GalaxyRefine: Protein structure refinement driven by side-chain repacking

Overexpression and characterization of the 100K protein of Fowl adenovirus-4 as an antiviral target

100K is an important scaffolding protein of adenoviruses including fowl adenovirus serotype 4 (FAdV-4) that causes inclusion body hepatitis-hydropericardium syndrome (IBH-HPS) in poultry. 100K carries out the trimerization of the major capsid hexon protein of the virus for the generation of new virions inside the target host cells. Despite its critical role for FAdV-4, no structural study, in particular, has been conducted so far. Here, the overexpression of soluble 100K protein was successfully carried out in E. coli using various expression constructs and purification yield of 3mg per litre culture volume was obtained. Gel filtration chromatography suggested that 100K protein exists in trimeric form. Circular dichroism and Fourier transform infrared spectroscopy clearly reveal that 100K protein folds with a high content of α-helices. The 3-dimentional homology model of the 100K protein, refined with molecular dynamics tools also depicts higher α-helical content within the protein model. Moreover, overexpressed recombinant 100K protein could be used to differentiate vaccinated and FAdV-4 infected chickens on the basis of higher serum anti 100K antibody titres. Our work provides preliminary structural and functional results to study biological role of the 100K protein and for further investigations to develop 100K inhibitors to control IBH-HPS in poultry.

Molecular interactions between tubulin tails and glutamylases reveal determinants of glutamylation patterns

Posttranslational modifications of tubulin currently emerge as key regulators of microtubule functions. Polyglutamylation generates a variety of modification patterns that are essential for controlling microtubule functions in different cell types and organelles, and deregulation of these patterns has been linked to ciliopathies, cancer and neurodegeneration. How the different glutamylating enzymes determine precise modification patterns has so far remained elusive. Using computational modelling, molecular dynamics simulations and mutational analyses we now show how the carboxy-terminal tails of tubulin bind into the active sites of glutamylases. Our models suggest that the glutamylation sites on α- and β-tubulins are determined by the positioning of the tails within the catalytic pocket. Moreover, we found that the binding modes of α- and β-tubulin tails are highly similar, implying that most enzymes could potentially modify both, α- and β-tubulin. This supports a model in which the binding of the enzymes to the entire microtubule lattice, but not the specificity of the C-terminal tubulin tails to their active sites, determines the catalytic specificities of glutamylases.

Computational protein structure refinement: Almost there, yet still so far to go

Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improves local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful.

Princeton_TIGRESS 2.0: High refinement consistency and net gains through support vector machines and molecular dynamics in double-blind predictions during the CASP11 experiment

Protein structure refinement is the challenging problem of operating on any protein structure prediction to improve its accuracy with respect to the native structure in a blind fashion. Although many approaches have been developed and tested during the last four CASP experiments, a majority of the methods continue to degrade models rather than improve them. Princeton_TIGRESS (Khoury et al., Proteins 2014;82:794-814) was developed previously and utilizes separate sampling and selection stages involving Monte Carlo and molecular dynamics simulations and classification using an SVM predictor. The initial implementation was shown to consistently refine protein structures 76% of the time in our own internal benchmarking on CASP 7-10 targets. In this work, we improved the sampling and selection stages and tested the method in blind predictions during CASP11. We added a decomposition of physics-based and hybrid energy functions, as well as a coordinate-free representation of the protein structure through distance-binning Cα-Cα distances to capture fine-grained movements. We performed parameter estimation to optimize the adjustable SVM parameters to maximize precision while balancing sensitivity and specificity across all cross-validated data sets, finding enrichment in our ability to select models from the populations of similar decoys generated for targets in CASPs 7-10. The MD stage was enhanced such that larger structures could be further refined. Among refinement methods that are currently implemented as web-servers, Princeton_TIGRESS 2.0 demonstrated the most consistent and most substantial net refinement in blind predictions during CASP11. The enhanced refinement protocol Princeton_TIGRESS 2.0 is freely available as a web server at http://atlas.engr.tamu.edu/refinement/. Proteins 2017; 85:1078-1098. © 2017 Wiley Periodicals, Inc.

