The performance of ZDOCK and ZRANK in rounds 6-11 of CAPRI

In silico studies on the interaction of phage displayed biorecognition element (TFQAFDLSPFPS) with the structural protein VP28 of white spot syndrome virus

White spot disease caused by the white spot syndrome virus (WSSV) incurs a huge loss to the shrimp farming industry. Since no effective therapeutic measures are available, early detection and prevention of the disease are indispensable. Towards this goal, we previously identified a 12-mer phage displayed peptide (designated as pep28) with high affinity for VP28, the structural protein of the white spot syndrome virus (WSSV). The peptide pep28 was successfully used as a biorecognition probe in the lateral flow assay developed for rapid, on-site detection of WSSV. To unravel the structural determinants for the selective binding between VP28 and pep28, we used bioinformatics, structural modeling, protein-protein docking, and binding-free energy studies. We performed atomistic molecular dynamics simulations of pep28-pIII model totaling 300 ns timescale. The most representative pep28-pIII structure from the simulation was used for docking with the crystal structure of VP28. Our results reveal that pep28 binds in a surface groove of the monomeric VP28 β-barrel and makes several hydrogen bonds and non-polar interactions. Ensemble-based binding-free energy studies reveal that the binding is dominated by non-polar interactions. Our studies provide molecular level insights into the binding mechanism of pep28 with VP28, which explain why the peptide is selective and can assist in modifying pep28 for its practical use, both as a biorecognition probe and a therapeutic.

Performance of ZDOCK and IRAD in CAPRI rounds 39-45

We report docking performance on the six targets of Critical Assessment of PRedicted Interactions (CAPRI) rounds 39-45 that involved heteromeric protein-protein interactions and had the solved structures released since the rounds were held. Our general strategy involved protein-protein docking using ZDOCK, reranking using IRAD, and structural refinement using Rosetta. In addition, we made extensive use of experimental data to guide our docking runs. All the experimental information at the amino-acid level proved correct. However, for two targets, we also used protein-complex structures as templates for modeling interfaces. These resulted in incorrect predictions, presumably due to the low sequence identity between the targets and templates. Albeit a small number of targets, the performance described here compared somewhat less favorably with our previous CAPRI reports, which may be due to the CAPRI targets being increasingly challenging.

Molecular Docking and Molecular Dynamics (MD) Simulation of Human Anti-Complement Factor H (CFH) Antibody Ab42 and CFH Polypeptide

An understanding of the interaction between the antibody and its targeted antigen and knowing of the epitopes are critical for the development of monoclonal antibody drugs. Complement factor H (CFH) is implied to play a role in tumor growth and metastasis. An autoantibody to CHF is associated with anti-tumor cell activity. The interaction of a human monoclonal antibody Ab42 that was isolated from a cancer patient with CFH polypeptide (pCFH) antigen was analyzed by molecular docking, molecular dynamics (MD) simulation, free energy calculation, and computational alanine scanning (CAS). Experimental alanine scanning (EAS) was then carried out to verify the results of the theoretical calculation. Our results demonstrated that the Ab42 antibody interacts with pCFH by hydrogen bonds through the Tyr315, Ser100, Gly33, and Tyr53 residues on the complementarity-determining regions (CDRs), respectively, with the amino acid residues of Pro441, Ile442, Asp443, Asn444, Ile447, and Thr448 on the pCFH antigen. In conclusion, this study has explored the mechanism of interaction between Ab42 antibody and its targeted antigen by both theoretical and experimental analysis. Our results have important theoretical significance for the design and development of relevant antibody drugs.

Mechanistic insights into high mobility group box-1 (HMGb1)-induced Toll-like receptor 4 (TLR4) dimer formation

Supplemental data for this article can be accessed here.High mobility group box-1 (HMGb1), an endogenous danger-associated molecular pattern protein (DAMP) whose extracellular release has been associated with sterile injury and various inflammatory diseases and conditions, has been shown to be a valuable clinical drug target. Elucidation of the specific interactions with the HMGb1 receptor, Toll-like receptor 4 (TLR4) and adaptor protein myeloid differentiation factor-2 (MD-2), will lead to more precisely targeted therapeutics. We sought to examine detailed interactions and dynamics of the HMGb1 A-box and B-box fragments, as well as the intact protein using in silico protein-protein docking (ZDOCK, ZRANK) and molecular dynamics (Schrödinger Desmond, New York, NY). Mutagenesis and SPR-binding studies allowed us to draw further conclusions regarding the details of the HMGb1-TLR4-MD2 interaction and shed light on the reasons for the opposing biological activities of HMGb1 A-box and B-box fragments. From our findings, we hypothesize that disulfide A-box fragment binds as an anchor toward the TLR4-MD-2 but does not facilitate the TLR4 dimer formation, thereby competing with the HMGb1-binding site and preventing HMGb1-induced signaling and downstream inflammation, whereas the pro-inflammatory B-box fragment retains the MD-2 active conformation and binds to both TLR4 proteins in the complex to aid TLR4 dimer formation, which activates the intracellular signaling for downstream inflammatory pathways and cytokine release. Communicated by Ramaswamy H. Sarma.

AnkPlex: algorithmic structure for refinement of near-native ankyrin-protein docking

BACKGROUND

Computational analysis of protein-protein interaction provided the crucial information to increase the binding affinity without a change in basic conformation. Several docking programs were used to predict the near-native poses of the protein-protein complex in 10 top-rankings. The universal criteria for discriminating the near-native pose are not available since there are several classes of recognition protein. Currently, the explicit criteria for identifying the near-native pose of ankyrin-protein complexes (APKs) have not been reported yet.

RESULTS

In this study, we established an ensemble computational model for discriminating the near-native docking pose of APKs named "AnkPlex". A dataset of APKs was generated from seven X-ray APKs, which consisted of 3 internal domains, using the reliable docking tool ZDOCK. The dataset was composed of 669 and 44,334 near-native and non-near-native poses, respectively, and it was used to generate eleven informative features. Subsequently, a re-scoring rank was generated by AnkPlex using a combination of a decision tree algorithm and logistic regression. AnkPlex achieved superior efficiency with ≥1 near-native complexes in the 10 top-rankings for nine X-ray complexes compared to ZDOCK, which only obtained six X-ray complexes. In addition, feature analysis demonstrated that the van der Waals feature was the dominant near-native pose out of the potential ankyrin-protein docking poses.

