De novo design of functionally diverse and druggable antimicrobial peptides by diffusion and multimodal deep learning

Abstract

The escalating crisis of antibiotic resistance underscores the urgent need for innovative anti-infective agents, such as antimicrobial peptides (AMPs), though their discovery and optimization remain challenging. To address this, we introduce AMP-D3, a comprehensive framework that integrates AMP generation, identification, and screening into a cohesive workflow. Central to AMP-D3 is PACD, an advanced generator that utilizes contrastive diffusion to produce diverse, functionally tailored AMPs. For precise recognition, the CAST model combines ESM-2 embeddings with localized sequence features through cross-modal attention, yielding an accuracy of 0.891--a 2.2% enhancement over current state-of-the-art approaches. Additionally, a robust multi-attribute prediction module evaluates key properties, including antifungal and anticancer potential, across twenty-two distinct characteristics. Furthermore, a meticulously designed screening pipeline identifies high-efficacy AMP candidates against critical pathogens, such as C. albicans, E. coli, P. aeruginosa, and S. aureus. This process assessed 10,000 peptides generated by PACD alongside 43,000 from prior studies, selecting the top 100 candidates per pathogen for further analysis. Experimental validation confirms that these peptides exhibit structural diversity and binding affinities comparable to naturally occurring AMPs, as evidenced by AlphaFold2 structural predictions and molecular docking studies. Thus, AMP-D3 represents a transformative approach to the design and expedited discovery of clinically viable AMPs.