connecting peptide physicochemical and antimicrobial properties by a rational prediction model连接肽理化和抗菌性的理性的预测模型.pdf

Connecting Peptide Physicochemical and Antimicrobial Properties by a Rational Prediction Model 1,2 2 ` ´ 1 1 Marc Torrent *, David Andreu , Victoria M. Nogues , Ester Boix ` ` 1 Department of Biochemistry and Molecular Biology, Biosciences Faculty, Universitat Autonoma de Barcelona, Cerdanyola del Valles, Spain, 2 Department of Experimental and Health Sciences, Barcelona Biomedical Research Park (PRBB), Pompeu Fabra University, Barcelona, Spain Abstract The increasing rate in antibiotic-resistant bacterial strains has become an imperative health issue. Thus, pharmaceutical industries have focussed their efforts to find new potent, non-toxic compounds to treat bacterial infections. Antimicrobial peptides (AMPs) are promising candidates in the fight against antibiotic-resistant pathogens due to their low toxicity, broad range of activity and unspecific mechanism of action. In this context, bioinformatics’ strategies can inspire the design of new peptide leads with enhanced activity. Here, we describe an artificial neural network approach, based on the AMP’s physicochemical characteristics, that is able not only to identify active peptides but also to assess its antimicrobial potency. The physicochemical properties considered are directly derived from the peptide sequence and comprise a complete set of parameters that accurately describe AMPs. Most interesting, the results obtained dovetail with a model for the AMP’s mechanism of action that takes into account new concepts such as peptide aggregation. Moreover, this classification system displays