Predicting peptide binding sites on protein surfaces by clustering chemical interactions

GAPS: a geometric attention-based network for peptide binding site identification by the transfer learning approach

Protein-peptide interactions (PPepIs) are vital to understanding cellular functions, which can facilitate the design of novel drugs. As an essential component in forming a PPepI, protein-peptide binding sites are the basis for understanding the mechanisms involved in PPepIs. Therefore, accurately identifying protein-peptide binding sites becomes a critical task. The traditional experimental methods for researching these binding sites are labor-intensive and time-consuming, and some computational tools have been invented to supplement it. However, these computational tools have limitations in generality or accuracy due to the need for ligand information, complex feature construction, or their reliance on modeling based on amino acid residues. To deal with the drawbacks of these computational algorithms, we describe a geometric attention-based network for peptide binding site identification (GAPS) in this work. The proposed model utilizes geometric feature engineering to construct atom representations and incorporates multiple attention mechanisms to update relevant biological features. In addition, the transfer learning strategy is implemented for leveraging the protein-protein binding sites information to enhance the protein-peptide binding sites recognition capability, taking into account the common structure and biological bias between proteins and peptides. Consequently, GAPS demonstrates the state-of-the-art performance and excellent robustness in this task. Moreover, our model exhibits exceptional performance across several extended experiments including predicting the apo protein-peptide, protein-cyclic peptide and the AlphaFold-predicted protein-peptide binding sites. These results confirm that the GAPS model is a powerful, versatile, stable method suitable for diverse binding site predictions.

Anticancer Effect of Active Component of Astragalus Membranaceus Combined with Olaparib on Ovarian Cancer Predicted by Network-Based Pharmacology

In China, a traditional Chinese medicine formulation called astragalus membranaceus (AM) has been utilised for more than 20 years to treat tumors with extraordinary effectiveness. The fundamental mechanisms, nevertheless, are still not well understood. The aim of this study is identifying its possible therapeutic targets and to evaluate the effects of AM in combination with a PARP inhibitor (olaparib) in the treatment of BRCA wild-type ovarian cancer. Significant genes were collected from Therapeutic Target Database and Database of Gene-Disease Associations. The components of AM were analyzed using the Traditional Chinese Medicine System Pharmacology (TCMSP) database to screen the active ingredients of AM based on their oral bioavailability and drug similarity index. In order to find intersection targets, Venn diagrams and STRING website diagrams were employed. STRING was also used to create a protein-protein interaction network. In order to create the ingredient-target network, Cytoscape 3.8.0 was used. DAVID database was utilized to carry out enrichment and pathway analyses. The binding ability of the active compounds of AM to the core targets of AM-OC was verified with molecular docking using AutoDock software. Experimental validations, including cell scratch, cell transwell, cloning experiment, were conducted to verify the effects of AM on OC cells. A total of 14 active ingredients of AM and 28 AM-OC-related targets were screened by network pharmacology analysis. The ten most significant Gene Ontology (GO) biological function analyses, as well as the 20 foremost Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathways were selected. Moreover, molecular docking results showed that bioactive compound (quercetin) demonstrated a good binding ability with tumor protein p53 (TP53), MYC, vascular endothelial growth factor A (VEGFA), phosphatase and tensin homolog (PTEN), AKT serine/threonine kinase 1 (AKT1) and cyclin D1 (CCND1) oncogenes. According to experimental methods, in vitro OC cell proliferation and migration appeared to be inhibited by quercetin, which also increased apoptosis. In addition, the combination with olaparib further enhanced the effect of quercetin on OC. Based on network pharmacology, molecular docking, and experimental validation, the combination of PARP inhibitor and quercetin enhanced the anti-proliferative activity in BRCA wild-type ovarian cancer cells, which supplies the theoretical groundwork for additional pharmacological investigation.

