Protein-peptide Docking Research Articles

Применение многомерной квантильной функции в задаче пептид-белок докинга

The paper presents an exploration of using evolutionary optimization algorithms in protein-peptide docking. The main assumptions that reduce docking to the continuous global optimization problem are described. Some special features of the given problem and the difficulties of using evolutionary algorithms are discussed. The paper provides a way of using evolutionary optimization algorithms based on using empirical quantile function. The multivariate quantile function structure is defined recursively using univariate quantile transform. The grid-based approach of using quantile function is presented. The disadvantages of this approach are indicated. The deterministic sampling algorithm is proposed. The used scheme of parallel sampling and the resulting speed-up are described. The GPU-accelerated approach for quantile function evaluation is presented. This paper provides multiple GPU-based ways which use a sample in explicit form. Their speed-up depending on sample size is shown. The paper presents the results of docking using an evolutionary algorithm and its quantile-function-based modification. The comparison with the relevant docking method within a particular force-field is made. The results of the experiments are analyzed.

An anticoagulant peptide from Porphyra yezoensis inhibits the activity of factor XIIa: In vitro and in silico analysis

Thrombosis represents a major cause of morbidity and mortality around the world. Peptides isolated from natural sources have been proven to have anticoagulant and antithrombotic properties. VITPOR AI, a 16-mer peptide, isolated from Porphyra yezoensis was reported to have anticoagulant property. In this study, the coagulation factor XIIa activity in the presence of VITPOR AI was determined. Molecular modelling was performed to investigate the interaction between peptide and FXIIa. The structure of the peptide was predicted using PEP-FOLD3 server and simulated by molecular dynamics (MD) using GROMACS package. Molecular docking was carried out using peptide-protein docking software, pepATTRACT and its stability was confirmed by MD simulations. The chromogenic substrate assay revealed that the peptide inhibited the amidolytic activity of FXIIa with IC50 of 70.24 μM. The docking result showed peptide interactions through hydrogen bonds with Pro 96, Tyr 99, Glu 146, Gly 193 and Ser 195 of FXIIa. The MD simulation demonstrated that the peptide's binding with the FXIIa was stable as it did not move away from its binding region throughout the simulation period of 100 ns Moreover, MM/PBSA analysis also indicated a stable binding between the protein and peptide. These results suggest that the inhibition of the FXIIa activity might be due to binding of the peptide to oxyanion hole of the catalytic site. Thus, VITPOR AI could be explored as a potent anticoagulant which inhibits only intrinsic pathway of coagulation cascade but does not affect the extrinsic pathway.

CABS-dock standalone: a toolbox for flexible protein-peptide docking.

SummaryCABS-dock standalone is a multiplatform Python package for protein–peptide docking with backbone flexibility. The main feature of the CABS-dock method is its ability to simulate significant backbone flexibility of the entire protein–peptide system in a reasonable computational time. In the default mode, the package runs a simulation of fully flexible peptide searching for a binding site on the surface of a flexible protein receptor. The flexibility level of the molecules may be defined by the user. Furthermore, the CABS-dock standalone application provides users with full control over the docking simulation from the initial setup to the analysis of results. The standalone version is an upgrade of the original web server implementation—it introduces a number of customizable options, provides support for large-sized systems and offers a framework for deeper analysis of docking results.Availability and implementationCABS-dock standalone is distributed under the MIT licence, which is free for academic and non-profit users. It is implemented in Python and Fortran. The CABS-dock standalone source code, wiki with documentation and examples of use and installation instructions for Linux, macOS and Windows are available in the CABS-dock standalone repository at https://bitbucket.org/lcbio/cabsdock.

