Protein-peptide Complexes Research Articles

Cyclization and Docking Protocol for Cyclic Peptide-Protein Modeling Using HADDOCK2.4.

An emerging class of therapeutic molecules are cyclic peptides with over 40 cyclic peptide drugs currently in clinical use. Their mode of action is, however, not fully understood, impeding rational drug design. Computational techniques could positively impact their design, but modeling them and their interactions remains challenging due to their cyclic nature and their flexibility. This study presents a step-by-step protocol for generating cyclic peptide conformations and docking them to their protein target using HADDOCK2.4. A dataset of 30 cyclic peptide–protein complexes was used to optimize both cyclization and docking protocols. It supports peptides cyclized via an N- and C-terminus peptide bond and/or a disulfide bond. An ensemble of cyclic peptide conformations is then used in HADDOCK to dock them onto their target protein using knowledge of the binding site on the protein side to drive the modeling. The presented protocol predicts at least one acceptable model according to the critical assessment of prediction of interaction criteria for each complex of the dataset when the top 10 HADDOCK-ranked single structures are considered (100% success rate top 10) both in the bound and unbound docking scenarios. Moreover, its performance in both bound and fully unbound docking is similar to the state-of-the-art software in the field, Autodock CrankPep. The presented cyclization and docking protocol should make HADDOCK a valuable tool for rational cyclic peptide-based drug design and high-throughput screening.

InterPepScore: a deep learning score for improving the FlexPepDock refinement protocol.

MotivationInteractions between peptide fragments and protein receptors are vital to cell function yet difficult to experimentally determine in structural details of. As such, many computational methods have been developed to aid in peptide–protein docking or structure prediction. One such method is Rosetta FlexPepDock which consistently refines coarse peptide–protein models into sub-Ångström precision using Monte-Carlo simulations and statistical potentials. Deep learning has recently seen increased use in protein structure prediction, with graph neural networks used for protein model quality assessment.ResultsHere, we introduce a graph neural network, InterPepScore, as an additional scoring term to complement and improve the Rosetta FlexPepDock refinement protocol. InterPepScore is trained on simulation trajectories from FlexPepDock refinement starting from thousands of peptide–protein complexes generated by a wide variety of docking schemes. The addition of InterPepScore into the refinement protocol consistently improves the quality of models created, and on an independent benchmark on 109 peptide–protein complexes its inclusion results in an increase in the number of complexes for which the top-scoring model had a DockQ-score of 0.49 (Medium quality) or better from 14.8% to 26.1%.Availability and implementationInterPepScore is available online at http://wallnerlab.org/InterPepScore.Supplementary information Supplementary data are available at Bioinformatics online.

Tertiary motifs as building blocks for the design of protein-binding peptides.

Despite advances in protein engineering, the de novo design of small proteins or peptides that bind to a desired target remains a difficult task. Most computational methods search for binder structures in a library of candidate scaffolds, which can lead to designs with poor target complementarity and low success rates. Instead of choosing from pre‐defined scaffolds, we propose that custom peptide structures can be constructed to complement a target surface. Our method mines tertiary motifs (TERMs) from known structures to identify surface‐complementing fragments or “seeds.” We combine seeds that satisfy geometric overlap criteria to generate peptide backbones and score the backbones to identify the most likely binding structures. We found that TERM‐based seeds can describe known binding structures with high resolution: the vast majority of peptide binders from 486 peptide‐protein complexes can be covered by seeds generated from single‐chain structures. Furthermore, we demonstrate that known peptide structures can be reconstructed with high accuracy from peptide‐covering seeds. As a proof of concept, we used our method to design 100 peptide binders of TRAF6, seven of which were predicted by Rosetta to form higher‐quality interfaces than a native binder. The designed peptides interact with distinct sites on TRAF6, including the native peptide‐binding site. These results demonstrate that known peptide‐binding structures can be constructed from TERMs in single‐chain structures and suggest that TERM information can be applied to efficiently design novel target‐complementing binders.

Matching protein surface structural patches for high-resolution blind peptide docking

Peptide docking can be perceived as a subproblem of protein–protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about peptide sequences to generate representative conformer ensembles, which can then be rigid-body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely, that the protein surface defines the peptide-bound conformation. Here, we present PatchMAN (Patch-Motif AligNments), a global peptide-docking approach that uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a nonredundant set of protein–peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide–protein complexes in 58%/84% within 2.5 Å/5 Å interface backbone RMSD, with corresponding sampling in 81%/100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the bound peptide conformation is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide–protein association, and to the design of new peptide binders. PatchMAN is available as a server at https://furmanlab.cs.huji.ac.il/patchman/.