CONCLUSION

The AnkPlex model achieved a success at predicting near-native docking poses and led to the discovery of informative characteristics that could further improve our understanding of the ankyrin-protein complex. Our computational study could be useful for predicting the near-native poses of binding proteins and desired targets, especially for ankyrin-protein complexes. The AnkPlex web server is freely accessible at http://ankplex.ams.cmu.ac.th .

Addressing recent docking challenges: A hybrid strategy to integrate template-based and free protein-protein docking

Protein-protein docking is an important computational tool for predicting protein-protein interactions. With the rapid development of proteomics projects, more and more experimental binding information ranging from mutagenesis data to three-dimensional structures of protein complexes are becoming available. Therefore, how to appropriately incorporate the biological information into traditional ab initio docking has been an important issue and challenge in the field of protein-protein docking. To address these challenges, we have developed a Hybrid DOCKing protocol of template-based and template-free approaches, referred to as HDOCK. The basic procedure of HDOCK is to model the structures of individual components based on the template complex by a template-based method if a template is available; otherwise, the component structures will be modeled based on monomer proteins by regular homology modeling. Then, the complex structure of the component models is predicted by traditional protein-protein docking. With the HDOCK protocol, we have participated in the CPARI experiment for rounds 28-35. Out of the 25 CASP-CAPRI targets for oligomer modeling, our HDOCK protocol predicted correct models for 16 targets, ranking one of the top algorithms in this challenge. Our docking method also made correct predictions on other CAPRI challenges such as protein-peptide binding for 6 out of 8 targets and water predictions for 2 out of 2 targets. The advantage of our hybrid docking approach over pure template-based docking was further confirmed by a comparative evaluation on 20 CASP-CAPRI targets. Proteins 2017; 85:497-512. © 2016 Wiley Periodicals, Inc.

Molecular Docking at a Glance

The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.

Performance of ZDOCK and IRAD in CAPRI rounds 28-34

We report the performance of our protein-protein docking pipeline, including the ZDOCK rigid-body docking algorithm, on 19 targets in CAPRI rounds 28-34. Following the docking step, we reranked the ZDOCK predictions using the IRAD scoring function, pruned redundant predictions, performed energy landscape analysis, and utilized our interface prediction approach RCF. In addition, we applied constraints to the search space based on biological information that we culled from the literature, which increased the chance of making a correct prediction. For all but two targets we were able to find and apply biological information and we found the information to be highly accurate, indicating that effective incorporation of biological information is an important component for protein-protein docking. Proteins 2017; 85:408-416. © 2016 Wiley Periodicals, Inc.

Photochemical Reactions of the LOV and LOV-Linker Domains of the Blue Light Sensor Protein YtvA

YtvA is a blue light sensor protein composed of an N-terminal LOV (light-oxygen-voltage) domain, a linker helix, and the C-terminal sulfate transporter and anti-σ factor antagonist domain. YtvA is believed to act as a positive regulator for light and salt stress responses by regulating the σB transcription factor. Although its biological function has been studied, the reaction dynamics and molecular mechanism underlying the function are not well understood. To improve our understanding of the signaling mechanism, we studied the reaction of the LOV domain (YLOV, amino acids 26-127), the LOV domain with its N-terminal extension (N-YLOV, amino acids 1-127), the LOV domain with its C-terminal linker helix (YLOV-linker, amino acids 26-147), and the YLOV domain with the N-terminal extension and the C-terminal linker helix (N-YLOV-linker, amino acids 1-147) using the transient grating method. The signals of all constructs showed adduct formation, thermal diffusion, and molecular diffusion. YLOV showed no change in the diffusion coefficient (D), while the other three constructs showed a significant decrease in D within ∼70 μs of photoexcitation. This indicates that conformational changes in both the N- and C-terminal helices of the YLOV domain indeed do occur. The time constant in the YtvA derivatives was much faster than the corresponding dynamics of phototropins. Interestingly, an additional reaction was observed as a volume expansion as well as a slight increase in D only when both helices were included. These findings suggest that although the rearrangement of the N- and C-terminal helices occurs independently on the fast time scale, this change induces an additional conformational change only when both helices are present.

Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures

Background

Protein-protein interactions (PPIs) mediate the vast majority of biological processes, therefore, significant efforts have been directed to investigate PPIs to fully comprehend cellular functions. Predicting complex structures is critical to reveal molecular mechanisms by which proteins operate. Despite recent advances in the development of new methods to model macromolecular assemblies, most current methodologies are designed to work with experimentally determined protein structures. However, because only computer-generated models are available for a large number of proteins in a given genome, computational tools should tolerate structural inaccuracies in order to perform the genome-wide modeling of PPIs.

Results

To address this problem, we developed eRank^PPI, an algorithm for the identification of near-native conformations generated by protein docking using experimental structures as well as protein models. The scoring function implemented in eRank^PPI employs multiple features including interface probability estimates calculated by eFindSite^PPI and a novel contact-based symmetry score. In comparative benchmarks using representative datasets of homo- and hetero-complexes, we show that eRank^PPI consistently outperforms state-of-the-art algorithms improving the success rate by ~10 %.

Conclusions

eRank^PPI was designed to bridge the gap between the volume of sequence data, the evidence of binary interactions, and the atomic details of pharmacologically relevant protein complexes. Tolerating structure imperfections in computer-generated models opens up a possibility to conduct the exhaustive structure-based reconstruction of PPI networks across proteomes. The methods and datasets used in this study are available at www.brylinski.org/erankppi.

Performance of ZDOCK in CAPRI rounds 20-26

We report the performance of our approaches for protein-protein docking and interface analysis in CAPRI rounds 20-26. At the core of our pipeline was the ZDOCK program for rigid-body protein-protein docking. We then reranked the ZDOCK predictions using the ZRANK or IRAD scoring functions, pruned and analyzed energy landscapes using clustering, and analyzed the docking results using our interface prediction approach RCF. When possible, we used biological information from the literature to apply constraints to the search space during or after the ZDOCK runs. For approximately half of the standard docking challenges we made at least one prediction that was acceptable or better. For the scoring challenges we made acceptable or better predictions for all but one target. This indicates that our scoring functions are generally able to select the correct binding mode.

