Physicochemical Determinants of Antimicrobial Activity

Antimicrobial peptides (AMPs) are lately receiving significant attention as targets for antibacterial drug research. While many machine learning techniques are shown effective for AMP recognition, their utility for the rational design of novel AMP-based drugs in the wet laboratory is questionable. In this paper we seek to elucidate determinants of antimicrobial activity in a well-studied class of AMPs, cathelicidins. We do so by considering an extensive set of physicochemical properties at the residue level as features in the context of SVM-based classification, employing a carefully-constructed decoy dataset. A detailed statistical analysis of feature profiles reveals interesting physicochemical properties to preserve when modifying or designing novel AMPs. The method presented here is a first step towards assisting de novo design of AMPs in the wet laboratory.

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