Benchmarking of different molecular docking methods for protein-peptide docking

BackgroundMolecular docking studies on protein-peptide interactions are a challenging and time-consuming task because peptides are generally more flexible than proteins and tend to adopt numerous conformations. There are several benchmarking studies on protein-protein, protein-ligand and nucleic acid-ligand docking interactions. However, a series of docking methods is not rigorously validated for protein-peptide complexes in the literature. Considering the importance and wide application of peptide docking, we describe benchmarking of 6 docking methods on 133 protein-peptide complexes having peptide length between 9 to 15 residues. The performance of docking methods was evaluated using CAPRI parameters like FNAT, I-RMSD, L-RMSD.ResultFirstly, we performed blind docking and evaluate the performance of the top docking pose of each method. It was observed that FRODOCK performed better than other methods with average L-RMSD of 12.46 Å. The performance of all methods improved significantly for their best docking pose and achieved highest average L-RMSD of 3.72 Å in case of FRODOCK. Similarly, we performed re-docking and evaluated the performance of the top and best docking pose of each method. We achieved the best performance in case of ZDOCK with average L-RMSD 8.60 Å and 2.88 Å for the top and best docking pose respectively. Methods were also evaluated on 40 protein-peptide complexes used in the previous benchmarking study, where peptide have length up to 5 residues. In case of best docking pose, we achieved the highest average L-RMSD of 4.45 Å and 2.09 Å for the blind docking using FRODOCK and re-docking using AutoDock Vina respectively.ConclusionThe study shows that FRODOCK performed best in case of blind docking and ZDOCK in case of re-docking. There is a need to improve the ranking of docking pose generated by different methods, as the present ranking scheme is not satisfactory. To facilitate the scientific community for calculating CAPRI parameters between native and docked complexes, we developed a web-based service named PPDbench (http://webs.iiitd.edu.in/raghava/ppdbench/).

Modeling of Disordered Protein Structures Using Monte Carlo Simulations and Knowledge-Based Statistical Force Fields.

The description of protein disordered states is important for understanding protein folding mechanisms and their functions. In this short review, we briefly describe a simulation approach to modeling protein interactions, which involve disordered peptide partners or intrinsically disordered protein regions, and unfolded states of globular proteins. It is based on the CABS coarse-grained protein model that uses a Monte Carlo (MC) sampling scheme and a knowledge-based statistical force field. We review several case studies showing that description of protein disordered states resulting from CABS simulations is consistent with experimental data. The case studies comprise investigations of protein–peptide binding and protein folding processes. The CABS model has been recently made available as the simulation engine of multiscale modeling tools enabling studies of protein–peptide docking and protein flexibility. Those tools offer customization of the modeling process, driving the conformational search using distance restraints, reconstruction of selected models to all-atom resolution, and simulation of large protein systems in a reasonable computational time. Therefore, CABS can be combined in integrative modeling pipelines incorporating experimental data and other modeling tools of various resolution.

Prediction of GluN2B-CT1290-1310/DAPK1 Interaction by Protein⁻Peptide Docking and Molecular Dynamics Simulation.

The interaction of death-associated protein kinase 1 (DAPK1) with the 2B subunit (GluN2B) C-terminus of N-methyl-D-aspartate receptor (NMDAR) plays a critical role in the pathophysiology of depression and is considered a potential target for the structure-based discovery of new antidepressants. However, the 3D structures of C-terminus residues 1290–1310 of GluN2B (GluN2B-CT1290-1310) remain elusive and the interaction between GluN2B-CT1290-1310 and DAPK1 is unknown. In this study, the mechanism of interaction between DAPK1 and GluN2B-CT1290-1310 was predicted by computational simulation methods including protein–peptide docking and molecular dynamics (MD) simulation. Based on the equilibrated MD trajectory, the total binding free energy between GluN2B-CT1290-1310 and DAPK1 was computed by the mechanics generalized born surface area (MM/GBSA) approach. The simulation results showed that hydrophobic, van der Waals, and electrostatic interactions are responsible for the binding of GluN2B-CT1290–1310/DAPK1. Moreover, through per-residue free energy decomposition and in silico alanine scanning analysis, hotspot residues between GluN2B-CT1290-1310 and DAPK1 interface were identified. In conclusion, this work predicted the binding mode and quantitatively characterized the protein–peptide interface, which will aid in the discovery of novel drugs targeting the GluN2B-CT1290-1310 and DAPK1 interface.

A comprehensive in silico analysis for identification of therapeutic epitopes in HPV16, 18, 31 and 45 oncoproteins.