Effect of various factors on ejaculate fertility in vitro

Introduction. Current demographic situation in Russia is characterized by decreasing birth rate. According to the World Health Organization, percentage of child-free marriages in various countries is 10–15 %. In Russia, the National Medical Research Center for Obstetrics, Gynecology, and Perinatology named after Academician V.I. Kulakov states that this number is 17 %. More than 4 million men suffer from infertility of various types. In recent years, pathologies of male reproductive function have achieved medical and social significance due to progressively decreasing sperm fertility. Fertility disorders are considered multi-factor conditions caused by internal and external factors and leading to pathological changes in sex organs. Correction of pathologies of male fertility does not always lead to positive results. Therefore, it is important to develop and study effective pharmaceuticals affecting the main fertility parameters.The study objective is to investigate the effect of various biological pharmaceuticals, physical and chemical factors on parameters of ejaculate fertility in vitro.Materials and methods. Experiments with a protein-peptide complex (PPC), methylene blue, and hydrogen peroxide were performed on human semen. After semen dilution, the sample was studied under the microscope and sperm motility and other ejaculate parameters were evaluated per the 5th edition WHO standard. Experiments were performed at 20–22 ºС. Statistical data analysis was performed using the Student’s t-test. Differences were considered statistically significant at р <0.05.Results. The results show increased motility in the fraction of active motile sperm in first 30 minutes after incubation with methylene blue. In case of initial asthenospermia, active motility increased by 72 %, in case of normospermia by 89 %. After 2 hours, all motility fractions were at the baseline level.The experiments also showed significant changes in sperm motility in the presence of PPC preparation in the ejaculate: increase in sperm motility was observed beginning at 30-minute mark and this level persisted through 3 hours of observation. A more pronounced change, by 60 %, was observed in the active motile sperm; total motility increased by up to 30 %. After 24 hours, sperm motility remained close to the baseline level, the number of normal sperm forms and live cells did not change. Dependence of the motility change on the preparation concentration should be noted: the highest increase was observed at PPC concentration with total protein level of 10–12 mg/mL. Higher concentrations did not have a positive effect on sperm motility. In experiments with low hydrogen peroxide concentration, a positive effect on sperm motility was observed.Conclusions. Sperm motility is supported by glycolysis energy, and one of the glycolysis enzymes glyceraldehyde 3-phosphate dehydrogenase is tightly bound to the fibrous layer of the flagellum, activation of metabolic pathways leading to increased enzyme activity, increased sperm motility. The mechanism of the observed effect is not entirely clear, however, the obtained data demonstrate potential benefits of further studies on use of pharmaceuticals in andrological and reproductive practice, assisted reproductive technologies, as well as for stimulation of sperm motility in further experimental studies.

HAMdetector: a Bayesian regression model that integrates information to detect HLA-associated mutations.

A key process in anti-viral adaptive immunity is that the human leukocyte antigen (HLA) system presents epitopes as major histocompatibility complex I (MHC I) protein-peptide complexes on cell surfaces and in this way alerts CD8+ cytotoxic T-lymphocytes (CTLs). This pathway exerts strong selection pressure on viruses, favoring viral mutants that escape recognition by the HLA/CTL system. Naturally, such immune escape mutations often emerge in highly variable viruses, e.g. HIV or HBV, as HLA-associated mutations (HAMs), specific to the hosts MHC I proteins. The reliable identification of HAMs is not only important for understanding viral genomes and their evolution, but it also impacts the development of broadly effective anti-viral treatments and vaccines against variable viruses. By their very nature, HAMs are amenable to detection by statistical methods in paired sequence/HLA data. However, HLA alleles are very polymorphic in the human host population which makes the available data relatively sparse and noisy. Under these circumstances, one way to optimize HAM detection is to integrate all relevant information in a coherent model. Bayesian inference offers a principled approach to achieve this. We present a new Bayesian regression model for the detection of HAMs that integrates a sparsity-inducing prior, epitope predictions and phylogenetic bias assessment, and that yields easily interpretable quantitative information on HAM candidates. The model predicts experimentally confirmed HAMs as having high posterior probabilities, and it performs well in comparison to state-of-the-art models for several datasets from individuals infected with HBV, HDV and HIV. The source code of this software is available at https://github.com/HAMdetector/Escape.jl under a permissive MIT license. The data underlying this article were provided by permission. Data will be shared on request to the corresponding author with permission of the respective co-authors. Supplementary data are available at Bioinformatics online.

