_disulf.cst -native 1not.pdb -increase_cycles 10 -score:weights score12.wts -ex1 -ex2 -extrachi_cutoff 0 [NATIVE RELAX]1not_native_disulf.cst; see protocol capture) enforces near-native disulfide bond lengths and angles between residue pairs  and ; Rosetta atom-pair constraints are defined, penalizing Sγ–Sγ distances outside 1.5–2.5 Å and inter-residue Sγ-Cβ distances outside 2.5–3.5 Å. For both command-lines, 20,000 models were generated.The constraint file  from subfragment  are:stepwise_protein_test.<exe> -database <path to rosetta_database> -rebuild -out:file:silent_struct_type binary -fasta 1not.fasta -n_sample 18 -nstruct 100 -cluster:radius 0.100 -extrachi_cutoff 0 -ex1 -ex2 -score:weights score12.wts -pack_weightspack_no_hb_env_dep.wts -add_peptide_plane -cst_file 1not_native_disulf.cst -native 1not.pdb -mute all -silent1 region_4_5_sample.cluster.out -tags1 S_0 -input_res1 4 5 -sample_res 3 4 -out:file:silent REGION_3_5/START_FROM_REGION_4_5_DENOVO_S_0/region_3_5_sample.out [SWA]stepwise_protein_test.<exe> -database <path to rosetta_database>-rebuild -out:file:silent_struct_type binary -fasta 1not.fasta -n_sample 18 -nstruct 100 -cluster:radius 0.100 -extrachi_cutoff 0 -ex1 -ex2 -score:weights score12.wts -pack_weightspack_no_hb_env_dep.wts -add_peptide_plane -cst_file 1not_native_disulf_CA_CA.cst -native 1not.pdb -mute all -silent1 region_4_5_sample.cluster.out -tags1 S_0 -input_res1 4 5 -sample_res 3 4 -out:file:silent REGION_3_5/START_FROM_REGION_4_5_DENOVO_S_0/region_3_5_sample.out[SWA NATIVE]VGTIVTMEYRIDRVRLFVDKLDNIAEVPRVG . The Rosetta command-line made use of a recent loop modeling that leverages kinematic loop closure The chymotrypsin inhibitor sequence was the 62-residue truncated sequence from barley seeds: TEWPELVGKSVEEAKKVILQDKPEAQIIVLPloopmodel.<exe> -database <path to rosetta_database> -loops:remodelperturb_kic -loops:refinerefine_kic -loops:input_pdb 2ci2_min.pdb -in:file:native 2ci2.pdb -loops:loop_file 2ci2_35_45.loop -loops:max_kic_build_attempts 10000 -in:file:fullatom -out:file:fullatom -out:prefix 2ci2 -out:pdb -ex1 -ex2 –extrachi_cutoff 0 -out:nstruct 200 -out:file:silent_struct_type binary -out:file:silent 2ci2_kic_loop35_45.out[KIC]10,000 KIC models were generated. Output files were rescored to generate RMSDs over just the rebuilt loops, using the command line:score.<exe> -database <path to rosetta_database>-in:file:silent 2ci2_kic_loop35_45.out -native 2ci2.pdb -out:file:scorefile 2ci2_kic_loop35_45.recalculate_rmsd.sc -in:file:silent_struct_type binary -in:file:fullatom -native_exclude_res 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62The optimized native conformation (2ci2_min.pdb) was generated by packing and minimizing side-chains, as described in StepWise Assembly methods were also applied to this case, but could not be directly compared to the KIC results because of difference in which degrees of freedom were optimized . gcuucggc. (Lower-case letters refer to nucleic acids in Rosetta.)The eight-nucleotide modeled RNA sequence, derived from residues 31–38 of a ribosomal fragment (PDB: 1F7Y), was:Fragment Assembly of RNA with Full-Atom Refinement rna_denovo.