Down-regulation of iron/zinc ion transport and toxin synthesis in Microcystis aeruginosa exposed to 5,4'-dihydroxyflavone

Flavonoids, common natural polyphenolic compounds from plants, have been proposed as highly effective and safe algicides. However, the molecular mechanism of flavonoids inhibiting Microcystis aeruginosa remains unclear. This study aims in exploring the global transcriptional changes and molecular docking in cyanobacterial cells in response to flavonoids. Transcriptomic analysis revealed that 5,4'-dihydroxyflavone (DHF) primarily affected the genes transcription of iron and zinc ion transport, resulting in the blockage of transport for iron (II), iron (III) and zinc (II), which eventually led to a decrease in intracellular iron and zinc content. 5,4'-DHF can also interfere with iron and zinc transport by binding to metal ion transport-related proteins, leading to eliminated biological activities in M. aeruginosa. Meanwhile, 5,4'-DHF inhibit microcystin synthesis and reduce the content of intercellular toxin by inhibiting the transcription of mcyC and binding with McyC protein, implying that 5,4'-DHF have potential to reduce the risk of microcystins in the environment. Moreover, iron starvation and down-regulation of photosynthesis-related genes transcription led to the inhibition of electron transport in photosynthetic system. These results provide more information for the inhibitory mechanism of flavonoids, and the inhibition of flavonoids on metal ion transmembrane transport provides a new perspective for the development of allelochemical algicides.

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.

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 silico studies of interactions of peptide-conjugated cholesterol metabolites and betulinic acid with EGFR, LDR, and N-terminal fragment of CCKA receptors

In this work, we designed three new ligands by conjugating cholesterol metabolites 3-hydroxy-5-cholestenoic acid (3-HC) and 3-oxo-4-cholestenoic acid (3-OC) and the natural tri-terpenoid betulinic acid with the tumor-targeting peptide YHWYGYTPQNVI. Molecular interactions with the unconjugated peptide and the conjugates were examined with three receptors that are commonly overexpressed in pancreatic adenocarcinoma cells using ligand docking and molecular dynamics. This study demonstrated the utility of the designed conjugates as a valuable scaffold for potentially targeting EGFR and LDLR receptors. Our results indicate that the conjugates showed strong binding affinities and formation of stable complexes with EGFR, while the unconjugated peptide, BT-peptide conjugate, an 3-HC-peptide conjugate showed the formation of fairly stable complexes with LDLR receptor. For EGFR, two receptor kinase domains were explored. Interactions with the N-terminal domain of CCKA-R were relatively weaker. For LDLR, binding occurred in the beta-propeller region. For the N-terminal fragment of CCKA-R, the conjugates induced significant conformational changes in the receptor. The molecular dynamic simulations for 100 ns demonstrate that BT-peptide conjugates and the unconjugated peptide had the highest binding and formed the most stable complexes with EGFR. RMSD and trajectory analyses indicate that these molecules transit to a dynamically stable configuration in most cases within 60 ns. NMA analysis indicated that amongst the conjugates that showed relatively higher interactions with the respective receptors, the highest potential for deformability was seen for the N-terminal-47 amino acid region of the CCKA-R receptor with and the lowest for the LDLR-receptor. Thus, the newly designed compounds may be evaluated in the future toward developing drug delivery materials for targeting tumor cells overexpressing LDLR or EGFR.

Protein-Peptide Binding Site Detection Using 3D Convolutional Neural Networks

Peptides and peptide-based molecules represent a promising therapeutic modality targeting intracellular protein-protein interactions, potentially combining the beneficial properties of biologics and small-molecule drugs. Protein-peptide complexes occupy a unique niche of interaction interfaces with respect to protein-protein and protein-small molecule complexes. Protein-peptide binding site identification resembles image object detection, a field that had been revolutionalized with computer vision techniques. We present a new protein-peptide binding site detection method called BiteNet_Pp by harnessing the power of 3D convolutional neural network. Our method employs a tensor-based representation of spatial protein structures, which is fed to 3D convolutional neural network, resulting in probability scores and coordinates of the binding "hot spots" in the input structures. We used the domain adaptation technique to fine-tune model trained on protein-small molecule complexes using a manually curated set of protein-peptide structures. BiteNet_Pp consistently outperforms existing state-of-the-art methods in the independent test benchmark. It takes less than a second to analyze a single-protein structure, making BiteNet_Pp suitable for the large-scale analysis of protein-peptide binding sites.

