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Rational Design of AI-Driven Self-Assembling Antimicrobial Peptides and Investigation of Their Mechanism Against Multidrug-Resistant Bacterial Infections

ABSTRACT: With the growing severity of antibiotic resistance, the development of novel antimicrobial agents is urgently needed. Antimicrobial peptides (AMPs) have emerged as a research hotspot due to their broad-spectrum activity and low resistance potential, yet traditional development methods remain inefficient. This study integrates artificial intelligence (AI) with self-assembly engineering strategies, utilizing the lead peptide SMAP-29 as a template for optimization. Through a multi-step AI framework (combining generative and discriminative models), the more potent and safer derivative peptide SMAP10A was efficiently designed. SMAP10A demonstrated outstanding efficacy against multidrug-resistant bacteria both in vitro and in vivo, with a potential mechanism involving targeting membrane phospholipids and inducing reactive oxygen species accumulation. This project aims to further introduce hydrophobic modifications to the peptide chain, inducing its self-assembly into nanostructures to enhance membrane interaction, stability, and in vivo efficacy. This work not only provides a potent candidate molecule against resistant bacteria but also establishes a scalable AI-driven AMP development paradigm, significantly accelerating the discovery of novel antimicrobial agents.

ORGANISM(S): Staphylococcus Aureus

SUBMITTER: Wenze Wen

PROVIDER: PXD071621 | iProX | Thu Dec 04 00:00:00 GMT 2025

REPOSITORIES: iProX

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