Designing Anti-Microbial Peptides Against Major β-Lactamase Enzymes in Clinically Important Gram-Negative Bacterial Pathogens: An In-Silico Study.

Anti-microbial resistance (AMR) creating healthcare concerns worldwide requires ardent exploration of therapeutic alternatives. Although anti-microbial peptides (AMP) are popular for broad-spectrum activity, recent evidence of increasing resistance to membrane-acting AMPs by ESKAPE pathogens has compelled us to design novel AMPs as therapeutic candidates. A library of 60 AMPs comprising natural AMPs and their mutants was constructed through in-silico methods. After physico-chemical property evaluations, each peptide in the library was subjected to flexible molecular docking against four major β-lactamases in Gram-negative ESKAPE pathogens. Among the potent AMP mutants, a Lactoferricin B-Mutant (M4) possessed uniformly high affinity with SHV1, OXA48, NDM1, and AmpC having energies -842.0Kcal/mol, -774.8Kcal/mol, -1103.3Kcal/mol, and -858.8Kcal/mol respectively. Coarse-grained clustering and flexibility analysis further accounted for the residue-level stable configurations of the protein-peptide complexes with high affinity. Highest affinity of Lactoferricin B_M4 was found with NDM1 due to H-bonds, salt-bridges, and hydrophobic interactions with the metallo-β-lactamase domain including crucial active-site residue Asp124. Molecular dynamics simulation further confirmed the stability of Lactoferricin B_M4-NDM1 complex having low residue-level root-mean square deviations (RMSD), atomic-level fluctuations, and radius of gyration (Rg). The study encourages experimental validations and similar methods to identify potential AMPs against drug-resistant pathogens.

Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking

We present a combined computational approach to protein–ligand binding, which consists of two steps: (1) a deep neural network is used to locate a binding region on a target protein, and (2) molecular docking of a ligand is performed within the specified region to obtain the best pose using Autodock Vina. Our in-house designed neural network was trained using the PepBDB dataset. Although the training dataset consisted of protein-peptide complexes, we show that the approach is not limited to peptides, but also works remarkably well for a large class of non-peptide ligands. The results are compared with those in which the binding region (first step) was provided by Accluster. In cases where no prior experimental data on the binding region are available, our deep neural network provides a fast and effective alternative to classical software for its localization. Our code is available at https://github.com/mksmd/NNforDocking.

Insights into protein-DNA interactions from hydrogen bond energy-based comparative protein-ligand analyses.

Hydrogen bonds play important roles in protein folding and protein–ligand interactions, particularly in specific protein–DNA recognition. However, the distributions of hydrogen bonds, especially hydrogen bond energy (HBE) in different types of protein–ligand complexes, is unknown. Here we performed a comparative analysis of hydrogen bonds among three non‐redundant datasets of protein–protein, protein–peptide, and protein–DNA complexes. Besides comparing the number of hydrogen bonds in terms of types and locations, we investigated the distributions of HBE. Our results indicate that while there is no significant difference of hydrogen bonds within protein chains among the three types of complexes, interfacial hydrogen bonds are significantly more prevalent in protein–DNA complexes. More importantly, the interfacial hydrogen bonds in protein–DNA complexes displayed a unique energy distribution of strong and weak hydrogen bonds whereas majority of the interfacial hydrogen bonds in protein–protein and protein–peptide complexes are of predominantly high strength with low energy. Moreover, there is a significant difference in the energy distributions of minor groove hydrogen bonds between protein–DNA complexes with different binding specificity. Highly specific protein–DNA complexes contain more strong hydrogen bonds in the minor groove than multi‐specific complexes, suggesting important role of minor groove in specific protein–DNA recognition. These results can help better understand protein–DNA interactions and have important implications in improving quality assessments of protein–DNA complex models.

Getting Insights into Structural and Energetic Properties of Reciprocal Peptide-Protein Interactions.

