Improving Recognition of Antimicrobial Peptides and Target Selectivity through Machine Learning and Genetic Programming.

Growing bacterial resistance to antibiotics is spurring research on utilizing naturally-occurring antimicrobial peptides (AMPs) as templates for novel drug design. While experimentalists mainly focus on systematic point mutations to measure the effect on antibacterial activity, the computational community seeks to understand what determines such activity in a machine learning setting. The latter seeks to identify the biological signals or features that govern activity. In this paper, we advance research in this direction through a novel method that constructs and selects complex sequence-based features which capture information about distal patterns within a peptide. Comparative analysis with state-of-the-art methods in AMP recognition reveals our method is not only among the top performers, but it also provides transparent summarizations of antibacterial activity at the sequence level. Moreover, this paper demonstrates for the first time the capability not only to recognize that a peptide is an AMP or not but also to predict its target selectivity based on models of activity against only Gram-positive, only Gram-negative, or both types of bacteria. The work described in this paper is a step forward in computational research seeking to facilitate AMP design or modification in the wet laboratory.