Molecular and Structural Characterization of the Tegumental 20.6-kDa Protein in Clonorchis sinensis as a Potential Druggable Target

The tegument, representing the membrane-bound outer surface of platyhelminth parasites, plays an important role for the regulation of the host immune response and parasite survival. A comprehensive understanding of tegumental proteins can provide drug candidates for use against helminth-associated diseases, such as clonorchiasis caused by the liver fluke Clonorchis sinensis. However, little is known regarding the physicochemical properties of C. sinensis teguments. In this study, a novel 20.6-kDa tegumental protein of the C. sinensis adult worm (CsTegu20.6) was identified and characterized by molecular and in silico methods. The complete coding sequence of 525 bp was derived from cDNA clones and encodes a protein of 175 amino acids. Homology search using BLASTX showed CsTegu20.6 identity ranging from 29% to 39% with previously-known tegumental proteins in C. sinensis. Domain analysis indicated the presence of a calcium-binding EF-hand domain containing a basic helix-loop-helix structure and a dynein light chain domain exhibiting a ferredoxin fold. We used a modified method to obtain the accurate tertiary structure of the CsTegu20.6 protein because of the unavailability of appropriate templates. The CsTegu20.6 protein sequence was split into two domains based on the disordered region, and then, the structure of each domain was modeled using I-TASSER. A final full-length structure was obtained by combining two structures and refining the whole structure. A refined CsTegu20.6 structure was used to identify a potential CsTegu20.6 inhibitor based on protein structure-compound interaction analysis. The recombinant proteins were expressed in Escherichia coli and purified by nickel-nitrilotriacetic acid affinity chromatography. In C. sinensis, CsTegu20.6 mRNAs were abundant in adult and metacercariae, but not in the egg. Immunohistochemistry revealed that CsTegu20.6 localized to the surface of the tegument in the adult fluke. Collectively, our results contribute to a better understanding of the structural and functional characteristics of CsTegu20.6 and homologs of flukes. One compound is proposed as a putative inhibitor of CsTegu20.6 to facilitate further studies for anthelmintics.

Rational design of DKK3 structure-based small peptides as antagonists of Wnt signaling pathway and in silico evaluation of their efficiency

Dysregulated Wnt signaling pathway is highly associated with the pathogenesis of several human cancers. Dickkopf proteins (DKKs) are thought to inhibit Wnt signaling pathway through binding to lipoprotein receptor-related protein (LRP) 5/6. In this study, based on the 3-dimensional (3D) structure of DKK3 Cys-rich domain 2 (CRD2), we have designed and developed several peptide inhibitors of Wnt signaling pathway. Modeller 9.15 package was used to predict 3D structure of CRD2 based on the Homology modeling (HM) protocol. After refinement and minimization with GalaxyRefine and NOMAD-REF servers, the quality of selected models was evaluated utilizing VADAR, SAVES and ProSA servers. Molecular docking studies as well as literature-based information revealed two distinct boxes located at CRD2 which are actively involved in the DKK3-LRP5/6 interaction. A peptide library was constructed conducting the backrub sequence tolerance scanning protocol in Rosetta3.5 according to the DKK3-LRP5/6 binding sites. Seven tolerated peptides were chosen and their binding affinity and stability were improved by some logical amino acid substitutions. Molecular dynamics (MD) simulations of peptide-LRP5/6 complexes were carried out using GROMACS package. After evaluation of binding free energies, stability, electrostatic potential and some physicochemical properties utilizing computational approaches, three peptides (PEP-I1, PEP-I3 and PEP-II2) demonstrated desirable features. However, all seven improved peptides could sufficiently block the Wnt-binding site of LRP6 in silico. In conclusion, we have designed and improved several small peptides based on the LRP6-binding site of CRD2 of DKK3. These peptides are highly capable of binding to LRP6 in silico, and may prevent the formation of active Wnt-LRP6-Fz complex.

Activation of the LRR Receptor-Like Kinase PSY1R Requires Transphosphorylation of Residues in the Activation Loop

PSY1R is a leucine-rich repeat (LRR) receptor-like kinase (RLK) previously shown to act as receptor for the plant peptide hormone PSY1 (peptide containing sulfated tyrosine 1) and to regulate cell expansion. PSY1R phosphorylates and thereby regulates the activity of plasma membrane-localized H⁺-ATPases. While this mechanism has been studied in detail, little is known about how PSY1R itself is activated. Here we studied the activation mechanism of PSY1R. We show that full-length PSY1R interacts with members of the SERK co-receptor family in planta. We identified seven in vitro autophosphorylation sites on serine and threonine residues within the kinase domain of PSY1R using mass spectrometry. We furthermore show that PSY1R autophosphorylation occurs in trans and that the initial transphosphorylation takes place within the activation loop at residues Ser951, Thr959, and Thr963. While Thr959 and Thr963 are conserved among other related plant LRR RLKs, Ser951 is unique to PSY1R. Based on homology modeling we propose that phosphorylation of Ser951 stabilize the inactive conformation of PSY1R.