Peptide-protein interactions play a key role for many cellular and metabolic processes involved in the onset of largely spread diseases such as cancer and neurodegenerative pathologies. Despite the progress in the structural characterization of peptide-protein interfaces, the in-depth knowledge of the molecular details behind their interactions is still a daunting task. Here, we present the first comprehensive in silico morphological and energetic study of peptide binding sites by focusing on both peptide and protein standpoints. Starting from the PixelDB database, a nonredundant benchmark collection of high-quality 3D crystallographic structures of peptide-protein complexes, a classification analysis of the most representative categories based on the nature of each cocrystallized peptide has been carried out. Several interpretable geometrical and energetic descriptors have been computed both from peptide and target protein sides in the attempt to unveil physicochemical and structural causative correlations. Finally, we investigated the most frequent peptide-protein residue pairs at the binding interface and made extensive energetic analyses, based on GRID MIFs, with the aim to study the peptide affinity-enhancing interactions to be further exploited in rational drug design strategies.

Molecular Docking and Dynamics Studies to Explore Effective Inhibitory Peptides Against the Spike Receptor Binding Domain of SARS-CoV-2.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a pandemic due to the high transmission and mortality rate of this virus. The world health and economic sectors have been severely affected by this deadly virus, exacerbated by the lack of sufficient efficient vaccines. The design of effective drug candidates and their rapid development is necessary to combat this virus. In this study, we selected 23 antimicrobial peptides from the literature and predicted their structure using PEP-FOLD 3.5. In addition, we docked them to the SARS-CoV-2 spike protein receptor-binding domain (RBD) to study their capability to inhibit the RBD, which plays a significant role in virus binding, fusion and entry into the host cell. We used several docking programs including HDOCK, HPEPDOCK, ClusPro, and HawkDock to calculate the binding energy of the protein-peptide complexes. We identified four peptides with high binding free energy and docking scores. The docking results were further verified by molecular dynamics (MD) simulations to characterize the protein-peptide complexes in terms of their root-mean-square fluctuation (RMSF), root-mean-square deviation (RMSD), radius of gyration (Rg), solvent-accessible surface area (SASA), and hydrogen bond formation. Allergenicity and toxicity predictions suggested that the peptides we identified were non-allergenic and non-toxic. This study suggests that these four antimicrobial peptides could inhibit the RBD of SARS-CoV-2. Future in vitro and in vivo studies are necessary to confirm this.

Prediction and Modeling of Protein-Protein Interactions Using "Spotted" Peptides with a Template-Based Approach.

Protein–peptide interactions (PpIs) are a subset of the overall protein–protein interaction (PPI) network in the living cell and are pivotal for the majority of cell processes and functions. High-throughput methods to detect PpIs and PPIs usually require time and costs that are not always affordable. Therefore, reliable in silico predictions represent a valid and effective alternative. In this work, a new algorithm is described, implemented in a freely available tool, i.e., “PepThreader”, to carry out PPIs and PpIs prediction and analysis. PepThreader threads multiple fragments derived from a full-length protein sequence (or from a peptide library) onto a second template peptide, in complex with a protein target, “spotting” the potential binding peptides and ranking them according to a sequence-based and structure-based threading score. The threading algorithm first makes use of a scoring function that is based on peptides sequence similarity. Then, a rerank of the initial hits is performed, according to structure-based scoring functions. PepThreader has been benchmarked on a dataset of 292 protein–peptide complexes that were collected from existing databases of experimentally determined protein–peptide interactions. An accuracy of 80%, when considering the top predicted 25 hits, was achieved, which performs in a comparable way with the other state-of-art tools in PPIs and PpIs modeling. Nonetheless, PepThreader is unique in that it is able at the same time to spot a binding peptide within a full-length sequence involved in PPI and model its structure within the receptor. Therefore, PepThreader adds to the already-available tools supporting the experimental PPIs and PpIs identification and characterization.

In Situ Self-Assembly of Bispecific Peptide for Cancer Immunotherapy.

Precise and effective manipulation of protein functions still faces tremendous challenges. Herein we report a programmable peptide molecule, consisted of targeting and self-assembly modules, that enables specific and highly efficient assembly governed by targeting receptor proteins. Upon binding to the cell membrane receptor, peptide conformation is somewhat stabilized along with decreased self-assembly activation energy, promoting peptide-protein complex oligomerization. We first design a GNNQQNY-RGD peptide (G7-RGD) to recognize integrin αV β3 receptor for proof-of-concept study. In the presence of αV β3 protein, the critical assembly concentration of free G7-RGD decreases from 525 to 33 μM and the resultant G7-RGD cluster drives integrin receptor oligomerization. Finally, a bispecific assembling peptide antiCD3-G7-RGD is rationally designed for cancer immunotherapy, which validates CD3 oligomerization and concomitant T cell activation, leading to T cell-mediated cancer cell cytolysis.