Human papillomaviruses (HPVs) are a group of circular double-stranded DNA viruses, showing severe tropism to mucosal tissues. A subset of HPVs, especially HPV16 and 18, are the primary etiological cause for several epithelial cell malignancies, causing about 5.2% of all cancers worldwide. Due to the high prevalence and mortality, HPV-associated cancers have remained as a significant health problem in human society, making an urgent need to develop an effective therapeutic vaccine against them. Achieving this goal is primarily dependent on the identification of efficient tumor-associated epitopes, inducing a robust cell-mediated immune response. Previous information has shown that E5, E6, and E7 early proteins are responsible for the induction and maintenance of HPV-associated cancers. Therefore, the prediction of major histocompatibility complex (MHC) class I T cell epitopes of HPV16, 18, 31 and 45 oncoproteins was targeted in this study. For this purpose, a two-step plan was designed to identify the most probable CD8+ T cell epitopes. In the first step, MHC-I and II binding, MHC-I processing, MHC-I population coverage and MHC-I immunogenicity prediction analyses, and in the second step, MHC-I and II protein-peptide docking, epitope conservation, and cross-reactivity with host antigens’ analyses were carried out successively by different tools. Finally, we introduced five probable CD8+ T cell epitopes for each oncoprotein of the HPV genotypes (60 epitopes in total), which obtained better scores by an integrated approach. These predicted epitopes are valuable candidates for in vitro or in vivo therapeutic vaccine studies against the HPV-associated cancers. Additionally, this two-step plan that each step includes several analyses to find appropriate epitopes provides a rational basis for DNA- or peptide-based vaccine development.

Protein-peptide docking using CABS-dock and contact information.

CABS-dock is a computational method for protein–peptide molecular docking that does not require predefinition of the binding site. The peptide is treated as fully flexible, while the protein backbone undergoes small fluctuations and, optionally, large-scale rearrangements. Here, we present a specific CABS-dock protocol that enhances the docking procedure using fragmentary information about protein–peptide contacts. The contact information is used to narrow down the search for the binding peptide pose to the proximity of the binding site. We used information on a single-chosen and randomly chosen native protein–peptide contact to validate the protocol on the peptiDB benchmark. The contact information significantly improved CABS-dock performance. The protocol has been made available as a new feature of the CABS-dock web server (at http://biocomp.chem.uw.edu.pl/CABSdock/).Short abstractCABS-dock is a tool for flexible docking of peptides to proteins. In this article, we present a protocol for CABS-dock docking driven by information about protein–peptide contact(s). Using information on individual protein–peptide contacts allows to improve the accuracy of CABS-dock docking.

Protein-small molecule docking with receptor flexibility in iMOLSDOCK.

We have earlier reported the iMOLSDOCK technique to perform 'induced-fit' peptide-protein docking. iMOLSDOCK uses the mutually orthogonal Latin squares (MOLSs) technique to sample the conformation and the docking pose of the small molecule ligand and also the flexible residues of the receptor protein, and arrive at the optimum pose and conformation. In this paper we report the extension carried out in iMOLSDOCK to dock nonpeptide small molecule ligands to receptor proteins. We have benchmarked and validated iMOLSDOCK with a dataset of 34 protein-ligand complexes as well as with Astex Diverse dataset, with nonpeptide small molecules as ligands. We have also compared iMOLSDOCK with other flexible receptor docking tools GOLD v5.2.1 and AutoDock Vina. The results obtained show that the method works better than these two algorithms, though it consumes more computer time. The source code and binary of MOLS 2.0 (under a GNU Lesser General Public License) are freely available for download at https://sourceforge.net/projects/mols2-0/files/ .

Discovery of a novel potent peptide agonist to adiponectin receptor 1.

Activation of adiponectin receptors (AdipoRs) by its natural ligand, adiponectin has been known to be involved in modulating critical metabolic processes such as glucose metabolism and fatty acid oxidation as demonstrated by a number of in vitro and in vivo studies over last two decades. These findings suggest that AdipoRs’ agonists could be developed into a potential therapeutic agent for metabolic diseases, such as diabetes mellitus, especially for type II diabetes, a long-term metabolic disorder characterized by high blood sugar, insulin resistance, and relative lack of insulin. Because of limitations in production of biologically active adiponectin, adiponectin-mimetic AdipoRs’ agonists have been suggested as alternative ways to expand the opportunity to develop anti-diabetic agents. Based on crystal structure of AdipoR1, we designed AdipoR1’s peptide agonists using protein-peptide docking simulation and screened their receptor binding abilities and biological functions via surface plasmon resonance (SPR) and biological analysis. Three candidate peptides, BHD1028, BHD43, and BHD44 were selected and confirmed to activate AdipoR1-mediated signal pathways. In order to enhance the stability and solubility of peptide agonists, candidate peptides were PEGylated. PEGylated BHD1028 exhibited its biological activity at nano-molar concentration and could be a potential therapeutic agent for the treatment of diabetes. Also, SPR and virtual screening techniques utilized in this study may potentially be applied to other peptide-drug screening processes against membrane receptor proteins.

