Simultaneous refinement of inaccurate local regions and overall structure in the CASP12 protein model refinement experiment

Advances in protein model refinement techniques are required as diverse sources of protein structure information are available from low-resolution experiments or informatics-based computations such as cryo-EM, NMR, homology models, or predicted residue contacts. Given semi-reliable or incomplete structural information, structure quality of a protein model has to be improved by ab initio methods such as energy-based simulation. In this study, we describe a new automatic refinement server method designed to improve locally inaccurate regions and overall structure simultaneously. Locally inaccurate regions may occur in protein structures due to non-convergent or missing information in template structures used in homology modeling or due to intrinsic structural flexibilities not resolved by experimental techniques. However, such variable or dynamic regions often play important functional roles by participating in interactions with other biomolecules or in transitions between different functional states. The new refinement method introduced here utilizes diverse types of geometric operators which drive both local and global changes, and the effect of structure changes and relaxations are accumulated. This resulted in consistent refinement of both local and global structural features. Performance of this method in CASP12 is discussed.

Cell-cell adhesion in metazoans relies on evolutionarily conserved features of the α-catenin·β-catenin-binding interface

Stable tissue integrity during embryonic development relies on the function of the cadherin·catenin complex (CCC). The Caenorhabditis elegans CCC is a useful paradigm for analyzing in vivo requirements for specific interactions among the core components of the CCC, and it provides a unique opportunity to examine evolutionarily conserved mechanisms that govern the interaction between α- and β-catenin. HMP-1, unlike its mammalian homolog α-catenin, is constitutively monomeric, and its binding affinity for HMP-2/β-catenin is higher than that of α-catenin for β-catenin. A crystal structure shows that the HMP-1·HMP-2 complex forms a five-helical bundle structure distinct from the structure of the mammalian α-catenin·β-catenin complex. Deletion analysis based on the crystal structure shows that the first helix of HMP-1 is necessary for binding HMP-2 avidly in vitro and for efficient recruitment of HMP-1 to adherens junctions in embryos. HMP-2 Ser-47 and Tyr-69 flank its binding interface with HMP-1, and we show that phosphomimetic mutations at these two sites decrease binding affinity of HMP-1 to HMP-2 by 40-100-fold in vitro. In vivo experiments using HMP-2 S47E and Y69E mutants showed that they are unable to rescue hmp-2(zu364) mutants, suggesting that phosphorylation of HMP-2 on Ser-47 and Tyr-69 could be important for regulating CCC formation in C. elegans Our data provide novel insights into how cadherin-dependent cell-cell adhesion is modulated in metazoans by conserved elements as well as features unique to specific organisms.

Benchmarking predictions of allostery in liver pyruvate kinase in CAGI4

The Critical Assessment of Genome Interpretation (CAGI) is a global community experiment to objectively assess computational methods for predicting phenotypic impacts of genomic variation. One of the 2015-2016 competitions focused on predicting the influence of mutations on the allosteric regulation of human liver pyruvate kinase. More than 30 different researchers accessed the challenge data. However, only four groups accepted the challenge. Features used for predictions ranged from evolutionary constraints, mutant site locations relative to active and effector binding sites, and computational docking outputs. Despite the range of expertise and strategies used by predictors, the best predictions were marginally greater than random for modified allostery resulting from mutations. In contrast, several groups successfully predicted which mutations severely reduced enzymatic activity. Nonetheless, poor predictions of allostery stands in stark contrast to the impression left by more than 700 PubMed entries identified using the identifiers "computational + allosteric." This contrast highlights a specialized need for new computational tools and utilization of benchmarks that focus on allosteric regulation.

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.

Absolute binding free energies for octa-acids and guests in SAMPL5 : Evaluating binding free energies for octa-acid and guest complexes in the SAMPL5 blind challenge

As part of the SAMPL5 blind prediction challenge, we calculate the absolute binding free energies of six guest molecules to an octa-acid (OAH) and to a methylated octa-acid (OAMe). We use the double decoupling method via thermodynamic integration (TI) or Hamiltonian replica exchange in connection with the Bennett acceptance ratio (HREM-BAR). We produce the binding poses either through manual docking or by using GalaxyDock-HG, a docking software developed specifically for this study. The root mean square deviations for our most accurate predictions are 1.4 kcal mol-1 for OAH with TI and 1.9 kcal mol-1 for OAMe with HREM-BAR. Our best results for OAMe were obtained for systems with ionic concentrations corresponding to the ionic strength of the experimental solution. The most problematic system contains a halogenated guest. Our attempt to model the σ-hole of the bromine using a constrained off-site point charge, does not improve results. We use results from molecular dynamics simulations to argue that the distinct binding affinities of this guest to OAH and OAMe are due to a difference in the flexibility of the host. We believe that the results of this extensive analysis of host-guest complexes will help improve the protocol used in predicting binding affinities for larger systems, such as protein-substrate compounds.