Methods for Molecular Modelling of Protein Complexes

Biological processes are often mediated by complexes formed between proteins and various biomolecules. The 3D structures of such protein-biomolecule complexes provide insights into the molecular mechanism of their action. The structure of these complexes can be predicted by various computational methods. Choosing an appropriate method for modelling depends on the category of biomolecule that a protein interacts with and the availability of structural information about the protein and its interacting partner. We intend for the contents of this chapter to serve as a guide as to what software would be the most appropriate for the type of data at hand and the kind of 3D complex structure required. Particularly, we have dealt with protein-small molecule ligand, protein-peptide, protein-protein, and protein-nucleic acid interactions.Most, if not all, model building protocols perform some sampling and scoring. Typically, several alternate conformations and configurations of the interactors are sampled. Each such sample is then scored for optimization. To boost the confidence in these predicted models, their assessment using other independent scoring schemes besides the inbuilt/default ones would prove to be helpful. This chapter also lists such software and serves as a guide to gauge the fidelity of modelled structures of biomolecular complexes.

InterPep2: global peptide-protein docking using interaction surface templates

Motivation

Interactions between proteins and peptides or peptide-like intrinsically disordered regions are involved in many important biological processes, such as gene expression and cell life-cycle regulation. Experimentally determining the structure of such interactions is time-consuming and difficult because of the inherent flexibility of the peptide ligand. Although several prediction-methods exist, most are limited in performance or availability.

Results

InterPep2 is a freely available method for predicting the structure of peptide–protein interactions. Improved performance is obtained by using templates from both peptide–protein and regular protein–protein interactions, and by a random forest trained to predict the DockQ-score for a given template using sequence and structural features. When tested on 252 bound peptide–protein complexes from structures deposited after the complexes used in the construction of the training and templates sets of InterPep2, InterPep2-Refined correctly positioned 67 peptides within 4.0 Å LRMSD among top10, similar to another state-of-the-art template-based method which positioned 54 peptides correctly. However, InterPep2 displays a superior ability to evaluate the quality of its own predictions. On a previously established set of 27 non-redundant unbound-to-bound peptide–protein complexes, InterPep2 performs on-par with leading methods. The extended InterPep2-Refined protocol managed to correctly model 15 of these complexes within 4.0 Å LRMSD among top10, without using templates from homologs. In addition, combining the template-based predictions from InterPep2 with ab initio predictions from PIPER-FlexPepDock resulted in 22% more near-native predictions compared to the best single method (22 versus 18).

Availability and implementation

The program is available from: http://wallnerlab.org/InterPep2.

Supplementary information

Supplementary data are available at Bioinformatics online.

Structure of the BRK domain of the SWI/SNF chromatin remodeling complex subunit BRG1 reveals a potential role in protein-protein interactions

BRG1/SMARCA4 and its paralog BRM/SMARCA2 are the ATPase subunits of human SWI/SNF chromatin remodeling complexes. These multisubunit assemblies can act as either tumor suppressors or drivers of cancer, and inhibiting both BRG1 and BRM, is emerging as an effective therapeutic strategy in diverse cancers. BRG1 and BRM contain a BRK domain. The function of this domain is unknown, but it is often found in proteins involved in transcription and developmental signaling in higher eukaryotes, in particular in proteins that remodel chromatin. We report the NMR structure of the BRG1 BRK domain. It shows similarity to the glycine-tyrosine-phenylalanine (GYF) domain, an established protein-protein interaction module. Computational peptide-binding-site analysis of the BRK domain identifies a binding site that coincides with a highly conserved groove on the surface of the protein. This sets the scene for experiments to elucidate the role of this domain, and evaluate the potential of targeting it for cancer therapy.

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 ABSTRACT

CABS-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.

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/.