HPEPDOCK: a web server for blind peptide-protein docking based on a hierarchical algorithm.

Protein–peptide interactions are crucial in many cellular functions. Therefore, determining the structure of protein–peptide complexes is important for understanding the molecular mechanism of related biological processes and developing peptide drugs. HPEPDOCK is a novel web server for blind protein–peptide docking through a hierarchical algorithm. Instead of running lengthy simulations to refine peptide conformations, HPEPDOCK considers the peptide flexibility through an ensemble of peptide conformations generated by our MODPEP program. For blind global peptide docking, HPEPDOCK obtained a success rate of 33.3% in binding mode prediction on a benchmark of 57 unbound cases when the top 10 models were considered, compared to 21.1% for pepATTRACT server. HPEPDOCK also performed well in docking against homology models and obtained a success rate of 29.8% within top 10 predictions. For local peptide docking, HPEPDOCK achieved a high success rate of 72.6% on a benchmark of 62 unbound cases within top 10 predictions, compared to 45.2% for HADDOCK peptide protocol. Our HPEPDOCK server is computationally efficient and consumed an average of 29.8 mins for a global peptide docking job and 14.2 mins for a local peptide docking job. The HPEPDOCK web server is available at http://huanglab.phys.hust.edu.cn/hpepdock/.

Hierarchical Flexible Peptide Docking by Conformer Generation and Ensemble Docking of Peptides.

Given the importance of peptide-mediated protein interactions in cellular processes, protein-peptide docking has received increasing attention. Here, we have developed a Hierarchical flexible Peptide Docking approach through fast generation and ensemble docking of peptide conformations, which is referred to as HPepDock. Tested on the LEADS-PEP benchmark data set of 53 diverse complexes with peptides of 3-12 residues, HPepDock performed significantly better than the 11 docking protocols of five small-molecule docking programs (DOCK, AutoDock, AutoDock Vina, Surflex, and GOLD) in predicting near-native binding conformations. HPepDock was also evaluated on the 19 bound/unbound and 10 unbound/unbound protein-peptide complexes of the Glide SP-PEP benchmark and showed an overall better performance than Glide SP-PEP+MM-GBSA and FlexPepDock in both bound and unbound docking. HPepDock is computationally efficient, and the average running time for docking a peptide is ∼15 min with the range from about 1 min for short peptides to around 40 min for long peptides.

A novel approach of virulome based reverse vaccinology for exploring and validating peptide-based vaccine candidates against the most troublesome nosocomial pathogen: Acinetobacter baumannii

Acinetobacter baumannii is one of the major cause of nosocomial infections around the globe. The emergence of hyper-virulent strains of the pathogen greatly narrows down therapeutic options for patients infected with this red alert superbug. Development of a peptide-based vaccine can offers an alternative, attractive, and cost-effective remedy for multidrug-resistant A. baumannii associated complications. Herein, we introduced a novel virulome based Reverse Vaccinology for screening peptide based vaccine candidates against A. baumannii and its validation using a negative control. The pipeline screened “FYLNDQPVS” of polysaccharide export outer membrane protein (EpsA) and “LQNNTRRMK” of chaperone-usher pathway protein B (CsuB) as broad-spectrum peptides for induction of targeted immune responses. The 9-mer epitope of both proteins was rendered virulent, antigenic, non-allergen, and highly conserved among thirty-four completely annotated strains. Interactome examination unravels peptides protein direct and indirect interactions with biological significant pathways, essential for A. baumannii pathogenesis and survival. Protein-peptide docking aids in addition by unveiling deep binding of the epitopes in the active site of the most prevalent binding allele in the human population-the DRB1*0101. Both the proteins till to date are not characterized for immunoprotective efficacy and desirable to be deciphered experimentally. The designed series of in silico filters rejected few recently reported peptide and non-peptide vaccine targets and has delivered outcomes, which we believe will enrich the existing knowledge of vaccinology against this life-threatening human pathogen.

