A novel method to improve recognition of antimicrobial peptides through distal sequence-based features

Growing bacterial resistance to antibiotics is urging the development of new lines of treatment. The discovery of naturally-occurring antimicrobial peptides (AMPs) is motivating many experimental and computational researchers to pursue AMPs as possible templates. In the experimental community, the focus is generally on systematic point mutation studies to measure the effect on antibacterial activity. In the computational community, the goal is to understand what determines such activity in a machine learning setting. In the latter, it is essential to identify biological signals or features in AMPs that are predictive of antibacterial activity. Construction of effective features has proven challenging. In this paper, we advance research in this direction. We propose a novel method to construct and select complex sequence-based features able to capture information about distal patterns within a peptide. Thorough comparative analysis in this paper indicates that such features compete with the state-of-the-art in AMP recognition while providing transparent summarizations of antibacterial activity at the sequence level. We demonstrate that these features can be combined with additional physicochemical features of interest to a biological researcher to facilitate specific AMP design or modification in the wet laboratory. Code, data, results, and analysis accompanying this paper are publicly available online at: http://cs.gmu.edu/~ashehu/?q=OurTools.