Cross-linking analysis reveals the putative dimer structure of the cyanobacterial BLUF photoreceptor PixD

PixD is a blue light using flavin (BLUF)-type blue-light photoreceptor controlling phototaxis in the cyanobacterium Synechocystis sp. PCC6803. The crystal structure of PixD shows a decamer, although in solution an equilibrium is maintained between the dimer and decamer. Because the ratio of these two conformers is altered by illumination, the equilibrium state determines photosensitivity. However, no structural information is available for the PixD dimer. Here, we report a predicted structure for the dimer based on docking simulation, mutagenesis, and mass spectrometry-based cross-linking analyses. The results indicate the importance of the PixD C-terminus for dimer preference and photosensitivity.

A flexible docking approach for prediction of T cell receptor-peptide-MHC complexes

T cell receptors (TCRs) are immune proteins that specifically bind to antigenic molecules, which are often foreign peptides presented by major histocompatibility complex proteins (pMHCs), playing a key role in the cellular immune response. To advance our understanding and modeling of this dynamic immunological event, we assembled a protein-protein docking benchmark consisting of 20 structures of crystallized TCR/pMHC complexes for which unbound structures exist for both TCR and pMHC. We used our benchmark to compare predictive performance using several flexible and rigid backbone TCR/pMHC docking protocols. Our flexible TCR docking algorithm, TCRFlexDock, improved predictive success over the fixed backbone protocol, leading to near-native predictions for 80% of the TCR/pMHC cases among the top 10 models, and 100% of the cases in the top 30 models. We then applied TCRFlexDock to predict the two distinct docking modes recently described for a single TCR bound to two different antigens, and tested several protein modeling scoring functions for prediction of TCR/pMHC binding affinities. This algorithm and benchmark should enable future efforts to predict, and design of uncharacterized TCR/pMHC complexes.

ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers

SUMMARY

Protein-protein interactions are essential to cellular and immune function, and in many cases, because of the absence of an experimentally determined structure of the complex, these interactions must be modeled to obtain an understanding of their molecular basis. We present a user-friendly protein docking server, based on the rigid-body docking programs ZDOCK and M-ZDOCK, to predict structures of protein-protein complexes and symmetric multimers. With a goal of providing an accessible and intuitive interface, we provide options for users to guide the scoring and the selection of output models, in addition to dynamic visualization of input structures and output docking models. This server enables the research community to easily and quickly produce structural models of protein-protein complexes and symmetric multimers for their own analysis.

AVAILABILITY

The ZDOCK server is freely available to all academic and non-profit users at: http://zdock.umassmed.edu. No registration is required.

Replica exchange improves sampling in low-resolution docking stage of RosettaDock

Many protein-protein docking protocols are based on a shotgun approach, in which thousands of independent random-start trajectories minimize the rigid-body degrees of freedom. Another strategy is enumerative sampling as used in ZDOCK. Here, we introduce an alternative strategy, ReplicaDock, using a small number of long trajectories of temperature replica exchange. We compare replica exchange sampling as low-resolution stage of RosettaDock with RosettaDock's original shotgun sampling as well as with ZDOCK. A benchmark of 30 complexes starting from structures of the unbound binding partners shows improved performance for ReplicaDock and ZDOCK when compared to shotgun sampling at equal or less computational expense. ReplicaDock and ZDOCK consistently reach lower energies and generate significantly more near-native conformations than shotgun sampling. Accordingly, they both improve typical metrics of prediction quality of complex structures after refinement. Additionally, the refined ReplicaDock ensembles reach significantly lower interface energies and many previously hidden features of the docking energy landscape become visible when ReplicaDock is applied.

Molecular dynamic and docking interaction study of Heterodera glycines serine proteinase with Vigna mungo proteinase inhibitor

Many plants do produce various defense proteins like proteinase inhibitors (PIs) to protect them against various pests. PIs function as pseudosubstrates of digestive proteinase, which inhibits proteolysis in pests and leads to amino acid deficiency-based mortality. This work reports the structural interaction studies of serine proteinase of Heterodera glycines (SPHG) with Vigna mungo proteinase inhibitor (VMPI). 3D protein structure modeling, validation of SPHG and VMPI, and their putative protein-protein binding sites were predicted. Protein-protein docking followed by molecular dynamic simulation was performed to find the reliable confirmation of SPHG-VMPI complex. Trajectory analysis of each successive conformation concludes better interaction of first loop in comparison with second loop. Lysine residues of first loop were actively participating in complex formation. Overall, this study discloses the structural aspects and interaction mechanisms of VMPI with SPHG, and it would be helpful in the development of pest-resistant genetically modified crops.

Epitope mapping of metuximab on CD147 using phage display and molecular docking

Metuximab is the generic name of Licartin, a new drug for radioimmunotherapy of hepatocellular carcinoma. Although it is known to be a mouse monoclonal antibody against CD147, the complete epitope mediating the binding of metuximab to CD147 remains unknown. We panned the Ph.D.-12 phage display peptide library against metuximab and got six mimotopes. The following bioinformatics analysis based on mimotopes suggested that metuximab recognizes a conformational epitope composed of more than 20 residues. The residues of its epitope may include T28, V30, K36, L38, K57, F74, D77, S78, D79, D80, Q81, G83, S86, N98, Q100, L101, H102, G103, P104, V131, P132, and K191. The homology modeling of metuximab and the docking of CD147 to metuximab were also performed. Based on the top one docking model, the epitope was predicted to contain 28 residues: AGTVFTTV (23–30), I37, D45, E84, V88, EPMGTANIQLH (92–102), VPP (131–133), Q164, and K191. Almost half of the residues predicted on the basis of mimotope analysis also appear in the docking result, indicating that both results are reliable. As the predicted epitopes of metuximab largely overlap with interfaces of CD147-CD147 interactions, a structural mechanism of metuximab is proposed as blocking the formation of CD147 dimer.

