关键词： 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

来  源：   DOI：10.1016/j.csbj.2023.02.051   PDF(Pubmed)

Abstract：
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.
摘要：
革兰氏阳性细菌中的重要细胞功能由称为群体感应肽（QSP）的信号分子控制，被认为是对细菌感染的有希望的治疗干预措施。在细菌系统中，QSP与膜偶联受体结合，然后自动磷酸化并激活细胞内反应调节剂。这些反应调节剂诱导细菌中的靶基因表达。毒力相关分子靶标的药物发现研究中最可靠的趋势之一是使用肽药物或新功能。从这个角度来看,计算方法作为生物学家的辅助辅助手段，其中基于机器学习和计算机分析的方法被开发为用于目标肽鉴定的合适工具。因此,识别或预测这些QSP及其受体和抑制剂的快速可靠的计算资源的开发正受到相当大的关注。人体肠道微生物的Quorumpeps和QuorumSensing（QSHGM）等数据库提供了QSP结构和功能的详细概述。QSPpred等工具和算法，QSPred-FL,iQSP,EnsembleQS和PEPred-Suite已用于QSP和特征表示的通用预测。基于氨基酸组成利用肽特征的编译关键资源的可用性，位置偏好，和基序以及结构和物理化学性质，包括生物膜抑制肽，可以帮助阐明感染性革兰氏阳性病原体中的QSP和膜受体相互作用。在这里，我们提供了适用于检测QSP和QS干扰分子的各种计算方法的全面调查。这篇综述强调了这些方法用于开发针对感染性革兰氏阳性病原体的潜在生物标志物的实用性。
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