Refining responsive antimicrobial nanomaterials from random peptide libraries via machine learning

Abstract

Antimicrobial resistance has evolved into one of the most serious threats to global public health, yet generalizable routes for refining random peptide mixtures (RPMs) into defined, stimuli-responsive, and low-cost antimicrobial formulations remain limited. Here, a refinement framework is presented. It centers on machine learning and co-assembly that converts broad-spectrum RPMs into interpretable antimicrobial peptide cocktails without exhaustive screening. Specifically, starting from a 10-mer Arg/Leu RPM (RL0.5), its antimicrobial activity and self-assembly are quantified, and machine learning is used to prioritize key functional peptides. Leveraging synergistic and co-assembly behaviors, an optimal combination (AEP) is selected. The resulting defined formulation, RL10, achieves a fourfold increase in in vitro activity against Escherichia coli and exhibits a reduced critical aggregation concentration relative to the starting RPM. Overall, this study presents a practical path from complex, low-cost precursors to efficient, co-assembling antimicrobial cocktails, and summarizes explainable design rules that support engineering and industrialization.

Keywords

antimicrobial peptides / industrialization / machine learning models / random peptide mixtures / responsive peptide cocktails / self-assembling nanomaterials

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