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Guiding Exploration of Antimicrobial Peptide Space with a Deep Neural Network

Antibiotic resistance has become a serious concern, and many health organizations are sounding the alarm and the need for new drug templates. Naturally-occurring antimicrobial peptides (AMPs) have long promised to serve as such templates, as they have shown lower likelihood for bacteria to form resistance. This has motivated wet and dry laboratories to seek novel AMPs. The sequence diversity of these peptides renders systematic screening studies either infeasible or too narrow in scope. Dry-laboratory approaches, most notably under the umbrella of machine learning, have contributed discriminative models of increasing accuracy. In this paper we investigate the capability of a state-of-the-art discriminative model based on deep learning to guide the exploration of peptide space in search of novel AMPs. The model is encapsulated within randomized heuristic search. Results show that such a generative framework is promising at providing wet laboratories with diverse options for toxicity and further biophysical screening, constituting a first step in dry and wet-laboratory integration studies.