<exe> -random_delay 20 -database <path to rosetta_database> -fastagcuucggc.fasta -nstruct 200 -out::file::silent gcuucggc.out -minimize_rna -cycles 5000 -mute all -native gcuucggc_RNA.pdb [FARFAR]Optimized native conformations used a similar command line but drew fragments only from the crystallographic model that was the source of the puzzle:rna_denovo.<exe> -random_delay 20 -database <path to rosetta_database>-fastagcuucggc.fasta -nstruct 200 -out::file::silent gcuucggc_NATIVE.out -minimize_rna -cycles 5000 -mute all -native gcuucggc_RNA.pdb -vall_torsions 1f7y_native.torsions [FARFAR NATIVE]In both cases, 20,000 FARFAR models were generated. The native torsion file was generated by:rna_database.<exe> -database <path to rosetta_database>-s 1f7y_RNA.pdb -vall_torsions -o 1f7y_native.torsionsi,j] of the target sequence are modeled from clustered conformational ensembles for subfragments  and  in a recursive manner. Single-residues are enumeratively sampled  in 20° increments, repacking 2′-OH groups, and minimizing. Here, an ideal Watson-Crick stem was assumed for residues 1–2 and 7–8, and the UUCG loop 3–6 was rebuilt from both ends and connected by CCD loop closure. An example command-line for the basic rebuild step building residue 3 onto the starting stem in either de novo and native-optimization runs are:For RNA modeling cases, StepWise Assembly provides a more efficient sampling method . Analogous to protein cases (A) and (B), sub-fragments [rna_swa_test.<exe> -algorithm rna_resample_test -database <path to rosetta_database> -fastagcuucggc.fasta -output_virtual -cluster:radius 0.100 -num_pose_kept 100 -score:weights rna_hires_2008.wts -native motif2_1f7y_RNA.pdb -rna_torsion_potential rd2008 -s1 gcgc.pdb -input_res1 1 2 7 8 -out:file:silent REGION_0_1/START_FROM_REGION_0_0/region_0_1_sample.out -sample_res 3[SWA]rna_swa_test.<exe> -algorithm rna_resample_test -database <path to rosetta_database> -fastagcuucggc.fasta -output_virtual -cluster:radius 0.100 -num_pose_kept 100 -score:weights rna_hires_2008.wts -native motif2_1f7y_RNA.pdb -cst_fileuucg_polar_fade.cst -sampler_native_rmsd_screen -sampler_native_rmsd_screen_cutoff 1.500 -rna_torsion_potential rd2008 -s1 gcgc.pdb -input_res1 1 2 7 8 -out:file:silent REGION_0_1/START_FROM_REGION_0_0/region_0_1_sample.out -sample_res 3[SWA NATIVE]In the latter command-line, a constraint file penalizes conformations in which contacting (within 4 Å) polar heavy atoms are placed beyond 1 Å from their native distances; the file is provided in the protocol capture (see next).https://svn.rosettacommons.org/source/trunk/RosettaCon2010/protocol_capture/rhiju_four_small_puzzles.All files, including fragments, sequence files (.fasta), native conformations (.pdb), as well as example logs are being provided via “protocol capture” in the Rosetta Subversion repository: The directory will be gladly provided to readers without access to the repository upon request.