Predicting Protein-Protein Interfaces that Bind Intrinsically Disordered Protein Regions

A long-standing goal in biology is the complete annotation of function and structure on all protein-protein interactions, a large fraction of which is mediated by intrinsically disordered protein regions (IDRs). However, knowledge derived from experimental structures of such protein complexes is disproportionately small due, in part, to challenges in studying interactions of IDRs. Here, we introduce IDRBind, a computational method that by combining gradient boosted trees and conditional random field models predicts binding sites of IDRs with performance approaching state-of-the-art globular interface predictions, making it suitable for proteome-wide applications. Although designed and trained with a focus on molecular recognition features, which are long interaction-mediating-elements in IDRs, IDRBind also predicts the binding sites of short peptides more accurately than existing specialized predictors. Consistent with IDRBind's specificity, a comparison of protein interface categories uncovered uniform trends in multiple physicochemical properties, positioning molecular recognition feature interfaces between peptide and globular interfaces.

Predicting protein-peptide interaction sites using distant protein complexes as structural templates

Protein-peptide interactions play an important role in major cellular processes, and are associated with several human diseases. To understand and potentially regulate these cellular function and diseases it is important to know the molecular details of the interactions. However, because of peptide flexibility and the transient nature of protein-peptide interactions, peptides are difficult to study experimentally. Thus, computational methods for predicting structural information about protein-peptide interactions are needed. Here we present InterPep, a pipeline for predicting protein-peptide interaction sites. It is a novel pipeline that, given a protein structure and a peptide sequence, utilizes structural template matches, sequence information, random forest machine learning, and hierarchical clustering to predict what region of the protein structure the peptide is most likely to bind. When tested on its ability to predict binding sites, InterPep successfully pinpointed 255 of 502 (50.7%) binding sites in experimentally determined structures at rank 1 and 348 of 502 (69.3%) among the top five predictions using only structures with no significant sequence similarity as templates. InterPep is a powerful tool for identifying peptide-binding sites; with a precision of 80% at a recall of 20% it should be an excellent starting point for docking protocols or experiments investigating peptide interactions. The source code for InterPred is available at http://wallnerlab.org/InterPep/ .

Benchmarking of different molecular docking methods for protein-peptide docking

Background

Molecular 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.

Result

Firstly, 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.

Conclusion

The 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/).

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2449-y) contains supplementary material, which is available to authorized users.

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.

Computer-aided design of amino acid-based therapeutics: a review

During the last two decades, the pharmaceutical industry has progressed from detecting small molecules to designing biologic-based therapeutics. Amino acid-based drugs are a group of biologic-based therapeutics that can effectively combat the diseases caused by drug resistance or molecular deficiency. Computational techniques play a key role to design and develop the amino acid-based therapeutics such as proteins, peptides and peptidomimetics. In this study, it was attempted to discuss the various elements for computational design of amino acid-based therapeutics. Protein design seeks to identify the properties of amino acid sequences that fold to predetermined structures with desirable structural and functional characteristics. Peptide drugs occupy a middle space between proteins and small molecules and it is hoped that they can target "undruggable" intracellular protein-protein interactions. Peptidomimetics, the compounds that mimic the biologic characteristics of peptides, present refined pharmacokinetic properties compared to the original peptides. Here, the elaborated techniques that are developed to characterize the amino acid sequences consistent with a specific structure and allow protein design are discussed. Moreover, the key principles and recent advances in currently introduced computational techniques for rational peptide design are spotlighted. The most advanced computational techniques developed to design novel peptidomimetics are also summarized.

Protein-peptide docking: opportunities and challenges

Peptides have recently attracted much attention as promising drug candidates. Rational design of peptide-derived therapeutics usually requires structural characterization of the underlying protein-peptide interaction. Given that experimental characterization can be difficult, reliable computational tools are needed. In recent years, a variety of approaches have been developed for 'protein-peptide docking', that is, predicting the structure of the protein-peptide complex, starting from the protein structure and the peptide sequence, including variable degrees of information about the peptide binding site and/or conformation. In this review, we provide an overview of protein-peptide docking methods and outline their capabilities, limitations, and applications in structure-based drug design. Key challenges are also briefly discussed, such as modeling of large-scale conformational changes upon binding, scoring of predicted models, and optimal inclusion of varied types of experimental data and theoretical predictions into an integrative modeling process.

Interfering peptides targeting protein-protein interactions: the next generation of drugs?

