Key-cutting machine: A novel optimization framework for tailored protein and peptide design

Abstract

Computational protein and peptide design is emerging as a transformative framework for engineering macromolecules with precise structures and functions, offering innovative solutions in medicine, biotechnology, and materials science. However, current methods predominantly rely on generative models, which are expensive to train and inflexible to modify. Here, we introduce the Key-Cutting Machine (KCM), a novel optimization-based platform that iteratively leverages structure prediction to match desired backbone geometries. KCM requires only a single GPU and enables seamless incorporation of user-defined requirements into the objective function, circumventing the high retraining costs typical of generative models while allowing straightforward assessment of measurable properties. By employing an Estimation of Distribution Algorithm, KCM optimizes sequences based on geometric, physicochemical, and energetic criteria. We benchmarked its performance on -helices, {beta}-sheets, and unstructured regions, demonstrating precise backbone geometry design. As a proof of concept, we applied KCM to antimicrobial peptide (AMP) design by using a template AMP as the "key", yielding a candidate with potent in vitro activity against multiple bacterial strains and efficacy in a murine infection model. KCM thus emerges as a robust tool for de novo protein and peptide design, offering a flexible paradigm for replicating and extending the structure-function relationships of existing templates.