Parallel Evolutionary Optimization Algorithms for Peptide-Protein Docking

In this study we examine the possibility of using evolutionary optimization algorithms in protein-peptide docking. We present the main assumptions that reduce the docking problem to a continuous global optimization problem and provide a way of using evolutionary optimization algorithms. The Rosetta all-atom force field was used for structural representation and energy scoring. We describe the parallelization scheme and MPI/OpenMP realization of the considered algorithms. We demonstrate the efficiency and the performance for some algorithms which were applied to a set of benchmark tests.

COMPUTATIONAL APPROACHES FOR THE PREDICTION OF ANTIMICROBIAL POTENTIAL PEPTIDES FROM OCIMUM TENUIFLORUM.

Objective: In this study, antimicrobial activity was predicted against novel antimicrobial target 1SDE receptor to understand the structural feature of predicted peptides using machine learning approach from Ocimum tenuiflorum. Methods: Protein sequences from O. tenuiflorum were digested using peptide cutter and further processed for the prediction of antimicrobial peptide (AMP) through AMP predictor tool of CAMP which have multidimensional algorithms such as support vector machine, artificial neural network, random forest, and discriminant analysis. After analyzing various peptides, only four peptides were predicted as antimicrobial in nature. Furthermore, the three-dimensional structure of different potential peptides was generated with the help of Pepfold-3.0 server followed by protein-peptide docking studies with novel target receptor with the help of PatchDock, FireDock webserver, and Hex 8.0 software. Interactions were further visualized using Discovery Studio Client 2.5 software tool.Results: This study revealed that peptide 2 resulted higher score in PatchDock and FireDock and also Hex 8.0 provides E total value of −430.18 which is higher than that of peptide 1 with −381.07, peptide 3 with −416.86, and peptide 4 with −407.94.Conclusion: The peptide predicted in this study has potential to act as effective AMP against target receptor and also utilize other antimicrobial target.

COMPUTATIONAL APPROACHES FOR THE PREDICTION OF ANTIMICROBIAL POTENTIAL PEPTIDES FROM OCIMUM TENUIFLORUM.

Objective: In this study, antimicrobial activity was predicted against novel antimicrobial target 1SDE receptor to understand the structural feature of predicted peptides using machine learning approach from Ocimum tenuiflorum. Methods: Protein sequences from O. tenuiflorum were digested using peptide cutter and further processed for the prediction of antimicrobial peptide (AMP) through AMP predictor tool of CAMP which have multidimensional algorithms such as support vector machine, artificial neural network, random forest, and discriminant analysis. After analyzing various peptides, only four peptides were predicted as antimicrobial in nature. Furthermore, the three-dimensional structure of different potential peptides was generated with the help of Pepfold-3.0 server followed by protein-peptide docking studies with novel target receptor with the help of PatchDock, FireDock webserver, and Hex 8.0 software. Interactions were further visualized using Discovery Studio Client 2.5 software tool.Results: This study revealed that peptide 2 resulted higher score in PatchDock and FireDock and also Hex 8.0 provides E total value of −430.18 which is higher than that of peptide 1 with −381.07, peptide 3 with −416.86, and peptide 4 with −407.94.Conclusion: The peptide predicted in this study has potential to act as effective AMP against target receptor and also utilize other antimicrobial target.

High-resolution global peptide-protein docking using fragments-based PIPER-FlexPepDock.

Peptide-protein interactions contribute a significant fraction of the protein-protein interactome. Accurate modeling of these interactions is challenging due to the vast conformational space associated with interactions of highly flexible peptides with large receptor surfaces. To address this challenge we developed a fragment based high-resolution peptide-protein docking protocol. By streamlining the Rosetta fragment picker for accurate peptide fragment ensemble generation, the PIPER docking algorithm for exhaustive fragment-receptor rigid-body docking and Rosetta FlexPepDock for flexible full-atom refinement of PIPER docked models, we successfully addressed the challenge of accurate and efficient global peptide-protein docking at high-resolution with remarkable accuracy, as validated on a small but representative set of peptide-protein complex structures well resolved by X-ray crystallography. Our approach opens up the way to high-resolution modeling of many more peptide-protein interactions and to the detailed study of peptide-protein association in general. PIPER-FlexPepDock is freely available to the academic community as a server at http://piperfpd.furmanlab.cs.huji.ac.il.

