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Computational tools for exploring peptide-membrane interactions in gram-positive bacteria.

In Computational and structural biotechnology journal

The vital cellular functions in Gram-positive bacteria are controlled by signaling molecules known as quorum sensing peptides (QSPs), considered promising therapeutic interventions for bacterial infections. In the bacterial system QSPs bind to membrane-coupled receptors, which then auto-phosphorylate and activate intracellular response regulators. These response regulators induce target gene expression in bacteria. One of the most reliable trends in drug discovery research for virulence-associated molecular targets is the use of peptide drugs or new functionalities. In this perspective, computational methods act as auxiliary aids for biologists, where methodologies based on machine learning and in silico analysis are developed as suitable tools for target peptide identification. Therefore, the development of quick and reliable computational resources to identify or predict these QSPs along with their receptors and inhibitors is receiving considerable attention. The databases such as Quorumpeps and Quorum Sensing of Human Gut Microbes (QSHGM) provide a detailed overview of the structures and functions of QSPs. The tools and algorithms such as QSPpred, QSPred-FL, iQSP, EnsembleQS and PEPred-Suite have been used for the generic prediction of QSPs and feature representation. The availability of compiled key resources for utilizing peptide features based on amino acid composition, positional preferences, and motifs as well as structural and physicochemical properties, including biofilm inhibitory peptides, can aid in elucidating the QSP and membrane receptor interactions in infectious Gram-positive pathogens. Herein, we present a comprehensive survey of diverse computational approaches that are suitable for detecting QSPs and QS interference molecules. This review highlights the utility of these methods for developing potential biomarkers against infectious Gram-positive pathogens.

Kumar Shreya, Balaya Rex Devasahayam Arokia, Kanekar Saptami, Raju Rajesh, Prasad Thottethodi Subrahmanya Keshava, Kandasamy Richard K

2023

3-HBA, 3–Hydroxybenzoic Acid, AAC, Amino Acid Composition, ABC, ATP-binding cassette, ACD, Available Chemicals Database, AIP, Autoinducing Peptide, AMP, Anti-Microbial Peptide, ATP, Adenosine Triphosphate, Agr, Accessory gene regulator, BFE, Binding Free Energy, BIP Inhibitors, BIP, Biofilm Inhibitory Peptides, BLAST, Basic Local Alignment Search Tool, BNB, Bernoulli Naïve-Bayes, CADD, Computer-Aided Drug Design, CSP, Competence Stimulating Peptide, CTD, Composition-Transition-Distribution, D, Aspartate, DCH, 3,3′-(3,4-dichlorobenzylidene)-bis-(4-hydroxycoumarin), DT, Decision Tree, FDA, Food and Drug Administration, GBM, Gradient Boosting Machine, GDC, g-gap Dipeptide, GNB, Gaussian NB, Gram-positive bacteria, H, Histidine, H-Kinase, Histidine Kinase, H-phosphotransferase, Histidine Phosphotransferase, HAM, Hamamelitannin, HGM, Human Gut Microbiota, HNP, Human Neutrophil Peptide, IT, Information Theory Features, In silico approaches, KNN, K-Nearest Neighbors, MCC, Mathew Co-relation Coefficient, MD, Molecular Dynamics, MDR, Multiple Drug Resistance, ML, Machine Learning, MRSA, Methicillin Resistant S. aureus, MSL, Multiple Sequence Alignment, OMR, Omargliptin, OVP, Overlapping Property Features, PCP, Physicochemical Properties, PDB, Protein Data Bank, PPIs, Protein-Protein Interactions, PSM, Phenol-Soluble Modulin, PTM, Post Translational Modification, QS, Quorum Sensing, QSCN, QS communication network, QSHGM, Quorum Sensing of Human Gut Microbes, QSI, QS Inhibitors, QSIM, QS Interference Molecules, QSP inhibitors, QSP predictors, QSP, QS Peptides, QSPR, Quantitative Structure Property Relationship, Quorum sensing peptides, RAP, RNAIII-activating protein, RF, Random Forest, RIP, RNAIII-inhibiting peptide, ROC, Receiver Operating Characteristic, SAR, Structure-Activity Relationship, SFS, Sequential Forward Search, SIT, Sitagliptin, SVM, Support Vector Machine, TCS, Two-Component Sensory, TRAP, Target of RAP, TRG, Trelagliptin, WHO, World Health Organization, mRMR, minimum Redundancy and Maximum Relevance