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

• Published in: Computational and structural biotechnology journal Vol. 21; pp. 1995 - 2008
• Main Authors: Kumar, Shreya, Balaya, Rex Devasahayam Arokia, Kanekar, Saptami, Raju, Rajesh, Prasad, Thottethodi Subrahmanya Keshava, Kandasamy, Richard K.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2023; Research Network of Computational and Structural Biotechnology; Elsevier
• Subjects: amino acid composition; biofilm; biomarkers; biotechnology; BIP Inhibitors; computer simulation; digestive system; drugs; gene expression; Gram-positive bacteria; humans; In silico approaches; peptides; prediction; QSP inhibitors; QSP predictors; Quorum sensing peptides; Review; surveys; therapeutics; BFE; RAP; QSPR; GDC; TRG; Gram-positive bacteria; 3-HBA; TRAP; HNP; DT; QSP predictors; MRSA; MD; MDR; BLAST; WHO; ML; QS; AAC; AMP; ROC; AIP; DCH; PSM; H-Kinase; QSCN; IT; Quorum sensing peptides; HGM; RF; OMR; In silico approaches; GNB; PCP; SIT; CADD; ABC; FDA; D; TCS; SAR; H; PTM; OVP; KNN; SVM; PDB; QSHGM; GBM; mRMR; QSIM; CSP; SFS; ACD; QSI; MSL; BIP; MCC; Agr; QSP; PPIs; QSP inhibitors; CTD; HAM; BNB; H-phosphotransferase; RIP; ATP; BIP Inhibitors; AIP, Autoinducing Peptide; SVM, Support Vector Machine; ML, Machine Learning; GNB, Gaussian NB; QS, Quorum Sensing; D, Aspartate; ATP, Adenosine Triphosphate; PSM, Phenol-Soluble Modulin; PPIs, Protein-Protein Interactions; MRSA, Methicillin Resistant S. aureus; QSP, QS Peptides; AAC, Amino Acid Composition; ABC, ATP-binding cassette; CSP, Competence Stimulating Peptide; BLAST, Basic Local Alignment Search Tool; H-Kinase, Histidine Kinase; HGM, Human Gut Microbiota; QSPR, Quantitative Structure Property Relationship; QSIM, QS Interference Molecules; TCS, Two-Component Sensory; TRAP, Target of RAP; OMR, Omargliptin; QSI, QS Inhibitors; HAM, Hamamelitannin; DT, Decision Tree; MD, Molecular Dynamics; RIP, RNAIII-inhibiting peptide; IT, Information Theory Features; MSL, Multiple Sequence Alignment; GBM, Gradient Boosting Machine; H-phosphotransferase, Histidine Phosphotransferase; BNB, Bernoulli Naïve-Bayes; QSCN, QS communication network; SAR, Structure-Activity Relationship; H, Histidine; mRMR, minimum Redundancy and Maximum Relevance; PCP, Physicochemical Properties; SIT, Sitagliptin; CADD, Computer-Aided Drug Design; TRG, Trelagliptin; AMP, Anti-Microbial Peptide; MCC, Mathew Co-relation Coefficient; QSHGM, Quorum Sensing of Human Gut Microbes; 3-HBA, 3–Hydroxybenzoic Acid; DCH, 3,3′-(3,4-dichlorobenzylidene)-bis-(4-hydroxycoumarin); RAP, RNAIII-activating protein; PTM, Post Translational Modification; HNP, Human Neutrophil Peptide; WHO, World Health Organization; PDB, Protein Data Bank; RF, Random Forest; CTD, Composition-Transition-Distribution; OVP, Overlapping Property Features; GDC, g-gap Dipeptide; FDA, Food and Drug Administration; ROC, Receiver Operating Characteristic; Agr, Accessory gene regulator; KNN, K-Nearest Neighbors; BIP, Biofilm Inhibitory Peptides; SFS, Sequential Forward Search; BFE, Binding Free Energy; MDR, Multiple Drug Resistance; ACD, Available Chemicals Database
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2023.02.051

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. [Display omitted]