Rice mitogen activated protein kinase kinase and mitogen activated protein kinase interaction network revealed by in-silico docking and yeast two-hybrid approaches

Protein-protein interaction is one of the crucial ways to decipher the functions of proteins and to understand their role in complex pathways at cellular level. Such a protein-protein interaction network in many crop plants remains poorly defined owing largely to the involvement of high costs, requirement for state of the art laboratory, time and labour intensive techniques. Here, we employed computational docking using ZDOCK and RDOCK programmes to identify interaction network between members of Oryza sativa mitogen activated protein kinase kinase (MAPKK) and mitogen activated protein kinase (MAPK). The 3-dimentional (3-D) structures of five MAPKKs and eleven MAPKs were determined by homology modelling and were further used as input for docking studies. With the help of the results obtained from ZDOCK and RDOCK programmes, top six possible interacting MAPK proteins were predicted for each MAPKK. In order to assess the reliability of the computational prediction, yeast two-hybrid (Y2H) analyses were performed using rice MAPKKs and MAPKs. A direct comparison of Y2H assay and computational prediction of protein interaction was made. With the exception of one, all the other MAPKK-MAPK pairs identified by Y2H screens were among the top predictions by computational dockings. Although, not all the predicted interacting partners could show interaction in Y2H, yet, the harmony between the two approaches suggests that the computational predictions in the present work are reliable. Moreover, the present Y2H analyses per se provide interaction network among MAPKKs and MAPKs which would shed more light on MAPK signalling network in rice.

Water-soluble LYNX1 residues important for interaction with muscle-type and/or neuronal nicotinic receptors

Human LYNX1, belonging to the Ly6/neurotoxin family of three-finger proteins, is membrane-tethered with a glycosylphosphatidylinositol anchor and modulates the activity of nicotinic acetylcholine receptors (nAChR). Recent preparation of LYNX1 as an individual protein in the form of water-soluble domain lacking glycosylphosphatidylinositol anchor (ws-LYNX1; Lyukmanova, E. N., Shenkarev, Z. O., Shulepko, M. A., Mineev, K. S., D'Hoedt, D., Kasheverov, I. E., Filkin, S. Y., Krivolapova, A. P., Janickova, H., Dolezal, V., Dolgikh, D. A., Arseniev, A. S., Bertrand, D., Tsetlin, V. I., and Kirpichnikov, M. P. (2011) NMR structure and action on nicotinic acetylcholine receptors of water-soluble domain of human LYNX1. J. Biol. Chem. 286, 10618-10627) revealed the attachment at the agonist-binding site in the acetylcholine-binding protein (AChBP) and muscle nAChR but outside it, in the neuronal nAChRs. Here, we obtained a series of ws-LYNX1 mutants (T35A, P36A, T37A, R38A, K40A, Y54A, Y57A, K59A) and examined by radioligand analysis or patch clamp technique their interaction with the AChBP, Torpedo californica nAChR and chimeric receptor composed of the α7 nAChR extracellular ligand-binding domain and the transmembrane domain of α1 glycine receptor (α7-GlyR). Against AChBP, there was either no change in activity (T35A, T37A), slight decrease (K40A, K59A), and even enhancement for the rest mutants (most pronounced for P36A and R38A). With both receptors, many mutants lost inhibitory activity, but the increased inhibition was observed for P36A at α7-GlyR. Thus, there are subtype-specific and common ws-LYNX1 residues recognizing distinct targets. Because ws-LYNX1 was inactive against glycine receptor, its "non-classical" binding sites on α7 nAChR should be within the extracellular domain. Micromolar affinities and fast washout rates measured for ws-LYNX1 and its mutants are in contrast to nanomolar affinities and irreversibility of binding for α-bungarotoxin and similar snake α-neurotoxins also targeting α7 nAChR. This distinction may underlie their different actions, i.e. nAChRs modulation versus irreversible inhibition, for these two types of three-finger proteins.

A predicted structure for the PixD-PixE complex determined by homology modeling, docking simulations, and a mutagenesis study

PixD is a blue light-using flavin (BLUF) photoreceptor that controls phototaxis in the cyanobacterium Synechocystis sp. PCC6803. PixD interacts with the response regulator-like protein PixE in a light-dependent manner, and this interaction is critical for light signal transduction in vivo. However, the structure of the PixD-PixE complex has not been determined. To improve our understanding of how PixD transmits its captured light signal to PixE, we used blue-native polyacrylamide gel electrophoresis to characterize the molecular mass of a recombinant PixD-PixE complex purified from Escherichia coli and found it to be 342 kDa, suggesting that the complex contains 10 PixD and 4 PixE monomers. The stoichiometry of the complex was confirmed by Western blotting. Specifically, three intermediate states, PixD(10)-PixE(1), PixD(10)-PixE(2), and PixD(10)-PixE(3), were detected. The apparent dissociation constant for PixE and PixD is ~5 μM. A docking simulation was performed using a modeled PixE structure and the PixD(10) crystal structure. The docking simulation showed how the molecules in the PixD(10)-PixE(4) structure interact. To verify the accuracy of the docked model, a site-directed mutagenesis study was performed in which Arg80 of PixE, which appears to be capable of interacting electrostatically with Asp135 of PixD in the predicted structure, was shown to be critical for complex formation as mutation of PixE Arg80 to Asp or Ala prevented PixD-PixE complex formation. This study provides a structural basis for future investigations of the light signal transduction mechanism involving PixD and PixE.

A PixD--PapB chimeric protein reveals the function of the BLUF domain C-terminal α-helices for light signal transduction

Blue light-using flavin (BLUF) proteins form a subfamily of blue light photoreceptors, are found in many bacteria and algae, and are further classified according to their structures. For one type of BLUF-containing protein, e.g. PixD, the central axes of its two C-terminal α-helices are perpendicular to the β-sheet of its N-terminal BLUF domain. For another type, e.g. PapB, the central axes of its two C-terminal α-helices are parallel to its BLUF domain β-sheet. However, the functional significance of the different orientations with respect to phototransduction is not clear. For the study reported herein, we constructed a chimeric protein, Pix0522, containing the core of the PixD BLUF domain and the C-terminal region of PapB, including the two α-helices, and characterized its biochemical and spectroscopic properties. Fourier transform infrared spectroscopy detected similar light-induced conformational changes in the C-terminal α-helices of Pix0522 and PapB. Pix0522 interacts with and activates the PapB-interacting enzyme, PapA, demonstrating the functionality of Pix0522. These results provide direct evidence that the BLUF C-terminal α-helices function as an intermediary that accepts the flavin-sensed blue light signal and transmits it downstream during phototransduction.

