Computational Peptidology

Giuseppe Maccari, Mariagrazia Di Luca, Riccardo Nifosì

Peng Zhou, Jian Huang

The rapid spread of drug-resistant pathogenic microbial strains has created an urgent need for the development of new anti-infective molecules, having different mechanism of action in comparison to existing drugs. Natural antimicrobial peptides (AMPs) represent a novel class of molecules with a broad spectrum of activity and a low rate in inducing bacterial resistance. In particular, linear alpha-helical cationic antimicrobial peptides are among the most widespread membrane-disruptive AMPs in nature, representing a particularly successful structural arrangement of the innate defense against microbes. However, until now, many AMPs have failed in clinical trials because of several drawbacks that strongly limit their applicability such as degradation, cytotoxicity, and high production cost. Thus, to overcome the limitations of native peptides, a rational in silico approach to AMPs design becomes a promising strategy that drastically reduce production costs and the time required for evaluation of activity and toxicity.This chapter focuses on the strategies and methods for de novo design of potentially active AMPs. In particular, statistical-based design strategies and MD methods for modelling AMPs are elucidated.