Harnessing protein folding neural networks for peptide\u2013protein docking

Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide–protein interactions. Our simple implementation of AlphaFold2 generates peptide–protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor. We explore what AlphaFold2 has memorized and learned, and describe specific examples that highlight differences compared to state-of-the-art peptide docking protocol PIPER-FlexPepDock. These results show that AlphaFold2 holds great promise for providing structural insight into a wide range of peptide–protein complexes, serving as a starting point for the detailed characterization and manipulation of these interactions.

Population Dynamics of Stretching Excitations of p-Azido-phenylalanine Incorporated in Calmodulin-Peptide Complexes.

We genetically incorporated the unnatural amino acid p-azido-phenylalanine (AzF) into the ubiquitous Ca2+ sensor protein calmodulin (CaM) in complex with different peptides to explore the response of the azido stretching line shape to varying binding motifs with femtosecond infrared spectroscopy. The dynamic response of the azido stretching mode varies in different CaM-peptide complexes. We model these dynamics as coherent excitations of Fermi resonances and extract a lifetime of the azido stretching vibration of about 1 ps. The resulting model parameters are commensurate with the linear infrared absorption lineshapes which suggests that the conformation-sensitive vibrational lineshape could be composed of Fermi resonances that differ between the protein-peptide complexes.

Drug and Anti-Viral Peptide Design to Inhibit the Monkeypox Virus by Restricting A36R Protein.

Most recently, monkeypox virus (MPXV) has emanated as a global public health threat. Unavailability of effective medicament against MPXV escalates demand for new therapeutic agent. In this study, in silico strategies were conducted to identify novel drug against the A36R protein of MPXV. The A36R protein of MPXV is responsible for the viral migration, adhesion, and vesicle trafficking to the host cell. To block the A36R protein, 4893 potential antiviral peptides (AVPs) were retrieved from DRAMP and SATPdb databases. Finally, 57 sequences were screened based on peptide filtering criteria, which were then modeled. Likewise, 31 monkeypox virus A36R protein sequences were collected from NCBI protein database to find consensus sequence and to predict 3D protein model. The refined and validated models of the A36R protein and AVP peptides were used to predict receptor-ligand interactions using DINC 2 server. Three peptides that showed best interactions were SATPdb10193, SATPdb21850, and SATPdb26811 with binding energies -6.10, -6.10, and -6.30 kcal/mol, respectively. Small molecules from drug databases were also used to perform virtual screening against the A36R protein. Among databases, Enamine-HTSC showed strong affinity with docking scores ranging from -8.8 to 9.8 kcal/mol. Interaction of target protein A36R with the top 3 peptides and the most probable drug (Z55287118) examined by molecular dynamic (MD) simulation. Trajectory analyses (RMSD, RMSF, SASA, and Rg) confirmed the stable nature of protein-ligand and protein-peptide complexes. This work suggests that identified top AVPs and small molecules might interfere with the function of the A36R protein of MPXV.

Modelling peptide-protein complexes: docking, simulations and machine learning.

Peptides mediate up to 40% of protein interactions, their high specificity and ability to bind in places where small molecules cannot make them potential drug candidates. However, predicting peptide-protein complexes remains more challenging than protein-protein or protein-small molecule interactions, in part due to the high flexibility peptides have. In this review, we look at the advances in docking, molecular simulations and machine learning to tackle problems related to peptides such as predicting structures, binding affinities or even kinetics. We specifically focus on explaining the number of docking programmes and force fields used in molecular simulations, so a prospective user can have an educated guess as to why choose one modelling tool or another to address their scientific questions.