To the Editor: Kala-azar, or visceral leishmaniasis, is a parasitic disease that leads to fever, anemia, and hepatosplenomegaly. Death is the usual outcome when infection is not treated. The majority of infections are caused by the protozoan Leishmania donovani, restricted to India and eastern Africa, but the most widespread are caused by L. infantum, found from People’s Republic of China to the New World, where it infects humans, dogs, and wild canids. All Mediterranean countries are affected by L. infantum, where most patients are co-infected with HIV. Several species of sand flies transmit the disease , possibly by insect eggs or larvae being carried in organic matter.During the 1980s, urban transmission of kala-azar became a major problem in Brazil. More than 3,000 cases are reported annually, and the disease has spread from northeastern Brazil westward to the Amazon region, as well as to the industrialized southeast. Several as yet unproven explanations for the urbanization of kala-azar in Brazil have been proposed  are only 3–4°C lower than those of São Borja, Rio Grande do Sul state, the southernmost city where L. longipalpis transmits kala-azar, and even warmer than Chajarí, Argentina , at the highest southern latitude where this vector is found (Kala-azar has now reached the temperate Brazilian south and Argentina. This spread of the disease warns us of the danger of introduction in other temperate areas. Europe is particularly vulnerable because of the existing natural transmission of orizonte , BraziliL. longipalpis sand flies in Lisbon, the situation could change dramatically, and kala-azar might become a major urban disease in Europe. The International Health Regulations recommends disinfection of aircraft by preflight and blocks-away spraying with pyrethroids (L. longipalpis sand flies was recently described in Brazil (L. longipalpis sand flies, such as minimum temperature tolerance, mechanisms of urban spread, presence in aircraft, and role in inducing more severe disease._______________________________________________________________________________________________________________________________Human kala-azar is less common in Europe, possibly because sand flies there are less anthropophilic. If aircraft introduce anthropophilic


In Page 6, right column, second paragraph, COG1517 should be COG1571.HVO_2477 should be HVO_2744 and HVO_B0354 should be HVO_B0357.In Table  1, page 7, In Page 8, right column, end of last paragraph, HVO_2477 should be HVO_2744.HVO_B0354 should read VDC2399 H26 ΔHVO_B0357. In Supplemental Table  1, the entry VDC2399 H26 ΔHVO_2477 entries should be HVO_2744, and all HVO_B0354 entries should be HVO_B0357.In Supplemental Table  4, all These corrections do not influence the overall conclusions of this study.


Computational design of protein function involves a search for amino acids with the lowest energy subject to a set of constraints specifying function. In many cases a set of natural protein backbone structures, or “scaffolds”, are searched to find regions where functional sites  can be placed, and the identities of the surrounding amino acids are optimized to satisfy functional constraints. Input native protein structures almost invariably have regions that score very poorly with the design force field, and any design based on these unmodified structures may result in mutations away from the native sequence solely as a result of the energetic strain. Because the input structure is already a stable protein, it is desirable to keep the total number of mutations to a minimum and to avoid mutations resulting from poorly-scoring input structures. Here we describe a protocol using cycles of minimization with combined backbone/sidechain restraints that is Pareto-optimal with respect to RMSD to the native structure and energetic strain reduction. The protocol should be broadly useful in the preparation of scaffold libraries for functional site design. There has been recent progress in the computational design of functional proteins and in the prediction of biomolecular interactions across a wide range of problems: ligand-protein Most crystal structures will have regions of high energy as evaluated in Rosetta or other design programs, which