Protein-protein interactions (PPIs) are well recognized as promising therapeutic targets. Consequently, interfering peptides (IPs) - natural or synthetic peptides capable of interfering with PPIs - are receiving increasing attention. Given their physicochemical characteristics, IPs seem better suited than small molecules to interfere with the large surfaces implicated in PPIs. Progress on peptide administration, stability, biodelivery and safety are also encouraging the interest in peptide drug development. The concept of IPs has been validated for several PPIs, generating great expectations for their therapeutic potential. Here, we describe approaches and methods useful for IPs identification and in silico, physicochemical and biological-based strategies for their design and optimization. Selected promising in-vivo-validated examples are described and advantages, limitations and potential of IPs as therapeutic tools are discussed.

Application of the ATTRACT Coarse-Grained Docking and Atomistic Refinement for Predicting Peptide-Protein Interactions

Peptide-protein interactions are abundant in the cell and form an important part of the interactome. Large-scale modeling of peptide-protein complexes requires a fully blind approach; i.e., simultaneously predicting the peptide-binding site and the peptide conformation to high accuracy. Here, we present one of the first fully blind peptide-protein docking protocols, pepATTRACT. It combines a coarse-grained ensemble docking search of the entire protein surface with two stages of atomistic flexible refinement. pepATTRACT yields high-quality predictions for 70 % of the cases when tested on a large benchmark of peptide-protein complexes. This performance in fully blind mode is similar to state-of-the-art local docking approaches that use information on the location of the binding site. Limiting the search to the peptide-binding region, the resulting pepATTRACT-local approach further improves the performance. Docking scripts for pepATTRACT and pepATTRACT-local can be generated via a web interface at www.attract.ph.tum.de/peptide.html . Here, we explain how to set up a docking run with the pepATTRACT web interface and demonstrate its usage by an application on binding of disordered regions from tumor suppressor p53 to a partner protein.

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

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

Highly Flexible Protein-Peptide Docking Using CABS-Dock

Protein-peptide molecular docking is a difficult modeling problem. It is even more challenging when significant conformational changes that may occur during the binding process need to be predicted. In this chapter, we demonstrate the capabilities and features of the CABS-dock server for flexible protein-peptide docking. CABS-dock allows highly efficient modeling of full peptide flexibility and significant flexibility of a protein receptor. During CABS-dock docking, the peptide folding and binding process is explicitly simulated and no information about the peptide binding site or its structure is used. This chapter presents a successful CABS-dock use for docking a potentially therapeutic peptide to a protein target. Moreover, simulation contact maps, a new CABS-dock feature, are described and applied to the docking test case. Finally, a tutorial for running CABS-dock from the command line or command line scripts is provided. The CABS-dock web server is available from http://biocomp.chem.uw.edu.pl/CABSdock/ .

The Usage of ACCLUSTER for Peptide Binding Site Prediction

Peptides mediate up to 40 % of protein-protein interactions in a variety of cellular processes and are also attractive drug candidates. Thus, predicting peptide binding sites on the given protein structure is of great importance for mechanistic investigation of protein-peptide interactions and peptide therapeutics development. In this chapter, we describe the usage of our web server, referred to as ACCLUSTER, for peptide binding site prediction for a given protein structure. ACCLUSTER is freely available for users without registration at http://zougrouptoolkit.missouri.edu/accluster .

Performance of MDockPP in CAPRI rounds 28-29 and 31-35 including the prediction of water-mediated interactions