Template-based modeling and ab initio refinement of protein oligomer structures using GALAXY in CAPRI round 30

Many proteins function as homo- or hetero-oligomers; therefore, attempts to understand and regulate protein functions require knowledge of protein oligomer structures. The number of available experimental protein structures is increasing, and oligomer structures can be predicted using the experimental structures of related proteins as templates. However, template-based models may have errors due to sequence differences between the target and template proteins, which can lead to functional differences. Such structural differences may be predicted by loop modeling of local regions or refinement of the overall structure. In CAPRI (Critical Assessment of PRotein Interactions) round 30, we used recently developed features of the GALAXY protein modeling package, including template-based structure prediction, loop modeling, model refinement, and protein-protein docking to predict protein complex structures from amino acid sequences. Out of the 25 CAPRI targets, medium and acceptable quality models were obtained for 14 and 1 target(s), respectively, for which proper oligomer or monomer templates could be detected. Symmetric interface loop modeling on oligomer model structures successfully improved model quality, while loop modeling on monomer model structures failed. Overall refinement of the predicted oligomer structures consistently improved the model quality, in particular in interface contacts. Proteins 2017; 85:399-407. © 2016 Wiley Periodicals, Inc.

Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.

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.

Potential Application of Alchemical Free Energy Simulations to Discriminate GPCR Ligand Efficacy

G protein-coupled receptors (GPCRs) play fundamental roles in physiological processes by modulating diverse signaling pathways and thus have been one of the most important drug targets. Based on the fact that GPCR-mediated signaling is modulated in a ligand-specific manner such as agonist, inverse agonist, and neutral antagonist (termed ligand efficacy), quantitative characterization of the ligand efficacy is essential for rational design of selective modulators for GPCR targets. As experimental approaches for this purpose are time-, cost-, and labor-intensive, computational tools that can systematically predict GPCR ligand efficacy can have a big impact on GPCR drug design. Here, we have performed free energy perturbation molecular dynamics simulations to calculate absolute binding free energy of an inverse agonist, a neutral antagonist, and an agonist to β2-adrenergic receptor (β2-AR) active and inactive states, respectively, in explicit lipid bilayers. Relatively short alchemical free energy calculations reveal that both the time-series of the total binding free energy and decomposed energy contributions can be used as relevant physical properties to discriminate β2-AR ligand efficacy. This study illustrates a merit of the current approach over simple, fast docking calculations or highly expensive millisecond-time scale simulations.

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.

Binding Site Prediction of Proteins with Organic Compounds or Peptides Using GALAXY Web Servers

We introduce two GALAXY web servers called GalaxySite and GalaxyPepDock that predict protein complex structures with small organic compounds and peptides, respectively. GalaxySite predicts ligands that may bind the input protein and generates complex structures of the protein with the predicted ligands from the protein structure given as input or predicted from the input sequence. GalaxyPepDock takes a protein structure and a peptide sequence as input and predicts structures for the protein-peptide complex. Both GalaxySite and GalaxyPepDock rely on available experimentally resolved structures of protein-ligand complexes evolutionarily related to the target. With the continuously increasing size of the protein structure database, the probability of finding related proteins in the database is increasing. The servers further relax the complex structures to refine the structural aspects that are missing in the available structures or that are not compatible with the given protein by optimizing physicochemical interactions. GalaxyPepDock allows conformational change of the protein receptor induced by peptide binding. The atomistic interactions with ligands predicted by the GALAXY servers may offer important clues for designing new molecules or proteins with desired binding properties.

Use of the Weighted Histogram Analysis Method for the Analysis of Simulated and Parallel Tempering Simulations

The growing adoption of generalized-ensemble algorithms for biomolecular simulation has resulted in a resurgence in the use of the weighted histogram analysis method (WHAM) to make use of all data generated by these simulations. Unfortunately, the original presentation of WHAM by Kumar et al. is not directly applicable to data generated by these methods. WHAM was originally formulated to combine data from independent samplings of the canonical ensemble, whereas many generalized-ensemble algorithms sample from mixtures of canonical ensembles at different temperatures. Sorting configurations generated from a parallel tempering simulation by temperature obscures the temporal correlation in the data and results in an improper treatment of the statistical uncertainties used in constructing the estimate of the density of states. Here we present variants of WHAM, STWHAM and PTWHAM, derived with the same set of assumptions, that can be directly applied to several generalized ensemble algorithms, including simulated tempering, parallel tempering (better known as replica-exchange among temperatures), and replica-exchange simulated tempering. We present methods that explicitly capture the considerable temporal correlation in sequentially generated configurations using autocorrelation analysis. This allows estimation of the statistical uncertainty in WHAM estimates of expectations for the canonical ensemble. We test the method with a one-dimensional model system and then apply it to the estimation of potentials of mean force from parallel tempering simulations of the alanine dipeptide in both implicit and explicit solvent.

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.