will lead to sequence changes in design if they are not addressed. However most minimization protocols will lead to too much deviation from the original wild-type crystal structure. The question is how to properly balance energy minimization with reduction of structural deviation from the starting structure. The concept of optimizing a structure to the energy function in use has a long precedent – for example the equilibration of structures in molecular dynamics prior to a production run Here we carry out a systematic examination of a number of structure refinement methods, evaluating them for optimality (minimization of Rosetta energy and RMSD from the starting structure simultaneously) and for their influence on subsequent sequence re-design. We found that a combination of harmonic backbone and sidechain coordinate restraints minimizes all-atom RMSD and Rosetta energy together, and reduces the number of sequence changes in subsequent design. Because the restrained relax protocol examined here results in fewer sequence alterations in design, it also minimizes the amount of human intervention required in the overall design process.primum non nocere, or “first do no harm”) while also minimizing the Rosetta energy. One possibility in preparing structures for Rosetta is to re-refine the structure using an energy function incorporating standard Rosetta score terms as well as the correspondence to electron density Our goal in evaluating structure minimization protocols was first to minimize RMSD to the native  to understand energetic changes effected by the protocol at a residue-by-residue level. The high-energy residues about 5 REU are all moved to lower energy by the protocol; residues in the 1–5 REU range are largely moved to lower energy; residues with negative energy remain mostly unchanged . The design protocol on the native places a glutamate at the position 14, in place of native tyrosine . Briefly, these structures were prepared for Rosetta by removal of waters and non-canonical amino acids and preparation of Rosetta parameter files for the ligands. Structure selection is described fully in Nivón and Bjelic.The 41 protein monomer sequence recovery set has been described elsewhere min on one core of a mixed Intel-Xeon-L5335-2GHz/AMD-Opteron-2.2GHz cluster and roughly doubling in time for each additional 100 residues.The fast relax protocol in Rosetta is described elsewhere Harmonic coordinate restraints take the form f(x)  =  (d/sd)?2, where d is the distance of the atom from the desired coordinate, and sd is a parameter related to the strength of the restraint. Bounded coordinate restraints take a zero value within WIDTH of the desired coordinate, followed by a small harmonic segment to transition (from WIDTH to WIDTH +0.5*sd) and a linear value of slope set at 1/sd for the rest. Sidechain-sidechain restraint runs have harmonic distance restraints of sd = 2.0 for all sidechain atom pairs within the specified cutoff distance, and backbone heavy atom harmonic coordinate restraints of sd = 0.5.Parameter scans were performed with the flags:“-no_optH false -flip_HNQ -use_input_sc -correct -no_his_his_pairE -linmem_ig 10 -nblist_autoupdate true”Harmonic runs added flags “-constrain_relax_to_start_coords -relax:ramp_constraints false -relax:coord_constrain_sidechains -relax:coord_cst_stdev <SD>”, where SD ranged from 0.000001 to 5.0Bounded runs added flags “-constrain_relax_to_start_coords -relax:ramp_constraints false -relax:coord_constrain_sidechains -relax:coord_cst_stdev <SD> -relax:coord_cst_width <WIDTH>”, where SD ranged from 0.1 to 5.0, and WIDTH ranged from 0 to 1.0.Sidechain-sidechain runs added flags “-constrain_relax_to_start_coords-relax:ramp_constraints false -relax:sc_cst_maxdist <DIST>” where DIST ranged from 3 to 8.Unrestrained runs had no additional flags.RMSD values were computed in pymol with the command “align relaxed and not hydro, reference and not hydro, cycles = 0”. The median per-residue energy and RMSD of 10 replicates of the relax procedure were taken as the value for that protein, and the mean value across the 51 proteins in the input set were computed for each of the different parameter runs.Design was performed with the Rosetta enzyme design