Protein-protein interactions are either through direct contacts between two binding partners or mediated by structural waters. Both direct contacts and water-mediated interactions are crucial to the formation of a protein-protein complex. During the recent CAPRI rounds, a novel parallel searching strategy for predicting water-mediated interactions is introduced into our protein-protein docking method, MDockPP. Briefly, a FFT-based docking algorithm is employed in generating putative binding modes, and an iteratively derived statistical potential-based scoring function, ITScorePP, in conjunction with biological information is used to assess and rank the binding modes. Up to 10 binding modes are selected as the initial protein-protein complex structures for MD simulations in explicit solvent. Water molecules near the interface are clustered based on the snapshots extracted from independent equilibrated trajectories. Then, protein-ligand docking is employed for a parallel search for water molecules near the protein-protein interface. The water molecules generated by ligand docking and the clustered water molecules generated by MD simulations are merged, referred to as the predicted structural water molecules. Here, we report the performance of this protocol for CAPRI rounds 28-29 and 31-35 containing 20 valid docking targets and 11 scoring targets. In the docking experiments, we predicted correct binding modes for nine targets, including one high-accuracy, two medium-accuracy, and six acceptable predictions. Regarding the two targets for the prediction of water-mediated interactions, we achieved models ranked as "excellent" in accordance with the CAPRI evaluation criteria; one of these two targets is considered as a difficult target for structural water prediction. Proteins 2017; 85:424-434. © 2016 Wiley Periodicals, Inc.

Fully Blind Docking at the Atomic Level for Protein-Peptide Complex Structure Prediction

Protein-peptide interactions play an important role in many cellular processes. In silico prediction of protein-peptide complex structure is highly desirable for mechanistic investigation of these processes and for therapeutic design. However, predicting all-atom structures of protein-peptide complexes without any knowledge about the peptide binding site and the bound peptide conformation remains a big challenge. Here, we present a docking-based method for predicting protein-peptide complex structures, referred to as MDockPeP, which starts with the peptide sequence and globally docks the all-atom, flexible peptide onto the protein structure. MDockPeP was tested on the peptiDB benchmarking database using both bound and unbound protein structures. The results show that MDockPeP successfully generated near-native peptide binding modes in 95.0% of the bound docking cases and in 92.2% of the unbound docking cases. The performance is significantly better than other existing docking methods. MDockPeP is computationally efficient and suitable for large-scale applications.

Rational, computer-enabled peptide drug design: principles, methods, applications and future directions

Peptides provide promising templates for developing drugs to occupy a middle space between small molecules and antibodies and for targeting 'undruggable' intracellular protein-protein interactions. Importantly, rational or in cerebro design, especially when coupled with validated in silico tools, can be used to efficiently explore chemical space and identify islands of 'drug-like' peptides to satisfy diverse drug discovery program objectives. Here, we consider the underlying principles of and recent advances in rational, computer-enabled peptide drug design. In particular, we consider the impact of basic physicochemical properties, potency and ADME/Tox opportunities and challenges, and recently developed computational tools for enabling rational peptide drug design. Key principles and practices are spotlighted by recent case studies. We close with a hypothetical future case study.

A Dye-Decolorizing Peroxidase from Vibrio cholerae

The dye-decolorizing peroxidase (DyP) protein from Vibrio cholerae (VcDyP) was expressed in Escherichia coli, and its DyP activity was assayed by monitoring degradation of a typical anthraquinone dye, reactive blue 19 (RB19). Its kinetic activity was obtained by fitting the data to the Michaelis-Menten equation, giving kcat and Km values of 1.3 ± 0.3 s(-1) and 50 ± 20 μM, respectively, which are comparable to those of other DyP enzymes. The enzymatic activity of VcDyP was highest at pH 4. A mutational study showed that two distal residues, Asp144 and Arg230, which are conserved in a DyP family, are essential for the DyP reaction. The crystal structure and resonance Raman spectra of VcDyP indicate the transfer of a radical from heme to the protein surface, which was supported by the formation of the intermolecular covalent bond in the reaction with H2O2. To identify the radical site, each of nine tyrosine or two tryptophan residues was substituted. It was clarified that Tyr129 and Tyr235 are in the active site of the dye degradation reaction at lower pH, while Tyr109 and Tyr133 are the sites of an intermolecular covalent bond at higher pH. VcDyP degrades RB19 at lower pH, while it loses activity under neutral or alkaline conditions because of a change in the radical transfer pathway. This finding suggests the presence of a pH-dependent switch of the radical transfer pathway, probably including His178. Although the physiological function of the DyP reaction is unclear, our findings suggest that VcDyP enhances the DyP activity to survive only when it is placed under a severe condition such as being in gastric acid.

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

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