Cyclic peptides as an inhibitor of metastasis in breast cancer targeting MMP-1: Computational approach

Matrix metalloproteinases-1 is recognized as a potential target of tumors and cancer for their role in breaking down collagen and extracellular matrix proteins. Our study screened 51 peptides against MMP-1 to find potent inhibitors for preventing breast cancer metastasis. Since cyclic peptides have higher selectivity and stability than linear peptides, we have focused our study on peptides with a cyclic structure as an MMP-1 inhibitor. We used an in-silico approach to design cyclic peptides and illustrated their activity as specific non-zinc binding inhibitors of MMP-1. Peptide cyclization, induced-fit docking (IFD), molecular dynamics simulation, and binding free energy (MM/GBSA) calculation were applied to evaluate the activity of peptides to inhibit MMP-1. Among the 51 peptides, we selected four peptides based on their docking score, binding affinity, and interacting residues. Complex 35, 36, 37, and 51 achieved highest docking scores of −215.37, −215.71, −195.56, and −208.08 kcal/mol, respectively. From the molecular dynamic simulation, we found three complexes (35, 36, and 37) with a stable structural conformation in terms of APO protein for 500 ns? Complexes 35, 36, 37 and 51 exhibited an average RMSD value of 2.45 ± 0.19, 3.39 ± 0.39, 2.03 ± 0.47, 2.60 ± 0.30 Å, respectively. The MM/GBSA free energy analysis of protein-peptide complex, complex 35, 36, and 37 show the greater MM/GBSA result consisting of the average value of −54.61 ± 0.45, −66.57 ± 0.38, and −43.57 ± 0.45 kJ/mol. These findings can facilitate the rational design and modifications of selective peptide inhibitors targeting the catalytic domain of MMP-1.

Network Analyses Based on Machine Learning Methods to Quantify Effects of Peptide-Protein Complexes as Drug Targets Using Cinnamon in Cardiovascular Diseases and Metabolic Syndrome as a Case Study.

Peptide–protein complexes play important roles in multiple diseases such as cardiovascular diseases (CVDs) and metabolic syndrome (MetS). The peptides may be the key molecules in the designing of inhibitors or drug targets. Many Chinese traditional drugs are shown to play various roles in different diseases, and comprehensive analyses should be performed using networks which could offer more information than results generated from a single level. In this study, a network analysis pipeline was designed based on machine learning methods to quantify the effects of peptide–protein complexes as drug targets. Three steps, namely, pathway filter, combined network construction, and biomarker prediction and validation based on peptides, were performed using cinnamon (CA) in CVDs and MetS as a case. Results showed that 17 peptide–protein complexes including six peptides and four proteins were identified as CA targets. The expressions of AKT1, AKT2, and ENOS were tested using qRT-PCR in a mouse model that was constructed. AKT2 was shown to be a CA-indicating biomarker, while E2F1 and ENOS were CA treatment targets. AKT1 was considered a diabetic responsive biomarker because it was down-regulated in diabetic but not related to CA. Taken together, the pipeline could identify new drug targets based on biological function analyses. This may provide a deep understanding of the drugs’ roles in different diseases which may foster the development of peptide–protein complex–based therapeutic approaches.

Predicting Protein-Peptide Complex Structures by Accounting for Peptide Flexibility and the Physicochemical Environment.

Predicting protein-peptide complex structures is crucial to the understanding of a vast variety of peptide-mediated cellular processes and to peptide-based drug development. Peptide flexibility and binding mode ranking are the two major challenges for protein-peptide complex structure prediction. Peptides are highly flexible molecules, and therefore, brute-force modeling of peptide conformations of interest in protein-peptide docking is beyond current computing power. Inspired by the fact that the protein-peptide binding process is like protein folding, we developed a novel strategy, named MDockPeP2, which tries to address these challenges using physicochemical information embedded in abundant monomeric proteins with an exhaustive search strategy, in combination with an integrated global search and a local flexible minimization method. Only the peptide sequence and the protein crystal structure are required. The method was systemically assessed using a newly constructed structural database of 89 nonredundant protein-peptide complexes with the peptide sequence length ranging from 5 to 29 in which about half of the peptides are longer than 15 residues. MDockPeP2 yielded a total success rate of 58.4% (70.8, 79.8%) for the bound docking (i.e., with the bound receptor and fully flexible peptides) and 19.0% (44.8, 70.7%) for the challenging unbound docking when top 10 (100, 1000) models were considered for each prediction. MDockPeP2 achieved significantly higher success rates on two other datasets, peptiDB and LEADS-PEP, which contain only short- and medium-size peptides (≤ 15 residues). For peptiDB, our method obtained a success rate of 62.0% for the bound docking and 35.9% for the unbound docking when the top 10 models were considered. For LEADS-PEP, MDockPeP2 achieved a success rate of 69.8% when the top 10 models were considered. The program is available at https://zougrouptoolkit.missouri.edu/mdockpep2/download.html.