program, using the default parameters from the enzdes scientific test in rosetta (rosetta/rosetta_tests/scientific/biweekly/tests/enzdes_benchmark). Briefly, a design shell of 6 Å, with a 12 Å shell of repackable  residues was used. One cycle of design with a “soft” Lennard Jones repulsive term was followed by one with the standard repulsive term. Extra rotamers were included based on the Dunbrack rotamer distribution, and recent updates to the Rosetta score function were also included –l./inputs/pdb.list–enzdes::detect_design_interface–enzdes::cut1 6.0–enzdes::cut2 8.0–enzdes::cut3 10.0–enzdes::cut4 12.0–enzdes::cst_design–enzdes::design_min_cycles 2–enzdes::cst_min–enzdes::chi_min–ex1–ex2–ex1aro–ex2aro–extrachi_cutoff 1–soft_rep_design–flip_HNQ–correct–no_his_his_pairE–score::hbond_params correct_params–lj_hbond_hdis 1.75–lj_hbond_OH_donor_dis 2.6–dun08 false–nstruct 1–enzdes::no_unconstrained_repack–linmem_ig 10–nblist_autoupdate true–enzdes::lig_packer_weight 1.8–docking::ligand::old_estat true–extra_res_fa inputs/2b3b.params inputs/2ifb.params inputs/1sw1.params inputs/2FQX.params inputs/2p0d.params inputs/2DRI.params inputs/1fby.params inputs/1ZHX.params inputs/2RDE.params inputs/1db1.params inputs/2h6b.params inputs/1z17.params inputs/2FME.params inputs/1y3n.params inputs/1urg.params inputs/1FZQ.params inputs/1y52.params inputs/1POT.params inputs/1XT8.params inputs/2FR3.params inputs/2UYI.params inputs/1USK.params inputs/1n4h.params inputs/2qo4.params inputs/2GM1.params inputs/2rct.params inputs/2HZQ.params inputs/1hsl.params inputs/1A99.params inputs/1uw1.params inputs/1l8b.params inputs/3B50.params inputs/1H6H.params inputs/2Q2Y.params inputs/1hmr.params inputs/1OPB.params inputs/1x7r.params inputs/2Q89.params inputs/1nl5.params inputs/1TYR.params inputs/2e2r.params inputs/1LKE.params inputs/2PFY.params inputs/1wdn.params inputs/1nq7.params inputs/1y2u.params inputs/2ioy.params inputs/1J6Z.params inputs/1RBP.params inputs/1XZX.params inputs/2f5t.params–chemical:exclude_patches LowerDNA UpperDNA Cterm_amidation SpecialRotamerVirtualBB ShoveBB VirtualDNAPhosphate VirtualNTerm CTermConnect sc_orbitalspro_hydroxylated_case1 pro_hydroxylated_case2 ser_phosphorylatedthr_phosphorylated tyr_phosphorylated tyr_sulfated lys_dimethylatedlys_monomethylated lys_trimethylated lys_acetylated glu_carboxylatedcys_acetylated tyr_diiodinated N_acetylated C_methylamidatedMethylatedProteinCtermFile S1Supporting figures and tables. Table S1. Energy comparison between input and restrained-relax structures. Average over all energy terms for the input set (INPUT) and the set relaxed with sidechain coordinate restraints at sd = 0.5 (COORD). Values are sorted by the difference in each score term. Energy terms fa_dun (sidechain rotamer probability-based energy) and fa_rep (Lennard-Jones repulsive term) are the largest contributors to lower energy after the relax. Table S2. The top-10 most-improved residues after the relax protocol for 1A99. Score term changes are shown for fa_dun and fa_rep score terms only. INPUT is for the original wild-type crystal structure. COORD is for the coordinate restrained relax structures. Delta is the difference between INPUT and COORD for the specified score term and residue. Table S3. Residue 14 energies for 2ifb.pdb before/after restrained relax and before/after design. Note that after the restrained relax the total energy for residue 14 (−1.17) is much lower, even slightly lower than the energy after enzyme design on an unrelaxed input structure (−1.14). Figure S1. Per-residue score comparison between an input and restrained-relax structure. Histogram of individual residue Rosetta scores for the putrescine receptor (1A99.pdb) unmodified (blue) and after the coordinate-restrained relax protocol (red), with overlap in purple. Figure S2. Example of improved design after restrained relax. 2ifb position 14 example, native in green, design on the unmodified native in cyan, and design on the restrained-relax native in purple. The wild-type identity at position 14 is mutated from tyrosine to glutamate in the run on an unmodified structure (cyan), but remains the original tyrosine in the run on the restrained-relax structure (purple).(DOCX)Click here for additional data file.


