Protein-protein interaction network prediction by using rigid-body docking tools: application to bacterial chemotaxis

Molecular investigation of antimicrobial peptides against Helicobacter pylori proteins using a peptide-protein docking approach

The impact of H. pylori resistance on patient's treatment failure is a major concern. Therefore, the development of novel or alternative therapies for H. pylori is urgently needed. The purpose of this study was to investigate the molecular interactions of various antimicrobial peptides (AMPs) to H. pylori proteins. We performed the peptide-protein molecular docking using HADDOCK 2.4 webserver. Fourteen AMPs were tested for their binding efficacy against four H. pylori proteins. Simulation of the peptide-protein complex was performed using molecular dynamic software package AMBER20. From molecular docking analysis, five peptides (LL-37, Tilapia piscidin 4, napin, snakin-1 and EcAMP1) showed strong binding interactions against H. pylori proteins. The strongest binding affinity was observed in the interactions between Snakin-1 and PBP2, TP4 and type I HopQ and EcAMP1 and type I HopQ with -11.1, -13.6 and -13.8 kcal/mol, respectively. The dynamic simulation was performed for two complexes (snakin1-PBP2 and EcAMP1-HopQ). Results of the dynamics simulation showed that EcAMP1 had stable interaction and binding to type I HopQ protein without significant structural changes. In conclusion, both results of docking and simulation showed that EcAMP1 might be useful as a potential therapeutic agent for H. pylori treatment. This molecular approach provides deep understanding of the interaction insights between AMPs and H. pylori proteins. It paves the way for the development of novel anti-H. pylori using antimicrobial peptides.

PPI-Miner: A Structure and Sequence Motif Co-Driven Protein-Protein Interaction Mining and Modeling Computational Method

Protein-protein interactions (PPIs) play important roles in biological processes of life, and predicting PPIs becomes a critical scientific issue of concern. Most PPIs occur through small domains or motifs (fragments), which are challenging and laborious to map by standard biochemical approaches because they generally require the cloning of several truncation mutants. Here, we present a computational method, named as PPI-Miner, to fish potential protein interacting partners utilizing protein motifs as queries. In brief, this work first developed a motif-matching algorithm designed to identify the proteins that contain sequential or structural similar motifs with the given query motif. Being aligned to the query motif, the binding mode of the discovered motif and its receptor protein will be initially determined to be used to build PPI complexes accordingly. Eventually, a PPI complex structure could be built and optimized with a designed automatic protocol. Besides discovering PPIs, PPI-Miner can also be applied to other areas, i.e., the rational design of molecular glues and protein vaccines. In this work, PPI-Miner was employed to mine the potential cereblon (CRBN) substrates from human proteome. As a result, 1,739 candidates were predicted, and 16 of them have been experimentally validated in previous studies. The source code of PPI-Miner can be obtained from the GitHub repository (https://github.com/Wang-Lin-boop/PPI-Miner), the webserver is freely available for users (https://bailab.siais.shanghaitech.edu.cn/services/ppi-miner), and the database of predicted CRBN substrates is accessible at https://bailab.siais.shanghaitech.edu.cn/services/crbn-subslib.

Investigation of Plant Antimicrobial Peptides against Selected Pathogenic Bacterial Species Using a Peptide-Protein Docking Approach

Antimicrobial resistance is the key threat to global health due to high morbidity and mortality. The alteration of bacterial proteins, enzymatic degradation, and change of membrane permeability towards antimicrobial agents are the key mechanisms of antimicrobial resistance. Based on the current condition, there is an urgent clinical need to develop new drugs to treat these bacterial infections. In the current study, the binding patterns of selected antimicrobial peptides (AMPs) with different multidrug-resistant bacterial strains have been analyzed. Among ten selected AMPs in this study, napin and snakin-1 exhibited the best scores and binding patterns. Napin exhibited strong interactions with penicillin-binding protein 1a of Acinetobacter baumannii (with a binding score of -158.7 kcal/mol and ten hydrogen bonds), with glucose-1-phosphate thymidylyltransferase of Mycobacterium tuberculosis H37Rv (with a binding score of -107.8 kcal/mol and twelve hydrogen bonds), and with streptomycin 3^″-adenylyltransferase protein of Salmonella enterica (with a binding score of -84.2 kcal/mol and four hydrogen bonds). Similarly, snakin-1 showed strong interactions with oxygen-insensitive NADPH nitroreductase of Helicobacter pylori (with a binding score of -105.0 kcal/mol and thirteen hydrogen bonds) and with penicillin-binding protein 2a of methicillin-resistant Staphylococcus aureus (with a binding score of -103.8 kcal/mol and twenty-three hydrogen bonds). The docking results were further validated by molecular dynamics simulations. The results of this computational approach support the evidence of efficiency of these AMPs as potent inhibitors of these specific proteins of bacterial strains. However, further validations are required to fully evaluate the potential of selected AMPs as drug candidates against these resistant bacterial strains.

Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge

Prediction of biological interaction networks from single-omics data has been extensively implemented to understand various aspects of biological systems. However, more recently, there is a growing interest in integrating multi-omics datasets for the prediction of interactomes that provide a global view of biological systems with higher descriptive capability, as compared to single omics. In this review, we have discussed various computational approaches implemented to infer and analyze two of the most important and well studied interactomes: protein-protein interaction networks and gene co-expression networks. We have explicitly focused on recent methods and pipelines implemented to infer and extract biologically important information from these interactomes, starting from utilizing single-omics data and then progressing towards multi-omics data. Accordingly, recent examples and case studies are also briefly discussed. Overall, this review will provide a proper understanding of the latest developments in protein and gene network modelling and will also help in extracting practical knowledge from them.

MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions

Background

Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs.

Results

In this study, we constructed an integrated database of MEGADOCK PPI predictions, named MEGADOCK-Web. MEGADOCK-Web provides more than 10 times the number of PPI predictions than previous databases and enables users to conduct PPI predictions that cannot be found in conventional PPI prediction databases. In MEGADOCK-Web, there are 7528 protein chains and 28,331,628 predicted PPIs from all possible combinations of those proteins. Each protein structure is annotated with PDB ID, chain ID, UniProt AC, related KEGG pathway IDs, and known PPI pairs. Additionally, MEGADOCK-Web provides four powerful functions: 1) searching precalculated PPI predictions, 2) providing annotations for each predicted protein pair with an experimentally known PPI, 3) visualizing candidates that may interact with the query protein on biochemical pathways, and 4) visualizing predicted complex structures through a 3D molecular viewer.

Conclusion

MEGADOCK-Web provides a huge amount of comprehensive PPI predictions based on docking calculations with biochemical pathways and enables users to easily and quickly assess PPI feasibilities by archiving PPI predictions. MEGADOCK-Web also promotes the discovery of new PPIs and protein functions and is freely available for use at http://www.bi.cs.titech.ac.jp/megadock-web/.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2073-x) contains supplementary material, which is available to authorized users.

Network Analyses in Plant Pathogens

Even in the age of big data in Biology, studying the connections between the biological processes and the molecular mechanisms behind them is a challenging task. Systems biology arose as a transversal discipline between biology, chemistry, computer science, mathematics, and physics to facilitate the elucidation of such connections. A scenario, where the application of systems biology constitutes a very powerful tool, is the study of interactions between hosts and pathogens using network approaches. Interactions between pathogenic bacteria and their hosts, both in agricultural and human health contexts are of great interest to researchers worldwide. Large amounts of data have been generated in the last few years within this area of research. However, studies have been relatively limited to simple interactions. This has left great amounts of data that remain to be utilized. Here, we review the main techniques in network analysis and their complementary experimental assays used to investigate bacterial-plant interactions. Other host-pathogen interactions are presented in those cases where few or no examples of plant pathogens exist. Furthermore, we present key results that have been obtained with these techniques and how these can help in the design of new strategies to control bacterial pathogens. The review comprises metabolic simulation, protein-protein interactions, regulatory control of gene expression, host-pathogen modeling, and genome evolution in bacteria. The aim of this review is to offer scientists working on plant-pathogen interactions basic concepts around network biology, as well as an array of techniques that will be useful for a better and more complete interpretation of their data.

Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks

BACKGROUND

Deciphering complete networks of interactions between proteins is the key to comprehend cellular regulatory mechanisms. A significant effort has been devoted to expanding the coverage of the proteome-wide interaction space at molecular level. Although a growing body of research shows that protein docking can, in principle, be used to predict biologically relevant interactions, the accuracy of the across-proteome identification of interacting partners and the selection of near-native complex structures still need to be improved.

RESULTS

In this study, we developed a new method to discover and model protein interactions employing an exhaustive all-to-all docking strategy. This approach integrates molecular modeling, structural bioinformatics, machine learning, and functional annotation filters in order to provide interaction data for the bottom-up assembly of protein interaction networks. Encouragingly, the success rates for dimer modeling is 57.5 and 48.7% when experimental and computer-generated monomer structures are employed, respectively. Further, our protocol correctly identifies 81% of protein-protein interactions at the expense of only 19% false positive rate. As a proof of concept, 61,913 protein-protein interactions were confidently predicted and modeled for the proteome of E. coli. Finally, we validated our method against the human immune disease pathway.

CONCLUSIONS

Protein docking supported by evolutionary restraints and machine learning can be used to reliably identify and model biologically relevant protein assemblies at the proteome scale. Moreover, the accuracy of the identification of protein-protein interactions is improved by considering only those protein pairs co-localized in the same cellular compartment and involved in the same biological process. The modeling protocol described in this communication can be applied to detect protein-protein interactions in other organisms and pathways as well as to construct dimer structures and estimate the confidence of protein interactions experimentally identified with high-throughput techniques.

Software for molecular docking: a review

Molecular docking methodology explores the behavior of small molecules in the binding site of a target protein. As more protein structures are determined experimentally using X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy, molecular docking is increasingly used as a tool in drug discovery. Docking against homology-modeled targets also becomes possible for proteins whose structures are not known. With the docking strategies, the druggability of the compounds and their specificity against a particular target can be calculated for further lead optimization processes. Molecular docking programs perform a search algorithm in which the conformation of the ligand is evaluated recursively until the convergence to the minimum energy is reached. Finally, an affinity scoring function, ΔG [U total in kcal/mol], is employed to rank the candidate poses as the sum of the electrostatic and van der Waals energies. The driving forces for these specific interactions in biological systems aim toward complementarities between the shape and electrostatics of the binding site surfaces and the ligand or substrate.

Specificity of broad protein interaction surfaces for proteins with multiple binding partners

Analysis of protein-protein interaction networks has revealed the presence of proteins with multiple interaction ligand proteins, such as hub proteins. For such proteins, multiple ligands would be predicted as interacting partners when predicting all-to-all protein-protein interactions (PPIs). In this work, to obtain a better understanding of PPI mechanisms, we focused on protein interaction surfaces, which differ between protein pairs. We then performed rigid-body docking to obtain information of interfaces of a set of decoy structures, which include many possible interaction surfaces between a certain protein pair. Then, we investigated the specificity of sets of decoy interactions between true binding partners in each case of alpha-chymotrypsin, actin, and cyclin-dependent kinase 2 as test proteins having multiple true binding partners. To observe differences in interaction surfaces of docking decoys, we introduced broad interaction profiles (BIPs), generated by assembling interaction profiles of decoys for each protein pair. After cluster analysis, the specificity of BIPs of true binding partners was observed for each receptor. We used two types of BIPs: those involved in amino acid sequences (BIP-seqs) and those involved in the compositions of interacting amino acid residue pairs (BIP-AAs). The specificity of a BIP was defined as the number of group members including all true binding partners. We found that BIP-AA cases were more specific than BIP-seq cases. These results indicated that the composition of interacting amino acid residue pairs was sufficient for determining the properties of protein interaction surfaces.

Rigid-Docking Approaches to Explore Protein-Protein Interaction Space

Protein-protein interactions play core roles in living cells, especially in the regulatory systems. As information on proteins has rapidly accumulated on publicly available databases, much effort has been made to obtain a better picture of protein-protein interaction networks using protein tertiary structure data. Predicting relevant interacting partners from their tertiary structure is a challenging task and computer science methods have the potential to assist with this. Protein-protein rigid docking has been utilized by several projects, docking-based approaches having the advantages that they can suggest binding poses of predicted binding partners which would help in understanding the interaction mechanisms and that comparing docking results of both non-binders and binders can lead to understanding the specificity of protein-protein interactions from structural viewpoints. In this review we focus on explaining current computational prediction methods to predict pairwise direct protein-protein interactions that form protein complexes.

Highly precise protein-protein interaction prediction based on consensus between template-based and de novo docking methods

Background

Elucidation of protein-protein interaction (PPI) networks is important for understanding disease mechanisms and for drug discovery. Tertiary-structure-based in silico PPI prediction methods have been developed with two typical approaches: a method based on template matching with known protein structures and a method based on de novo protein docking. However, the template-based method has a narrow applicable range because of its use of template information, and the de novo docking based method does not have good prediction performance. In addition, both of these in silico prediction methods have insufficient precision, and require validation of the predicted PPIs by biological experiments, leading to considerable expenditure; therefore, PPI prediction methods with greater precision are needed.

Results

We have proposed a new structure-based PPI prediction method by combining template-based prediction and de novo docking prediction. When we applied the method to the human apoptosis signaling pathway, we obtained a precision value of 0.333, which is higher than that achieved using conventional methods (0.231 for PRISM, a template-based method, and 0.145 for MEGADOCK, a non-template-based method), while maintaining an F-measure value (0.285) comparable to that obtained using conventional methods (0.296 for PRISM, and 0.220 for MEGADOCK).

Conclusions

Our consensus method successfully predicted a PPI network with greater precision than conventional template/non-template methods, which may thus reduce the cost of validation by laboratory experiments for confirming novel PPIs from predicted PPIs. Therefore, our method may serve as an aid for promoting interactome analysis.