De novo multi-mechanism antimicrobial peptide design via multimodal deep learning

Abstract

Artificial intelligence (AI)-driven discovery of antimicrobial peptides (AMPs) is yet to fully utilise their three-dimensional (3D) structural characteristics, microbial specie-specific antimicrobial activities and mechanisms. Here, we constructed a QLAPD database comprising the sequence, structures and antimicrobial properties of 12,914 AMPs. QLAPD underlies a multimodal, multitask, multilabel, and conditionally controlled AMP discovery (M3-CAD) pipeline, which is proposed for thede novodesign of multi-mechanism AMPs to combat multidrug-resistant organisms (MDROs). This pipeline integrates the generation, regression, and classification modules, using a innovative 3D voxel coloring method to capture the nuanced physicochemical context of amino acids, significantly enhancing structural characterizations. QL-AMP-1, discovered by M3-CAD, which possesses four antimicrobial mechanisms, exhibited low toxicity and significant activity against MDROs. The skin wound infection model demonstrates its considerable antimicrobial effects and negligible toxicity. Altogether, integrating 3D features, specie-specific antimicrobial activities and mechanisms enhanced AI-driven AMP discovery, making the M3-CAD pipeline a viable tool forde novoAMP design.