The purpose of the presented study was to trace orthologs of btd in other insects and reconstruct the evolutionary history of the Sp genes within the metazoa.The Sp-family of transcription factors are evolutionarily conserved zinc finger proteins present in many animal species. The orthology of the Sp genes in different animals is unclear and their evolutionary history is therefore controversially discussed. This is especially the case for the Sp gene Tribolium castaneum), a hemimetabolous insect (Oncopeltus fasciatus), primitively wingless hexapods (Folsomia candida and Thermobia domestica), and an amphipod crustacean . We supplemented this data set with data from fully sequenced animal genomes. We performed phylogenetic sequence analysis with the result that all Sp factors fall into three monophyletic clades. These clades are also supported by protein domain structure, gene expression, and chromosomal location. We show that clear orthologs of the D. melanogaster btd gene are present even in the basal insects, and that the Sp5-related genes in the genome sequence of several deuterostomes and the basal metazoans Trichoplax adhaerens and Nematostella vectensis are also orthologs of btd.We isolated Sp genes from representatives of a holometabolous insect (Sp5/btd ortholog, which strongly suggests that btd is not the result of a recent gene duplication, but directly traces back to an ancestral gene already present in the metazoan ancestor.All available data provide strong evidence for an ancestral cluster of three Sp-family genes as well as synteny of this Sp cluster and the Hox cluster. The ancestral Sp gene cluster already contained a  Sp1 have been identified in the human genome, and homologous genes have been isolated from several other animal species as well  codes for a member of the Sp-family, which represents an important factor for the formation of several head segments and is also involved in the development of the central and peripheral nervous system , Td_Sp1-4 [EMBL: FN562988], Td_Sp5/btd [EMBL: FN562989], Td_Sp6-9 [EMBL: FN562990], Fc_Sp1-4 [EMBL: FN562985], Fc_Sp5/btd [EMBL: FN562986], Fc_Sp6-9 [EMBL: FN562987], Ph_Sp1-4 [EMBL: FN562991], Ph_Sp6-9 [EMBL: FN562992]. BLAST analysis was used to identify the Sp1-4 homologue of D. melanogaster and T. castaneum. Gene specific primers were made to amplify Tc_btd [GenBank: NM_001114320.1], Tc_Sp8 [GenBank: NM_001039420] and Tc_Sp1-4 [GenBank: XM_967159] from T. castaneum cDNA, as well as Dm_btd [GenBank: NM_078545], Dm_D-Sp1 [GenBank: NM_132351] and Dm_CG5669 (Sp1-4) [GenBank: NM_142975] from D. melanogaster cDNA. The sequences of these primers are given in Additional File H. sapiens (Genome Reference Consortium Human Build 37 (GRCh37), Primary_Assembly) , Dm_Btd [GenBank: NP_511100], Dm_D-Sp1 [GenBank: NP_572579], Dps_GA19045 [GenBank: XP_001358829], Dps_GA22354 [GenBank: XP_002134535], Dps_GA12282 [GenBank: XP_001354397], Ag_Sp1-4 [GenBank: NZ_AAAB02008898], Ag_Sp5/Btd [GenBank: NZ_AAAB02008847], Ag Sp6-9 [GenBank: NZ_AAAB01008847]; Nav_Sp1-4 [GenBank: XP_001599101], Nav_Sp5/Btd [GenBank: AAZX01008599], Nav_Sp6-9 [GenBank: XP_001606079], Am_Sp1-4 [GenBank: XP_624316.2], Am_Sp5/Btd [GenBank: XP_001119912], Am_Sp6-9 [GenBank: XP_624528], Bm_Sp1-4 [GenBank: BABH01010251], Bm_Sp5/Btd [GenBank: BABH01024462], Bm_Sp6-9 [GenBank: AADK01002198], Tc_Sp1-4 [GenBank: XP_972252], Tc_Btd [GenBank: NP_001107792], Tc_Sp8 [GenBank: NP_001034509], Of_Sp8/9 [EMBL: FN396612], Nv_Sp1-4 [GenBank: XP_001635004], Nv_Sp5/Btd [GenBank: XP_001635002], Nv_Sp6-9 [GenBank: XP_001634948], Sp_Sp1-4 [GenBank: XR_025838], Sp_Sp5/Btd [GenBank: XP_789110.1], Sp_Sp6-9 [GenBank: XP_793203.2], Hs_Sp1 [GenBank: NP_612482], Hs_Sp2 [GenBank: NP_003101], Hs_Sp3 [GenBank: NP_003102], Hs_Sp4 [GenBank: NP_003103], Hs_Sp5 [GenBank: NP_001003845], Hs_Sp6 [GenBank: NP_954871], Hs_Sp7 [GenBank: NP_690599], Hs_Sp8 [GenBank: NP_874359], Hs_Sp9 [GenBank: NP_001138722], Mm_Sp1 [GenBank: NP_038700], Mm_Sp2 [GenBank: NP_084496], Mm_Sp3 [GenBank: NP_035580], Mm_Sp4 [GenBank: NP_033265], Mm_Sp5 [GenBank: NP_071880], Mm_Sp6 [GenBank: NP_112460], Mm_Sp7 [GenBank: NP_569725], Mm_Sp8 [GenBank: NP_796056], Mm_Sp9 [GenBank: NP_001005343], Dr_Sp1 [GenBank: NP_997827], Dr_Sp2 [GenBank: NP_001093452], Dr_Sp3 [GenBank: NP_001082967], Dr_Sp3-like [GenBank: XP_691096], Dr_Sp4 [GenBank: NP_956418], Dr_Sp5 [GenBank: NP_851304], Dr_Sp5-like [GenBank: NP_919352], Dr_Similar_to_Sp5 [GenBank: XP_001335730], Dr_Sp6 [GenBank: NP_991195], Dr_Sp7 [GenBank: NP_998028], Dr_Sp8 [GenBank: NP_998406], Dr_Sp8-like [GenBank: NP_991113], Dr_Sp9 [GenBank: NP_998125], Gg_Sp1 [GenBank: NP_989935], Gg_Sp2 [GenBank: XP_423405], Gg_Sp3 [GenBank: NP_989934], Gg_Sp4 [GenBank: XP_418708], Gg_Sp5 [GenBank: NP_001038149], Gg_Sp8 [GenBank: AAU04515.1], Gg_Sp9 [GenBank: AAU04516.1], Fr_Sp1 [GenBank: CAAB01000453.1], Fr_Sp2 [GenBank: CAAB01001586.1], Fr_Sp3 [GenBank: CAAB01000508.1], Fr_Sp3-like [GenBank: CAAB01000254.1], Fr_Sp4 [GenBank: CAAB01001019.1], Fr_Sp5 [GenBank: CAAB01001064.1], Fr_Sp5-like [GenBank: CAAB01000006.1], Fr_Sp6 [GenBank: CAAB01004244.1], Fr_Sp7 [GenBank: CAAB01000453.1], Fr_Sp8 [GenBank: CAAB01001019.1], Fr_Sp9 [GenBank: CAAB01000508.1]. In addition, we have provisionally annotated the Sp-family genes of D. pulex, T. adhaerens and B. floridae using the following genomic regions: Dp_Sp1-4 , Dp_Sp5/btd , Dp_Sp6-9 , Ta_Sp1-4 , Ta_Sp5/btd , Ta_Sp6-9 , Bf_Sp1-4 , Bf_Sp5/btd , Bf_Sp6-9 .Click here for fileGenomic locations of Sp genes and Hox genes. This table supplements the schematic overview given in Fig. H. sapiens: Genome Reference Consortium Human Build 37 (GRCh37), Primary_Assembly; D. melanogaster: release 5.10, A. gambiae: AgamP3.3, A. mellifera: Amel_4.0, T. castaneum: Tcas_3.0, D. pulex: JGI-2006-09, N. vectensis: Nematostella vectensis v1.0. The data for the N. vectensis Hox genes can be found in the references given in the table. Alternating shading for different species is used in the table to enhance the legibility of the table. Abbreviations: LG, linkage group; un, unassembled portions of the genome.Click here for fileSequence information about the gene specific primers used in this study. The first column gives the species and the gene. The second column gives the primer sequences in 5' to 3' orientation. The third column gives the length of the cloned fragment resulting from the PCR with the given primers. The fourth column gives the clone ID number. The fifth column gives the polymerase used to transcribe the RNA probe used for in situ hybridizations. The primers for D. melanogaster and T. castaneum have been designed as gene specific pairs using the genome sequence information. For O. fasciatus, T. domestica, F. candida, and P. hawaiensis we first isolated a small fragment of the genes using degenerate primers specified in the Materials and methods section. The gene specific RACE primers were designed on the basis of this sequence information and were used in conjunction with the commercial RACE adaptor primers. The cloned fragment of Ph Sp6-9 resulted from priming of the given primer pair. Abbreviations: Fwd, forward; Rev, reverse.Click here for file