title: Computational resources and tools for antimicrobial peptides.
abstract: Antimicrobial peptides (AMPs), as evolutionarily conserved components of innate immune system, protect against pathogens including bacteria, fungi, viruses, and parasites. In general, AMPs are relatively small peptides (<10 kDa) with cationic nature and amphipathic structure and have modes of action different from traditional antibiotics. Up to now, there are more than 19 000 AMPs that have been reported, including those isolated from nature sources or by synthesis. They have been considered to be promising substitutes of conventional antibiotics in the quest to address the increasing occurrence of antibiotic resistance. However, most AMPs have modest direct antimicrobial activity, and their mechanisms of action, as well as their structure-activity relationships, are still poorly understood. Computational strategies are invaluable assets to provide insight into the activity of AMPs and thus exploit their potential as a new generation of antimicrobials. This article reviews the advances of AMP databases and computational tools for the prediction and design of new active AMPs. Copyright © 2016 European Peptide Society and John Wiley & Sons, Ltd.
authors.affiliation: School of Life Science and Technology, China Pharmaceutical University, Nanjing, 210009, China.
authors.forename: Shicai
authors.initials: S
authors.lastname: Liu
authors.affiliation: School of Life Science and Technology, China Pharmaceutical University, Nanjing, 210009, China.
authors.forename: Linlin
authors.initials: L
authors.lastname: Fan
authors.affiliation: School of Life Science and Technology, China Pharmaceutical University, Nanjing, 210009, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Sun
authors.affiliation: School of Life Science and Technology, China Pharmaceutical University, Nanjing, 210009, China.
authors.forename: Xingzhen
authors.initials: X
authors.lastname: Lao
authors.affiliation: School of Life Science and Technology, China Pharmaceutical University, Nanjing, 210009, China.
authors.forename: Heng
authors.initials: H
authors.lastname: Zheng
mesh_terms: D000818:Animals; D000900:Anti-Bacterial Agents / Q000737:chemistry* / Q000302:isolation & purification / Q000494:pharmacology; D023181:Antimicrobial Cationic Peptides / Q000737:chemistry* / Q000302:isolation & purification / Q000494:pharmacology; D001419:Bacteria / Q000187:drug effects* / Q000254:growth & development; D001424:Bacterial Infections / Q000188:drug therapy* / Q000382:microbiology; D002986:Clinical Trials as Topic; D030562:Databases, Protein / Q000706:statistics & numerical data*; D015195:Drug Design; D004352:Drug Resistance, Microbial / Q000187:drug effects; D006801:Humans; D000069550:Machine Learning; D012984:Software; D013329:Structure-Activity Relationship
chemical_list: D000900:Anti-Bacterial Agents; D023181:Antimicrobial Cationic Peptides
keywords: antimicrobial peptides; database screening; databases; design; machine learning methods; prediction