Protein docking prediction using predicted protein-protein interface

Background

Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations.

Results

We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering.

Conclusion

We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

IN SILICO SCREENING OF PROTEIN–PROTEIN INTERACTIONS WITH ALL-TO-ALL RIGID DOCKING AND CLUSTERING: AN APPLICATION TO PATHWAY ANALYSIS

We propose a computational screening system of protein–protein interactions using tertiary structure data. Our system combines all-to-all protein docking and clustering to find interacting protein pairs. We tuned our prediction system by applying various parameters and clustering algorithms and succeeded in outperforming previous methods. This method was also applied to a biological pathway estimation problem to show its use in network level analysis. The structural data were collected from the Protein Data Bank, PDB. Then all-to-all docking among target protein structures was conducted using a conventional protein–protein docking software package, ZDOCK. The highest-ranked 2000 decoys were clustered based on structural similarity among the predicted docking forms. The features of generated clusters were analyzed to estimate the biological relevance of protein–protein interactions. Our system achieves a best F-measure value of 0.43 when applied to a subset of general protein–protein docking benchmark data. The same system was applied to protein data in a bacterial chemotaxis pathway, utilizing essentially the same parameter set as the benchmark data. We obtained 0.45 for the F-measure value. The proposed approach to computational PPI detection is a promising methodology for mediating between structural studies and systems biology by utilizing cumulative protein structure data for pathway analysis.

Protein-protein complex structure predictions by multimeric threading and template recombination

The total number of protein-protein complex structures currently available in the Protein Data Bank (PDB) is six times smaller than the total number of tertiary structures in the PDB, which limits the power of homology-based approaches to complex structure modeling. We present a threading-recombination approach, COTH, to boost the protein complex structure library by combining tertiary structure templates with complex alignments. The query sequences are first aligned to complex templates using a modified dynamic programming algorithm, guided by ab initio binding-site predictions. The monomer alignments are then shifted to the multimeric template framework by structural alignments. COTH was tested on 500 nonhomologous dimeric proteins, which can successfully detect correct templates for 50% of the cases after homologous templates are excluded, which significantly outperforms conventional homology modeling algorithms. It also shows a higher accuracy in interface modeling than rigid-body docking of unbound structures from ZDOCK although with lower coverage. These data demonstrate new avenues to model complex structures from nonhomologous templates.

Accelerating protein docking in ZDOCK using an advanced 3D convolution library

Computational prediction of the 3D structures of molecular interactions is a challenging area, often requiring significant computational resources to produce structural predictions with atomic-level accuracy. This can be particularly burdensome when modeling large sets of interactions, macromolecular assemblies, or interactions between flexible proteins. We previously developed a protein docking program, ZDOCK, which uses a fast Fourier transform to perform a 3D search of the spatial degrees of freedom between two molecules. By utilizing a pairwise statistical potential in the ZDOCK scoring function, there were notable gains in docking accuracy over previous versions, but this improvement in accuracy came at a substantial computational cost. In this study, we incorporated a recently developed 3D convolution library into ZDOCK, and additionally modified ZDOCK to dynamically orient the input proteins for more efficient convolution. These modifications resulted in an average of over 8.5-fold improvement in running time when tested on 176 cases in a newly released protein docking benchmark, as well as substantially less memory usage, with no loss in docking accuracy. We also applied these improvements to a previous version of ZDOCK that uses a simpler non-pairwise atomic potential, yielding an average speed improvement of over 5-fold on the docking benchmark, while maintaining predictive success. This permits the utilization of ZDOCK for more intensive tasks such as docking flexible molecules and modeling of interactomes, and can be run more readily by those with limited computational resources.

Integrating atom-based and residue-based scoring functions for protein-protein docking

Most scoring functions for protein-protein docking algorithms are either atom-based or residue-based, with the former being able to produce higher quality structures and latter more tolerant to conformational changes upon binding. Earlier, we developed the ZRANK algorithm for reranking docking predictions, with a scoring function that contained only atom-based terms. Here we combine ZRANK's atom-based potentials with five residue-based potentials published by other labs, as well as an atom-based potential IFACE that we published after ZRANK. We simultaneously optimized the weights for selected combinations of terms in the scoring function, using decoys generated with the protein-protein docking algorithm ZDOCK. We performed rigorous cross validation of the combinations using 96 test cases from a docking benchmark. Judged by the integrative success rate of making 1000 predictions per complex, addition of IFACE and the best residue-based pair potential reduced the number of cases without a correct prediction by 38 and 27% relative to ZDOCK and ZRANK, respectively. Thus combination of residue-based and atom-based potentials into a scoring function can improve performance for protein-protein docking. The resulting scoring function is called IRAD (integration of residue- and atom-based potentials for docking) and is available at http://zlab.umassmed.edu.