Evaluation of GalaxyDock Based on the Community Structure-Activity Resource 2013 and 2014 Benchmark Studies

We analyze the results of the GalaxyDock protein-ligand docking program in the two recent experiments of Community Structure-Activity Resource (CSAR), CSAR 2013 and 2014. GalaxyDock performs global optimization of a modified AutoDock3 energy function by employing the conformational space annealing method. The energy function of GalaxyDock is quite sensitive to atomic clashes. Such energy functions can be effective for sampling physically correct conformations but may not be effective for scoring when conformations are not fully optimized. In phase 1 of CSAR 2013, we successfully selected all four true binders of digoxigenin along with three false positives. However, the energy values were rather high due to insufficient optimization of the conformations docked to homology models. A posteriori relaxation of the model complex structures by GalaxyRefine improved the docking energy values and differentiated the true binders from the false positives better. In the scoring test of CSAR 2013 phase 2, we selected the best poses for each of the two targets. The results of CSAR 2013 phase 3 suggested that an improved method for generating initial conformations for GalaxyDock is necessary for targets involving bulky ligands. Finally, combining existing binding information with GalaxyDock energy-based optimization may be needed for more accurate binding affinity prediction.

Measured and predicted affinities of binding and relative potencies to activate the AhR of PAHs and their alkylated analogues

Polycyclic aromatic hydrocarbons (PAHs) and their alkylated forms are important components of crude oil. Both groups of PAHs have been reported to cause dioxin-like responses, mediated by aryl hydrocarbon receptor (AhR). Thus, characterization of binding affinity to the AhR of unsubstituted or alkylated PAHs is important to understand the toxicological consequences of oil contamination on ecosystems. We investigated the potencies of major PAHs of crude oil, e.g., chrysene, phenanthrene and dibenzothiophene, and their alkylated forms (n=17) to upregulate expression of AhR-mediated processes by use of the H4IIE-luc transactivation bioassay. In addition, molecular descriptors of different AhR activation potencies among PAHs were investigated by use of computational molecular docking models. Based on responses of the H4IIE-luc in vitro assay, it was shown that potencies of PAHs were determined by alkylation in addition to the number and conformation of rings. Potencies of AhR-mediated processes were generally greater when a chrysene group was substituted, especially in 1-methyl-chrysene. Significant negative correlations were observed between the in vitro dioxin-like potency measured in H4IIE-luc cells and the binding distance estimated from the in silico modeling. The difference in relative potency for AhR activation observed among PAHs and their alkylated forms could be explained by differences among binding distances in the ligand binding domain of the AhR caused by alkylation. The docking model developed in the present study may have utility in predicting risks of environmental contaminants of which toxicities are mediated by AhR binding.

High-resolution protein-protein docking by global optimization: recent advances and future challenges

A computational protein-protein docking method that predicts atomic details of protein-protein interactions from protein monomer structures is an invaluable tool for understanding the molecular mechanisms of protein interactions and for designing molecules that control such interactions. Compared to low-resolution docking, high-resolution docking explores the conformational space in atomic resolution to provide predictions with atomic details. This allows for applications to more challenging docking problems that involve conformational changes induced by binding. Recently, high-resolution methods have become more promising as additional information such as global shapes or residue contacts are now available from experiments or sequence/structure data. In this review article, we highlight developments in high-resolution docking made during the last decade, specifically regarding global optimization methods employed by the docking methods. We also discuss two major challenges in high-resolution docking: prediction of backbone flexibility and water-mediated interactions.

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.

Protein loop modeling using a new hybrid energy function and its application to modeling in inaccurate structural environments

Protein loop modeling is a tool for predicting protein local structures of particular interest, providing opportunities for applications involving protein structure prediction and de novo protein design. Until recently, the majority of loop modeling methods have been developed and tested by reconstructing loops in frameworks of experimentally resolved structures. In many practical applications, however, the protein loops to be modeled are located in inaccurate structural environments. These include loops in model structures, low-resolution experimental structures, or experimental structures of different functional forms. Accordingly, discrepancies in the accuracy of the structural environment assumed in development of the method and that in practical applications present additional challenges to modern loop modeling methods. This study demonstrates a new strategy for employing a hybrid energy function combining physics-based and knowledge-based components to help tackle this challenge. The hybrid energy function is designed to combine the strengths of each energy component, simultaneously maintaining accurate loop structure prediction in a high-resolution framework structure and tolerating minor environmental errors in low-resolution structures. A loop modeling method based on global optimization of this new energy function is tested on loop targets situated in different levels of environmental errors, ranging from experimental structures to structures perturbed in backbone as well as side chains and template-based model structures. The new method performs comparably to force field-based approaches in loop reconstruction in crystal structures and better in loop prediction in inaccurate framework structures. This result suggests that higher-accuracy predictions would be possible for a broader range of applications. The web server for this method is available at http://galaxy.seoklab.org/loop with the PS2 option for the scoring function.

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.

Blind prediction of interfacial water positions in CAPRI

We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes.