A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures

• DOI: 10.1371/journal.pcbi.1012302
• Publication Date: 2024-07-24T17:39:38Z
• Authors: Noah B. Herrington; Yan Chak Li; David Stein; Gaurav Pandey; Avner Schlessinger
• Keywords: Druggability

Protein kinase function and interactions with drugs are controlled in part by the movement of DFG ɑC-Helix motifs that related to catalytic activity kinase. Small molecule ligands elicit therapeutic effects distinct selectivity profiles residence times often depend on active or inactive conformation(s) they bind. Modern AI-based structural modeling methods have potential expand upon limited availability experimentally determined structures states. Here, we first explored conformational space kinases PDB models generated AlphaFold2 (AF2) ESMFold, two prominent protein structure prediction methods. Our investigation AF2’s ability explore diversity kinome at various multiple sequence alignment (MSA) depths showed a bias within predicted DFG-in conformations, particularly those motif, based their overabundance PDB. We demonstrate predicting using AF2 lower MSA these alternative conformations more extensively, including identifying previously unobserved for 398 kinases. Ligand enrichment analyses 23 that, average, docked distinguished between molecules decoys better than random (average AUC (avgAUC) 64.58), but select perform well (e.g., avgAUCs PTK2 JAK2 were 79.28 80.16, respectively). Further analysis explained ligand discrepancy low- high-performing as binding site occlusions would preclude docking. The overall results our suggested although uncharted regions exhibited scores suitable rational drug discovery, rigorous refinement is likely still necessary discovery campaigns.