Benchmarking and analysis of protein docking performance in Rosetta v3.2

RosettaDock has been increasingly used in protein docking and design strategies in order to predict the structure of protein-protein interfaces. Here we test capabilities of RosettaDock 3.2, part of the newly developed Rosetta v3.2 modeling suite, against Docking Benchmark 3.0, and compare it with RosettaDock v2.3, the latest version of the previous Rosetta software package. The benchmark contains a diverse set of 116 docking targets including 22 antibody-antigen complexes, 33 enzyme-inhibitor complexes, and 60 ‘other’ complexes. These targets were further classified by expected docking difficulty into 84 rigid-body targets, 17 medium targets, and 14 difficult targets. We carried out local docking perturbations for each target, using the unbound structures when available, in both RosettaDock v2.3 and v3.2. Overall the performances of RosettaDock v2.3 and v3.2 were similar. RosettaDock v3.2 achieved 56 docking funnels, compared to 49 in v2.3. A breakdown of docking performance by protein complex type shows that RosettaDock v3.2 achieved docking funnels for 63% of antibody-antigen targets, 62% of enzyme-inhibitor targets, and 35% of ‘other’ targets. In terms of docking difficulty, RosettaDock v3.2 achieved funnels for 58% of rigid-body targets, 30% of medium targets, and 14% of difficult targets. For targets that failed, we carry out additional analyses to identify the cause of failure, which showed that binding-induced backbone conformation changes account for a majority of failures. We also present a bootstrap statistical analysis that quantifies the reliability of the stochastic docking results. Finally, we demonstrate the additional functionality available in RosettaDock v3.2 by incorporating small-molecules and non-protein co-factors in docking of a smaller target set. This study marks the most extensive benchmarking of the RosettaDock module to date and establishes a baseline for future research in protein interface modeling and structure prediction.

Structural determinants of caspase-9 inhibition by the vaccinia virus protein, F1L

In multicellular organisms, apoptosis is a powerful method of host defense against viral infection. Apoptosis is mediated by a cascade of caspase-family proteases that commit infected cells to a form of programmed cell death. Therefore, to replicate within host cells, viruses have developed various strategies to inhibit caspase activation. In the mitochondrial cell-death pathway, release of cytochrome c from mitochondria into the cytosol triggers assembly of the oligomeric apoptosome, resulting in dimerization and activation of the apical caspase-9 (C9), and in turn its downstream effector caspases, leading to apoptosis. We previously showed that the vaccinia virus-encoded Bcl-2-like protein, F1L, which suppresses cytochrome c release by binding Bcl-2 family proteins, is also a C9 inhibitor. Here, we identify a novel motif within the flexible N-terminal region of F1L that is necessary and sufficient for interaction with and inhibition of C9. Based on functional studies and mutagenesis, we developed an atomic model of the complex in which F1L inhibits C9 by engaging the active site in the reverse orientation with respect to substrate peptides, in a manner analogous to that of XIAP-mediated inhibition of caspases-3 and -7. These studies offer new insights into the mechanism of apoptosome inhibition by F1L as well as novel probes to understand the molecular bases of apoptosome regulation and turnover. They also suggest how the two distinct functionalities of F1L (inhibition of C9 and suppression of pro-apoptotic Bcl-2 family proteins) may operate in a cellular setting.

Molecular basis of antigenic drift in Influenza A/H3N2 strains (1968-2007) in the light of antigenantibody interactions

The emergence of new strains of Influenza virus have caused several pandemics over the last hundred years with the latest being the H1N1 Swine flu pandemic of 2009. The Hemagglutinin (HA) protein of the Influenza virus is the primary target of human immune system and is responsible for generation of protective antibodies in humans. Mutations in this protein results in change in antigenic regions (antigenic drift) which consequently leads to loss of immunity in hosts even in vaccinated population (herd immunity). This necessitates periodic changes in the Influenza vaccine composition. In this paper, we investigate the molecular basis of the reported loss of herd immunity in vaccinated population (vaccine component: Influenza A/X-31/1968 (H3N2)) which resulted in the outbreak due to strain Influenza A/Port Chalmers/1/1973 (H3N2). Also, the effects of antigenic drift in HA protein (H3N2 vaccine strains 1968-2007) on the 3D structures as well as interactions with BH151, a 1968 antibody, has been studied. Rigid body molecular docking protocol has been used to study the antigen-antibody interactions. We believe that the present study will help in better understanding of host-pathogen interactions at the molecular level.

A machine learning approach for the prediction of protein surface loop flexibility

Proteins often undergo conformational changes when binding to each other. A major fraction of backbone conformational changes involves motion on the protein surface, particularly in loops. Accounting for the motion of protein surface loops represents a challenge for protein-protein docking algorithms. A first step in addressing this challenge is to distinguish protein surface loops that are likely to undergo backbone conformational changes upon protein-protein binding (mobile loops) from those that are not (stationary loops). In this study, we developed a machine learning strategy based on support vector machines (SVMs). Our SVM uses three features of loop residues in the unbound protein structures-Ramachandran angles, crystallographic B-factors, and relative accessible surface area-to distinguish mobile loops from stationary ones. This method yields an average prediction accuracy of 75.3% compared with a random prediction accuracy of 50%, and an average of 0.79 area under the receiver operating characteristic (ROC) curve using cross-validation. Testing the method on an independent dataset, we obtained a prediction accuracy of 70.5%. Finally, we applied the method to 11 complexes that involve members from the Ras superfamily and achieved prediction accuracy of 92.8% for the Ras superfamily proteins and 74.4% for their binding partners.

CPORT: a consensus interface predictor and its performance in prediction-driven docking with HADDOCK

Background

Macromolecular complexes are the molecular machines of the cell. Knowledge at the atomic level is essential to understand and influence their function. However, their number is huge and a significant fraction is extremely difficult to study using classical structural methods such as NMR and X-ray crystallography. Therefore, the importance of large-scale computational approaches in structural biology is evident. This study combines two of these computational approaches, interface prediction and docking, to obtain atomic-level structures of protein-protein complexes, starting from their unbound components.

Methodology/Principal Findings

Here we combine six interface prediction web servers into a consensus method called CPORT (Consensus Prediction Of interface Residues in Transient complexes). We show that CPORT gives more stable and reliable predictions than each of the individual predictors on its own. A protocol was developed to integrate CPORT predictions into our data-driven docking program HADDOCK. For cases where experimental information is limited, this prediction-driven docking protocol presents an alternative to ab initio docking, the docking of complexes without the use of any information. Prediction-driven docking was performed on a large and diverse set of protein-protein complexes in a blind manner. Our results indicate that the performance of the HADDOCK-CPORT combination is competitive with ZDOCK-ZRANK, a state-of-the-art ab initio docking/scoring combination. Finally, the original interface predictions could be further improved by interface post-prediction (contact analysis of the docking solutions).