Template-Based Prediction of Protein-Peptide Interactions by Using GalaxyPepDock

We introduce a web server called GalaxyPepDock that predicts protein-peptide interactions based on templates. With the continuously increasing size of the protein structure database, the probability of finding related proteins for templates is increasing. GalaxyPepDock takes a protein structure and a peptide sequence as input and returns protein-peptide complex structures as output. Templates for protein-peptide complex structures are selected from the structure database considering similarity to the target protein structure and to putative protein-peptide interactions as estimated by protein structure alignment and peptide sequence alignment. Complex structures are then built from the template structures by template-based modeling. By further structure refinement that performs energy-based optimization, structural aspects that are missing in the template structures or that are not compatible with the given protein and peptide are refined. During the refinement, flexibilities of both protein and peptide induced by binding are considered. The atomistic protein-peptide interactions predicted by GalaxyPepDock can offer important clues for designing new peptides with desired binding properties.

Computational analysis, structural modeling and ligand binding site prediction of Plasmodium falciparum 1-deoxy-d-xylulose-5-phosphate synthase

Malaria remains one of the most serious infectious diseases in the world. There are five human species of the Plasmodium genus, of which Plasmodium falciparum is the most virulent and responsible for the vast majority of malaria related deaths. The unique biochemical processes that exist in Plasmodium falciparum provide a useful way to develop novel inhibitors. One such biochemical pathway is the methyl erythritol phosphate pathway (MEP), required to synthesize isoprenoid precursors. In the present study, a detailed computational analysis has been performed for 1-deoxy-d-xylulose-5-phosphate synthase, a key enzyme in MEP. The protein is found to be stable and residues from 825 to 971 are highly conserved across species. The homology model of the enzyme is developed using three web-based servers and Modeller software. It has twelve disordered regions indicating its druggability. Virtual screening of ZINC database identifies ten potential compounds in thiamine diphosphate binding region of the enzyme.

GalaxyRefineComplex: Refinement of protein-protein complex model structures driven by interface repacking

Protein-protein docking methods have been widely used to gain an atomic-level understanding of protein interactions. However, docking methods that employ low-resolution energy functions are popular because of computational efficiency. Low-resolution docking tends to generate protein complex structures that are not fully optimized. GalaxyRefineComplex takes such low-resolution docking structures and refines them to improve model accuracy in terms of both interface contact and inter-protein orientation. This refinement method allows flexibility at the protein interface and in the overall docking structure to capture conformational changes that occur upon binding. Symmetric refinement is also provided for symmetric homo-complexes. This method was validated by refining models produced by available docking programs, including ZDOCK and M-ZDOCK, and was successfully applied to CAPRI targets in a blind fashion. An example of using the refinement method with an existing docking method for ligand binding mode prediction of a drug target is also presented. A web server that implements the method is freely available at http://galaxy.seoklab.org/refinecomplex.

Recent Advances in Computational Models for the Study of Protein-Peptide Interactions

We review computational models and software tools in current use for the study of protein-peptide interactions. Peptides and peptide derivatives are growing in interest as therapeutic agents to target protein-protein interactions. Protein-protein interactions are pervasive in biological systems and are responsible for the regulation of critical functions within the cell. Mutations or dysregulation of expression can alter the network of interactions among proteins and cause diseases such as cancer. Protein-protein binding interfaces, which are often large, shallow, and relatively feature-less, are difficult to target with small-molecule inhibitors. Peptide derivatives based on the binding motifs present in the target protein complex are increasingly drawing interest as superior alternatives to conventional small-molecule inhibitors. However, the design of peptide-based inhibitors also presents novel challenges. Peptides are more complex and more flexible than standard medicinal compounds. They also tend to form more extended and more complex interactions with their protein targets. Computational modeling is increasingly being employed to supplement synthetic and biochemical work to offer guidance and energetic and structural insights. In this review, we discuss recent in silico structure-based and physics-based approaches currently employed to model protein-peptide interactions with a few examples of their applications.

