Conserved protein sequence-structure signatures identify antibiotic resistance genes from the human microbiome

Bacteria exhibiting antimicrobial resistance (AMR) are a significant public health challenge worldwide. The human microbiome is a known repository of key determinants of AMR, antibiotic resistance genes (ARGs). Identifying previously undetected ARGs in the human microbiome that confer resistance to clinical concentrations of antibiotics is a crucial component of addressing AMR, yet can be hindered by their low homology to existing ARGs. Here, we address this by focussing on functionally important protein regions. ARG-PASS (ARG-PAirwise Sequence vs Structure) is a protein function prediction method which leverages a one-class support vector machine trained on pairwise primary and tertiary distributions of structurally conserved regions of proteins encoded by ARGs. ARG-PASS was applied to six reference strains of the Human Microbiome Project. Nine candidates were selected for experimental verification, and all were functionally confirmed when expressed in E.coli , belonging to ARG classes: APH, dfr, class B and C β-lactamases, and penicillin-binding proteins. We also used ARG-PASS directly on protein structures within the AlphaFold database and identified a phnP gene (metallo-β-lactamase fold) with low similarity to characterised β-lactamases and media-dependent ampicillin activity. In total, 80% of the tested genes confer resistance at CLSI resistant breakpoints and the remainder represent ‘pre-resistance’ genes, with activity but not at a clinically relevant minimum inhibitory concentration. We suggest pre-resistance genes may preferentially evolve into clinically relevant resistance determinants. ARG-PASS provides a precise computational approach that integrates sequence and structure to identify divergent ARGs that evade homology-based detection, supporting improved resistance surveillance and antibiotic stewardship.