Conclusions/Significance

The current study shows that blind, prediction-driven docking using CPORT and HADDOCK is competitive with ab initio docking methods. This is encouraging since prediction-driven docking represents the absolute bottom line for data-driven docking: any additional biological knowledge will greatly improve the results obtained by prediction-driven docking alone. Finally, the fact that original interface predictions could be further improved by interface post-prediction suggests that prediction-driven docking has not yet been pushed to the limit. A web server for CPORT is freely available at http://haddock.chem.uu.nl/services/CPORT.

Structural modeling of histone methyltransferase complex Set1C from Saccharomyces cerevisiae using constraint-based docking

Set1C is a histone methyltransferase playing an important role in yeast gene regulation. Modeling the structure of this eight-subunit protein complex is an important open problem to further elucidate its functional mechanism. Recently, there has been progress in modeling of larger complexes using constraints to restrict the combinatorial explosion in binary docking of subunits. Here, we model the subunits of Set1C and develop a constraint-based docking approach, which uses high-quality protein interaction as well as functional data to guide and constrain the combinatorial assembly procedure. We obtained 22 final models. The core complex consisting of the subunits Set1, Bre2, Sdc1 and Swd2 is conformationally conserved in over half of the models, thus, giving high confidence. We characterize these high-confidence and the lower confidence interfaces and discuss implications for the function of Set1C.

Computational docking of antibody-antigen complexes, opportunities and pitfalls illustrated by influenza hemagglutinin

Antibodies play an increasingly important role in both basic research and the pharmaceutical industry. Since their efficiency depends, in ultimate analysis, on their atomic interactions with an antigen, studying such interactions is important to understand how they function and, in the long run, to design new molecules with desired properties. Computational docking, the process of predicting the conformation of a complex from its separated components, is emerging as a fast and affordable technique for the structural characterization of antibody-antigen complexes. In this manuscript, we first describe the different computational strategies for the modeling of antibodies and docking of their complexes, and then predict the binding of two antibodies to the stalk region of influenza hemagglutinin, an important pharmaceutical target. The purpose is two-fold: on a general note, we want to illustrate the advantages and pitfalls of computational docking with a practical example, using different approaches and comparing the results to known experimental structures. On a more specific note, we want to assess if docking can be successful in characterizing the binding to the same influenza epitope of other antibodies with unknown structure, which has practical relevance for pharmaceutical and biological research. The paper clearly shows that some of the computational docking predictions can be very accurate, but the algorithm often fails to discriminate them from inaccurate solutions. It is of paramount importance, therefore, to use rapidly obtained experimental data to validate the computational results.

Biochemical and physiological characterization of a BLUF protein-EAL protein complex involved in blue light-dependent degradation of cyclic diguanylate in the purple bacterium Rhodopseudomonas palustris

Organisms adapt their physiologies in response to the quality and quantity of environmental light. Members of a recently identified photoreceptor protein family, BLUF domain proteins, use a flavin chromophore to sense blue light. Herein, we report that PapB, which contains a BLUF domain, controls the biofilm formation of the purple photosynthetic bacterium Rhodopseudomonas palustris. Purified PapB undergoes a typical BLUF-type photocycle, and light-excited PapB enhances the phosphodiesterase activity of the EAL domain protein, PapA, which degrades the second messenger, cyclic dimeric GMP (c-di-GMP). PapB directly interacts with PapA in vitro in a light-independent manner and induces a conformational change in the preformed PapA-PapB complex. A PapA-PapB docking simulation, as well as a site-directed mutagenesis study, identified amino acids partially responsible for the interaction between the PapA EAL domain and the two C-terminal α-helices of the PapB BLUF domain. Thus, the conformational change, which involves the C-terminal α-helices, transfers the flavin-sensed blue light signal to PapA. Deletion of papB in R. palustris enhances biofilm formation under high-intensity blue light conditions, indicating that PapB functions as a blue light sensor, which negatively regulates biofilm formation. These results demonstrate that R. palustris can control biofilm formation via a blue light-dependent modulation of its c-di-GMP level by the BLUF domain protein, PapB.

Performance of ZDOCK and ZRANK in CAPRI rounds 13-19

We report the performance of the ZDOCK and ZRANK algorithms in CAPRI rounds 13-19 and introduce a novel measure atom contact frequency (ACF). To compute ACF, we identify the residues that most often make contact with the binding partner in the complete set of ZDOCK predictions for each target. We used ACF to predict the interface of the proteins, which, in combination with the biological data available in the literature, is a valuable addition to our docking pipeline. Furthermore, we incorporated a straightforward and efficient clustering algorithm with two purposes: (1) to determine clusters of similar docking poses (corresponding to energy funnels) and (2) to remove redundancies from the final set of predictions. With these new developments, we achieved at least one acceptable prediction for targets 29 and 36, at least one medium-quality prediction for targets 41 and 42, and at least one high-quality prediction for targets 37 and 40; thus, we succeeded for six out of a total of 12 targets.

A novel class of protease targets of phosphatidylethanolamine-binding proteins (PEBP): a study of the acylpeptide hydrolase and the PEBP inhibitor from the archaeon Sulfolobus solfataricus

This work describes the identification and characterization of a Sulfolobus solfataricus acylpeptide hydrolase, named APEH(Ss), recognised as a new protease target of the endogenous PEBP inhibitor, SsCEI. APEH is one of the four members of the prolyl oligopeptidase (POP) family, which removes acylated amino acid residues from the N terminus of oligopeptides. APEH(Ss) is a cytosolic homodimeric protein with a molecular mass of 125 kDa. It displays a similar exopeptidase and endopeptidase activity to the homologous enzymes from Aeropyrum pernix and Pyrococcus horikoshii. Herein we demonstrate that SsCEI is the first PEBP protein found to efficiently inhibit APEH from both S. solfataricus and mammalian sources with IC(50) values in the nanomolar range. The 3D model of APEH(Ss) shows the typical structural features of the POP family including an N-terminal β-propeller and a C-terminal α/β hydrolase domain. Moreover, to gain insights into the binding mode of SsCEI toward APEH(Ss), a structural model of the inhibition complex is proposed, suggesting a mechanism of steric blockage on substrate access to the active site or on product release. Like other POP enzymes, APEH may constitute a new therapeutic target for the treatment of a number of pathologies and this study may represent a starting point for further medical research.

