AI-Powered Structural Genomics

Abstract

Structural genomics -- the systematic determination of three-dimensional protein structures for all gene products in an organism -- has been transformed by AI-based structure prediction, most notably AlphaFold2, which predicted structures for 214 million proteins with experimental-level accuracy. However, static predicted structures represent only one conformational state, missing the dynamic ensembles, ligand-bound states, post-translational modification effects, and mutation-induced structural changes essential for functional annotation and drug discovery. This study developed and benchmarked six structural genomics approaches -- homology modelling (SWISS-MODEL), ab initio prediction (Rosetta), AlphaFold2 single-state, molecular dynamics ensemble generation (OpenMM), normal mode analysis (ANM), and a proposed AI-Powered Structural Genomics Engine (AISGE) combining AlphaFold2 multi-state prediction with ESM-2-guided conformational sampling, ligand-aware structure refinement, mutation impact modelling via protein language model fine-tuning, and molecular dynamics-validated ensemble generation -- for comprehensive structural characterisation of 2,400 human proteins spanning enzyme active sites, signalling conformational switches, intrinsically disordered regions, and disease-associated mutation hotspots. AISGE achieved the highest structural accuracy across all categories: enzyme active-site RMSD 0.86 +- 0.24 Angstrom (vs AlphaFold2 1.42 +- 0.48; p < 0.001), conformational state coverage 82.4% of known states captured (vs AlphaFold2 38.6%; p < 0.001), mutation impact prediction DeltaDeltaG correlation r = 0.78 (vs FoldX 0.62; p < 0.001), and disordered region ensemble quality (radius of gyration within 8.4% of SAXS measurements vs 24.6% for static AlphaFold2). AISGE generated conformational ensembles for 2,400 proteins in a median 12.4 minutes per protein (vs 48 hours for equivalent MD simulation), enabling proteome-scale dynamic structural characterisation. These results establish AI-powered multi-state structural prediction as the next frontier beyond static structure determination