Local Protein Structure Refinement via Molecular Dynamics Simulations with locPREFMD

A method for the local refinement of protein structures that targets improvements in local stereochemistry while preserving the overall fold is presented. The method uses force field-based minimization and sampling via molecular dynamics simulations with a modified force field to bring bonds, angles, and torsion angles into an acceptable range for high-resolution protein structures. The method is implemented in the locPREFMD web server and was tested on computational models submitted to CASP11. Using MolProbity scores as the main assessment criterion, the locPREFMD method significantly improves the stereochemical quality of given input models close to the quality expected for experimental structures while maintaining the Cα coordinates of the initial model.

3Drefine: an interactive web server for efficient protein structure refinement

3Drefine is an interactive web server for consistent and computationally efficient protein structure refinement with the capability to perform web-based statistical and visual analysis. The 3Drefine refinement protocol utilizes iterative optimization of hydrogen bonding network combined with atomic-level energy minimization on the optimized model using a composite physics and knowledge-based force fields for efficient protein structure refinement. The method has been extensively evaluated on blind CASP experiments as well as on large-scale and diverse benchmark datasets and exhibits consistent improvement over the initial structure in both global and local structural quality measures. The 3Drefine web server allows for convenient protein structure refinement through a text or file input submission, email notification, provided example submission and is freely available without any registration requirement. The server also provides comprehensive analysis of submissions through various energy and statistical feedback and interactive visualization of multiple refined models through the JSmol applet that is equipped with numerous protein model analysis tools. The web server has been extensively tested and used by many users. As a result, the 3Drefine web server conveniently provides a useful tool easily accessible to the community. The 3Drefine web server has been made publicly available at the URL: http://sysbio.rnet.missouri.edu/3Drefine/.

Galaxy7TM: flexible GPCR-ligand docking by structure refinement

G-protein-coupled receptors (GPCRs) play important physiological roles related to signal transduction and form a major group of drug targets. Prediction of GPCR-ligand complex structures has therefore important implications to drug discovery. With previously available servers, it was only possible to first predict GPCR structures by homology modeling and then perform ligand docking on the model structures. However, model structures generated without explicit consideration of specific ligands of interest can be inaccurate because GPCR structures can be affected by ligand binding. The Galaxy7TM server, freely accessible at http://galaxy.seoklab.org/7TM, improves an input GPCR structure by simultaneous ligand docking and flexible structure refinement using GALAXY methods. The server shows better performance in both ligand docking and GPCR structure refinement than commonly used programs AutoDock Vina and Rosetta MPrelax, respectively.

CASP11--An Evaluation of a Modular BCL::Fold-Based Protein Structure Prediction Pipeline

In silico prediction of a protein's tertiary structure remains an unsolved problem. The community-wide Critical Assessment of Protein Structure Prediction (CASP) experiment provides a double-blind study to evaluate improvements in protein structure prediction algorithms. We developed a protein structure prediction pipeline employing a three-stage approach, consisting of low-resolution topology search, high-resolution refinement, and molecular dynamics simulation to predict the tertiary structure of proteins from the primary structure alone or including distance restraints either from predicted residue-residue contacts, nuclear magnetic resonance (NMR) nuclear overhauser effect (NOE) experiments, or mass spectroscopy (MS) cross-linking (XL) data. The protein structure prediction pipeline was evaluated in the CASP11 experiment on twenty regular protein targets as well as thirty-three 'assisted' protein targets, which also had distance restraints available. Although the low-resolution topology search module was able to sample models with a global distance test total score (GDT_TS) value greater than 30% for twelve out of twenty proteins, frequently it was not possible to select the most accurate models for refinement, resulting in a general decay of model quality over the course of the prediction pipeline. In this study, we provide a detailed overall analysis, study one target protein in more detail as it travels through the protein structure prediction pipeline, and evaluate the impact of limited experimental data.

SDRL: a sequence-dependent protein side-chain rotamer library

Since the introduction of the first protein side-chain rotamer library (RL) almost half a century ago, RLs have been components of many programs and algorithms in structural bioinformatics. Based on the dependence of side-chain dihedral angles on the local backbone, three types of RLs have been identified: backbone-independent, secondary-structure-dependent and backbone-dependent. In all previous studies, the effect of sequence specificity on side-chain conformational preferences was neglected. In the effort to develop a new class of RLs, we considered that the side-chain conformation of the central residue in each triplet on a protein backbone depends on the sequence of the triplet; therefore, we developed a sequence-dependent rotamer library (SDRL). To accomplish this, 400 possible triplet sequences for 18 natural amino acids as the central residue, which corresponds to 7200 triplet sequences in total, were considered. Searching the set of 11 546 selected PDB entries for the 7200 triplet sequences resulted in 2 364 541 instances occurring for 18 amino acids. Our results show that Leu and Val experience minimal impact from the adjacent residues in adopting side-chain conformations. Cys, Ile, Trp, His, Asp, Met, Glu, Gln, Arg and Lys, on the other hand, adopt their side-chain conformations mostly based on the adjacent residues on the backbone. The remaining residue types were moderately dependent on the adjacent residues. Using the new library, side-chain repacking algorithms can find preferred conformations of each residue more easily than with other backbone-independent RLs.

In-silico analysis of non-synonymous-SNPs of STEAP2: To provoke the progression of prostate cancer

As a novel biomarker from the STEAP family, STEAP2 encodes six transmembrane epithelial antigens to prostate cancer. The overexpression of STEAP2 is predicted as the second most common cancer in the world that is responsible for male cancer-related deaths. Nonsynonymous SNPs are important group of SNPs which lead to alternations in encoded polypeptides. Changes in the amino acid sequence of gene products can lead to abnormal tissue function. The present study firstly sorted out those SNPs which exist in the coding region of STEAP2 and evaluated their impact through computational tools. Secondly, the three-dimensional structure of STEAP2 was formed through I-TASSER and validated by different software. Genomic data has been retrieved from the 1000 Genome project and Ensembl and subsequently analysed using computational tools. Out of 177 non-synonymous single nucleotide polymorphisms (nsSNPs) within the coding region, 42 mis-sense SNPs have been predicted as deleterious by all analyses. Our research shows a welldesigned computational methodology to inspect the prostate cancer associated nsSNPs. It can be concluded that these nsSNPs can play their role in the up-regulation of STEAP2 which further leads to progression of prostate cancer. It can benefit scientists in the handling of cancerassociated diseases related to STEAP2 through developing novel drug therapies.

Crystal Structure of Streptococcus pyogenes Cas1 and Its Interaction with Csn2 in the Type II CRISPR-Cas System

CRISPRs and Cas proteins constitute an RNA-guided microbial immune system against invading nucleic acids. Cas1 is a universal Cas protein found in all three types of CRISPR-Cas systems, and its role is implicated in new spacer acquisition during CRISPR-mediated adaptive immunity. Here, we report the crystal structure of Streptococcus pyogenes Cas1 (SpCas1) in a type II CRISPR-Cas system and characterize its interaction with S. pyogenes Csn2 (SpCsn2). The SpCas1 structure reveals a unique conformational state distinct from type I Cas1 structures, resulting in a more extensive dimerization interface, a more globular overall structure, and a disruption of potential metal-binding sites for catalysis. We demonstrate that SpCas1 directly interacts with SpCsn2, and identify the binding interface and key residues for Cas complex formation. These results provide structural information for a type II Cas1 protein, and lay a foundation for studying multiprotein Cas complexes functioning in type II CRISPR-Cas systems.

The Tomato Nucleotide-binding Leucine-rich Repeat Immune Receptor I-2 Couples DNA-binding to Nucleotide-binding Domain Nucleotide Exchange

Plant nucleotide-binding leucine-rich repeat (NLR) proteins enable plants to recognize and respond to pathogen attack. Previously, we demonstrated that the Rx1 NLR of potato is able to bind and bend DNA in vitro. DNA binding in situ requires its genuine activation following pathogen perception. However, it is unknown whether other NLR proteins are also able to bind DNA. Nor is it known how DNA binding relates to the ATPase activity intrinsic to NLR switch function required to immune activation. Here we investigate these issues using a recombinant protein corresponding to the N-terminal coiled-coil and nucleotide-binding domain regions of the I-2 NLR of tomato. Wild type I-2 protein bound nucleic acids with a preference of ssDNA ≈ dsDNA > ssRNA, which is distinct from Rx1. I-2 induced bending and melting of DNA. Notably, ATP enhanced DNA binding relative to ADP in the wild type protein, the null P-loop mutant K207R, and the autoactive mutant S233F. DNA binding was found to activate the intrinsic ATPase activity of I-2. Because DNA binding by I-2 was decreased in the presence of ADP when compared with ATP, a cyclic mechanism emerges; activated ATP-associated I-2 binds to DNA, which enhances ATP hydrolysis, releasing ADP-bound I-2 from the DNA. Thus DNA binding is a general property of at least a subset of NLR proteins, and NLR activation is directly linked to its activity at DNA.

The Potato Nucleotide-binding Leucine-rich Repeat (NLR) Immune Receptor Rx1 Is a Pathogen-dependent DNA-deforming Protein

Plant nucleotide-binding leucine-rich repeat (NLR) proteins enable cells to respond to pathogen attack. Several NLRs act in the nucleus; however, conserved nuclear targets that support their role in immunity are unknown. Previously, we noted a structural homology between the nucleotide-binding domain of NLRs and DNA replication origin-binding Cdc6/Orc1 proteins. Here we show that the NB-ARC (nucleotide-binding, Apaf-1, R-proteins, and CED-4) domain of the Rx1 NLR of potato binds nucleic acids. Rx1 induces ATP-dependent bending and melting of DNA in vitro, dependent upon a functional P-loop. In situ full-length Rx1 binds nuclear DNA following activation by its cognate pathogen-derived effector protein, the coat protein of potato virus X. In line with its obligatory nucleocytoplasmic distribution, DNA binding was only observed when Rx1 was allowed to freely translocate between both compartments and was activated in the cytoplasm. Immune activation induced by an unrelated NLR-effector pair did not trigger an Rx1-DNA interaction. DNA binding is therefore not merely a consequence of immune activation. These data establish a role for DNA distortion in Rx1 immune signaling and define DNA as a molecular target of an activated NLR.

Protein structure refinement via molecular-dynamics simulations: What works and what does not?

Protein structure refinement during CASP11 by the Feig group was described. Molecular dynamics simulations were used in combination with an improved selection and averaging protocol. On average, modest refinement was achieved with some targets improved significantly. Analysis of the CASP submission from our group focused on refinement success versus amount of sampling, refinement of different secondary structure elements and whether refinement varied as a function of which group provided initial models. The refinement of local stereochemical features was examined via the MolProbity score and an updated protocol was developed that can generate high-quality structures with very low MolProbity scores for most starting structures with modest computational effort. Proteins 2016; 84(Suppl 1):282-292. © 2015 Wiley Periodicals, Inc.

Effective protein model structure refinement by loop modeling and overall relaxation

Protein structures predicted by state-of-the-art template-based methods may still have errors when the template proteins are not similar enough to the target protein. Overall target structure may deviate from the template structures owing to differences in sequences. Structural information for some local regions such as loops may not be available when there are sequence insertions or deletions. Those structural aspects that originate from deviations from templates can be dealt with by ab initio structure refinement methods to further improve model accuracy. In the CASP11 refinement experiment, we tested three different refinement methods that utilize overall structure relaxation, loop modeling, and quality assessment of multiple initial structures. From this experiment, we conclude that the overall relaxation method can consistently improve model quality. Loop modeling is the most useful when the initial model structure is high quality, with GDT-HA >60. The method that used multiple initial structures further refined the already refined models; the minor improvements with this method raise the issue of problem with the current energy function. Future research directions are also discussed. Proteins 2016; 84(Suppl 1):293-301. © 2015 Wiley Periodicals, Inc.

GalaxyPepDock: a protein-peptide docking tool based on interaction similarity and energy optimization

Protein-peptide interactions are involved in a wide range of biological processes and are attractive targets for therapeutic purposes because of their small interfaces. Therefore, effective protein-peptide docking techniques can provide the basis for potential therapeutic applications by enabling an atomic-level understanding of protein interactions. With the increasing number of protein-peptide structures deposited in the protein data bank, the prediction accuracy of protein-peptide docking can be enhanced by utilizing the information provided by the database. The GalaxyPepDock web server, which is freely accessible at http://galaxy.seoklab.org/pepdock, performs similarity-based docking by finding templates from the database of experimentally determined structures and building models using energy-based optimization that allows for structural flexibility. The server can therefore effectively model the structural differences between the template and target protein-peptide complexes. The performance of GalaxyPepDock is superior to those of the other currently available web servers when tested on the PeptiDB set and on recently released complex structures. When tested on the CAPRI target 67, GalaxyPepDock generates models that are more accurate than the best server models submitted during the CAPRI blind prediction experiment.

The origin of consistent protein structure refinement from structural averaging

Recent studies have shown that explicit solvent molecular dynamics (MD) simulation followed by structural averaging can consistently improve protein structure models. We find that improvement upon averaging is not limited to explicit water MD simulation, as consistent improvements are also observed for more efficient implicit solvent MD or Monte Carlo minimization simulations. To determine the origin of these improvements, we examine the changes in model accuracy brought about by averaging at the individual residue level. We find that the improvement in model quality from averaging results from the superposition of two effects: a dampening of deviations from the correct structure in the least well modeled regions, and a reinforcement of consistent movements towards the correct structure in better modeled regions. These observations are consistent with an energy landscape model in which the magnitude of the energy gradient toward the native structure decreases with increasing distance from the native state.

Structural models of zebrafish (Danio rerio) NOD1 and NOD2 NACHT domains suggest differential ATP binding orientations: insights from computational modeling, docking and molecular dynamics simulations

Nucleotide-binding oligomerization domain-containing protein 1 (NOD1) and NOD2 are cytosolic pattern recognition receptors playing pivotal roles in innate immune signaling. NOD1 and NOD2 recognize bacterial peptidoglycan derivatives iE-DAP and MDP, respectively and undergoes conformational alternation and ATP-dependent self-oligomerization of NACHT domain followed by downstream signaling. Lack of structural adequacy of NACHT domain confines our understanding about the NOD-mediated signaling mechanism. Here, we predicted the structure of NACHT domain of both NOD1 and NOD2 from model organism zebrafish (Danio rerio) using computational methods. Our study highlighted the differential ATP binding modes in NOD1 and NOD2. In NOD1, γ-phosphate of ATP faced toward the central nucleotide binding cavity like NLRC4, whereas in NOD2 the cavity was occupied by adenine moiety. The conserved ‘Lysine’ at Walker A formed hydrogen bonds (H-bonds) and Aspartic acid (Walker B) formed electrostatic interaction with ATP. At Sensor 1, Arg328 of NOD1 exhibited an H-bond with ATP, whereas corresponding Arg404 of NOD2 did not. ‘Proline’ of GxP motif (Pro386 of NOD1 and Pro464 of NOD2) interacted with adenine moiety and His511 at Sensor 2 of NOD1 interacted with γ-phosphate group of ATP. In contrast, His579 of NOD2 interacted with the adenine moiety having a relatively inverted orientation. Our findings are well supplemented with the molecular interaction of ATP with NLRC4, and consistent with mutagenesis data reported for human, which indicates evolutionary shared NOD signaling mechanism. Together, this study provides novel insights into ATP binding mechanism, and highlights the differential ATP binding modes in zebrafish NOD1 and NOD2.

WeFold: a coopetition for protein structure prediction

The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by 13 labs. During the collaboration, the laboratories were simultaneously competing with each other. Here, we present the first attempt at "coopetition" in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org.

Multi-Dimensional Scaling and MODELLER-Based Evolutionary Algorithms for Protein Model Refinement

Protein structure prediction, i.e., computationally predicting the three-dimensional structure of a protein from its primary sequence, is one of the most important and challenging problems in bioinformatics. Model refinement is a key step in the prediction process, where improved structures are constructed based on a pool of initially generated models. Since the refinement category was added to the biennial Critical Assessment of Structure Prediction (CASP) in 2008, CASP results show that it is a challenge for existing model refinement methods to improve model quality consistently. This paper presents three evolutionary algorithms for protein model refinement, in which multidimensional scaling(MDS), the MODELLER software, and a hybrid of both are used as crossover operators, respectively. The MDS-based method takes a purely geometrical approach and generates a child model by combining the contact maps of multiple parents. The MODELLER-based method takes a statistical and energy minimization approach, and uses the remodeling module in MODELLER program to generate new models from multiple parents. The hybrid method first generates models using the MDS-based method and then run them through the MODELLER-based method, aiming at combining the strength of both. Promising results have been obtained in experiments using CASP datasets. The MDS-based method improved the best of a pool of predicted models in terms of the global distance test score (GDT-TS) in 9 out of 16test targets.

Structure of vaccinia virus A46, an inhibitor of TLR4 signaling pathway, shows the conformation of VIPER motif

Vaccinia virus (VACV) encodes many proteins that interfere with the host immune system. Vaccinia virus A46 protein specifically targets the BB-loop motif of TIR-domain-containing proteins to disrupt receptor:adaptor (e.g., TLR4:MAL and TLR4:TRAM) interactions of the toll-like receptor signaling. The crystal structure of A46 (75-227) determined at 2.58 Å resolution showed that A46 formed a homodimer and adopted a Bcl-2-like fold similar to other VACV proteins such as A52, B14, and K7. Our structure also revealed that VIPER (viral inhibitory peptide of TLR4) motif resides in the α1-helix and six residues of the VIPER region were exposed to surface for binding to target proteins. In vitro binding assays between wild type and six mutants A46 (75-227) and full-length MAL identified critical residues in the VIPER motif. Computational modeling of the A46:MAL complex structure showed that the VIPER region of A46 and AB loop of MAL protein formed a major binding interface. In summary, A46 is a homodimer with a Bcl-2-like fold and VIPER motif is believed to be involved in the interaction with MAL protein based on our binding assays.

GalaxySite: ligand-binding-site prediction by using molecular docking

Knowledge of ligand-binding sites of proteins provides invaluable information for functional studies, drug design and protein design. Recent progress in ligand-binding-site prediction methods has demonstrated that using information from similar proteins of known structures can improve predictions. The GalaxySite web server, freely accessible at http://galaxy.seoklab.org/site, combines such information with molecular docking for more precise binding-site prediction for non-metal ligands. According to the recent critical assessments of structure prediction methods held in 2010 and 2012, this server was found to be superior or comparable to other state-of-the-art programs in the category of ligand-binding-site prediction. A strong merit of the GalaxySite program is that it provides additional predictions on binding ligands and their binding poses in terms of the optimized 3D coordinates of the protein–ligand complexes, whereas other methods predict only identities of binding-site residues or copy binding geometry from similar proteins. The additional information on the specific binding geometry would be very useful for applications in functional studies and computer-aided drug discovery.

A novel homozygous mutation in the EC1/EC2 interaction domain of the gap junction complex connexon 26 leads to profound hearing impairment

To date, about 165 genetic loci or genes have been identified which are associated with nonsyndromal hearing impairment. In about half the cases, genetic defects in the GJB2 gene (connexin 26) are the most common cause of inner-ear deafness. The genes GJB2 and GJB6 are localized on chromosome 13q11-12 in tandem orientation. Connexins belong to the group of "gap junction" proteins, which form connexons, each consisting of six connexin molecules. These are responsible for the exchange of ions and smaller molecules between neighboring cells. Mutational analysis in genes GJB2 and GJB6 was brought by direct sequencing of the coding exons including the intron transitions. Here we show in the participating extended family a homozygous mutation c.506G>A, (TGC>TAC) p.Cys169Tyr, in the GJB2 gene, which could be proven for the first time and led to nonsyndromal severe hearing impairment in the afflicted patients. The mutation is located in the EC1/EC2 interaction complex of the gap junction connexon 26 complex and interrupts the K(+) circulation and therefore the ion homeostasis in the inner ear. The homozygous mutation p.Cys169Tyr identified here provides a novel insight into the structure-function relationship of the gap junction complex connexin/connexon 26.

Princeton_TIGRESS: protein geometry refinement using simulations and support vector machines

Protein structure refinement aims to perform a set of operations given a predicted structure to improve model quality and accuracy with respect to the native in a blind fashion. Despite the numerous computational approaches to the protein refinement problem reported in the previous three CASPs, an overwhelming majority of methods degrade models rather than improve them. We initially developed a method tested using blind predictions during CASP10 which was officially ranked in 5th place among all methods in the refinement category. Here, we present Princeton_TIGRESS, which when benchmarked on all CASP 7,8,9, and 10 refinement targets, simultaneously increased GDT_TS 76% of the time with an average improvement of 0.83 GDT_TS points per structure. The method was additionally benchmarked on models produced by top performing three-dimensional structure prediction servers during CASP10. The robustness of the Princeton_TIGRESS protocol was also tested for different random seeds. We make the Princeton_TIGRESS refinement protocol freely available as a web server at http://atlas.princeton.edu/refinement. Using this protocol, one can consistently refine a prediction to help bridge the gap between a predicted structure and the actual native structure.