How different from random are docking predictions when ranked by scoring functions?

Docking algorithms predict the structure of protein-protein interactions. They sample the orientation of two unbound proteins to produce various predictions about their interactions, followed by a scoring step to rank the predictions. We present a statistical assessment of scoring functions used to rank near-native orientations, applying our statistical analysis to a benchmark dataset of decoys of protein-protein complexes and assessing the statistical significance of the outcome in the Critical Assessment of PRedicted Interactions (CAPRI) scoring experiment. A P value was assigned that depended on the number of near-native structures in the sampling. We studied the effect of filtering out redundant structures and tested the use of pair-potentials derived using ZDock and ZRank. Our results show that for many targets, it is not possible to determine when a successful reranking performed by scoring functions results merely from random choice. This analysis reveals that changes should be made in the design of the CAPRI scoring experiment. We propose including the statistical assessment in this experiment either at the preprocessing or the evaluation step.

Refining near-native protein-protein docking decoys by local resampling and energy minimization

How to refine a near-native structure to make it closer to its native conformation is an unsolved problem in protein-structure and protein-protein complex-structure prediction. In this article, we first test several scoring functions for selecting locally resampled near-native protein-protein docking conformations and then propose a computationally efficient protocol for structure refinement via local resampling and energy minimization. The proposed method employs a statistical energy function based on a Distance-scaled Ideal-gas REference state (DFIRE) as an initial filter and an empirical energy function EMPIRE (EMpirical Protein-InteRaction Energy) for optimization and re-ranking. Significant improvement of final top-1 ranked structures over initial near-native structures is observed in the ZDOCK 2.3 decoy set for Benchmark 1.0 (74% whose global rmsd reduced by 0.5 A or more and only 7% increased by 0.5 A or more). Less significant improvement is observed for Benchmark 2.0 (38% versus 33%). Possible reasons are discussed.

Analysis and prediction of protein quaternary structure

The quaternary structure (QS) of a protein is determined by measuring its molecular weight in solution. The data have to be extracted from the literature, and they may be missing even for proteins that have a crystal structure reported in the Protein Data Bank (PDB). The PDB and other databases derived from it report QS information that either was obtained from the depositors or is based on an analysis of the contacts between polypeptide chains in the crystal, and this frequently differs from the QS determined in solution.The QS of a protein can be predicted from its sequence using either homology or threading methods. However, a majority of the proteins with less than 30% sequence identity have different QSs. A model of the QS can also be derived by docking the subunits when their 3D structure is independently known, but the model is likely to be incorrect if large conformation changes take place when the oligomer assembles.

A gate-latch-lock mechanism for hormone signalling by abscisic acid receptors

Abscisic acid (ABA) is a ubiquitous hormone that regulates plant growth, development, and responses to environmental stresses. Its action is mediated by the PYR/PYL/RCAR family of START proteins, but it remains unclear how these receptors bind ABA and in turn, how hormone binding leads to inhibition of the downstream type 2C protein phosphatase (PP2C) effectors. Here we report crystal structures of apo and ABA-bound receptors as well as a ternary PYL2-ABA-PP2C complex. The apo receptors contain an open ligand-binding pocket flanked by a gate that closes in response to ABA via conformational changes in two highly conserved β-loops that serve as a gate and latch. Moreover, ABA-induced closure of the gate creates a surface that enables the receptor to dock into and competitively inhibit the PP2C active site. A conserved tryptophan in the PP2C inserts directly between the gate and latch, which functions to further lock the receptor in a closed conformation. Together, our results identify a conserved gate-latch-lock mechanism underlying ABA signaling.

Classification of heterodimer interfaces using docking models and construction of scoring functions for the complex structure prediction

Protein–protein docking simulations can provide the predicted complex structural models. In a docking simulation, several putative structural models are selected by scoring functions from an ensemble of many complex models. Scoring functions based on statistical analyses of heterodimers are usually designed to select the complex model with the most abundant interaction mode found among the known complexes, as the correct model. However, because the formation schemes of heterodimers are extremely diverse, a single scoring function does not seem to be sufficient to describe the fitness of the predicted models other than the most abundant interaction mode. Thus, it is necessary to classify the heterodimers in terms of their individual interaction modes, and then to construct multiple scoring functions for each heterodimer type. In this study, we constructed the classification method of heterodimers based on the discriminative characters between near-native and decoy models, which were found in the comparison of the interfaces in terms of the complementarities for the hydrophobicity, the electrostatic potential and the shape. Consequently, we found four heterodimer clusters, and then constructed the multiple scoring functions, each of which was optimized for each cluster. Our multiple scoring functions were applied to the predictions in the unbound docking.

Decorin core protein (decoron) shape complements collagen fibril surface structure and mediates its binding

Decorin is the archetypal small leucine rich repeat proteoglycan of the vertebrate extracellular matrix (ECM). With its glycosaminoglycuronan chain, it is responsible for stabilizing inter-fibrillar organization. Type I collagen is the predominant member of the fibrillar collagen family, fulfilling both organizational and structural roles in animal ECMs. In this study, interactions between decoron (the decorin core protein) and binding sites in the d and e₁ bands of the type I collagen fibril were investigated through molecular modeling of their respective X-ray diffraction structures. Previously, it was proposed that a model-based, highly curved concave decoron interacts with a single collagen molecule, which would form extensive van der Waals contacts and give rise to strong non-specific binding. However, the large well-ordered aggregate that is the collagen fibril places significant restraints on modes of ligand binding and necessitates multi-collagen molecular contacts. We present here a relatively high-resolution model of the decoron-fibril collagen complex. We find that the respective crystal structures complement each other well, although it is the monomeric form of decoron that shows the most appropriate shape complementarity with the fibril surface and favorable calculated energies of interaction. One molecule of decoron interacts with four to six collagen molecules, and the binding specificity relies on a large number of hydrogen bonds and electrostatic interactions, primarily with the collagen motifs KXGDRGE and AKGDRGE (d and e₁ bands). This work helps us to understand collagen-decorin interactions and the molecular architecture of the fibrillar ECM in health and disease.