Silicon exaptation discovers antimicrobial peptides
Advances in machine learning (ML) have enabled the de novo design of synthetic proteins and peptides tailored for specific structural or functional properties. As ML becomes a central tool in forward design, we suggest that its value extends beyond optimizing known objectives. It can also lead to what we term here in this work as silicon exaptation, revealing unexpected functional properties that were never part of the original design goals. This reflects a principle common in biology, that repurposes traits where features evolved for one role can later enable unforeseen functions. In this context, these accidental findings are not constrained by preconceptions but arise from latent function emergence and the algorithm's gradual drift, a new process that can co-evolves with human curiosity, intuition, and interpretive reasoning, which can be used to recognize unexpected patterns and transform them into new discoveries. In this study, we discovered a set of peptides through silicon exaptation, in which the ML pipeline was originally optimized for structural characteristics unrelated to any antimicrobial functionalities. The peptides were originally designed to adopt defined secondary structures in response to environmental stimuli, with no antimicrobial properties intended or included in the training objectives. We systematically evaluated the antimicrobial potential of these peptides using broth microdilution assays and membrane integrity tests, identifying several candidates with potent and selective antibacterial activity without detectable cytotoxicity toward mammalian cells.