Understanding Russell's viper venom factor V activator's substrate specificity by surface plasmon resonance and in-silico studies.

Blood coagulation factor V (FV) is activated either by Factor X or thrombin, cleaving at three different sites viz., Site I (Arg709-Ser710), site II (Arg1018-Thr1019), and site III (Arg1545-Ser1546). Russell’s viper venom factor V activator (RVV-V) is a thrombin-like serine proteinase that activates FV with selective, single cleavage at site III. A long lasting effort is being pending in understanding the ‘selective’ binding specificity of the RVV-V towards site III. Here, we present the binding kinetic study of RVV-V with two designed peptides corresponding to the regions from site I (Gln699—Asn713) and site II (1008Lys—Pro1022), respectively, that include 15 amino acids. Our investigation for justifying the binding efficacy and kinetics of peptides includes SPR method, protein-peptide docking, molecular dynamics simulation, and principal component analysis (PCA). Surprisingly, the SPR experiment disclosed that the Peptide II showed a lower binding affinity with KD of 2.775 mM while the Peptide I showed none. Docking and simulation of both the peptides with RVV-V engaged either rooted or shallow binding for Peptide II and Peptide I respectively. The peptide binding resulted in global conformational changes in the native fold of RVV-V, whereas the similar studies for thrombin failed to make major changes in the native fold. In support, the PCA analysis for RVV-V showed the dislocation of catalytic triad upon binding both the peptides. Hence, RVV-V, a serine protease, is incompetent in cleaving these two sites. This study suggests a transition in RVV-V from the native rigid to the distorted flexible structure and paves a way to design a new peptide substrate/inhibitor.

The pepATTRACT web server for blind, large-scale peptide-protein docking.

Peptide–protein interactions are ubiquitous in the cell and form an important part of the interactome. Computational docking methods can complement experimental characterization of these complexes, but current protocols are not applicable on the proteome scale. pepATTRACT is a novel docking protocol that is fully blind, i.e. it does not require any information about the binding site. In various stages of its development, pepATTRACT has participated in CAPRI, making successful predictions for five out of seven protein–peptide targets. Its performance is similar or better than state-of-the-art local docking protocols that do require binding site information. Here we present a novel web server that carries out the rigid-body stage of pepATTRACT. On the peptiDB benchmark, the web server generates a correct model in the top 50 in 34% of the cases. Compared to the full pepATTRACT protocol, this leads to some loss of performance, but the computation time is reduced from ∼18 h to ∼10 min. Combined with the fact that it is fully blind, this makes the web server well-suited for large-scale in silico protein–peptide docking experiments. The rigid-body pepATTRACT server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/pepATTRACT.

Modeling disordered protein interactions from biophysical principles.

Disordered protein-protein interactions (PPIs), those involving a folded protein and an intrinsically disordered protein (IDP), are prevalent in the cell, including important signaling and regulatory pathways. IDPs do not adopt a single dominant structure in isolation but often become ordered upon binding. To aid understanding of the molecular mechanisms of disordered PPIs, it is crucial to obtain the tertiary structure of the PPIs. However, experimental methods have difficulty in solving disordered PPIs and existing protein-protein and protein-peptide docking methods are not able to model them. Here we present a novel computational method, IDP-LZerD, which models the conformation of a disordered PPI by considering the biophysical binding mechanism of an IDP to a structured protein, whereby a local segment of the IDP initiates the interaction and subsequently the remaining IDP regions explore and coalesce around the initial binding site. On a dataset of 22 disordered PPIs with IDPs up to 69 amino acids, successful predictions were made for 21 bound and 18 unbound receptors. The successful modeling provides additional support for biophysical principles. Moreover, the new technique significantly expands the capability of protein structure modeling and provides crucial insights into the molecular mechanisms of disordered PPIs.