Single-sequence deep learning delivers crystal-quality models of covalent K-Ras G12 hotspot complexes

Abstract

Structure-based design of covalent drugs has achieved tremendous success by understanding and leveraging the three-dimensional interactions between small-molecule drug candidates and their protein targets. However, this approach traditionally relies on high-resolution co-complex structures obtained by X-ray crystallography, NMR, or cryo-EM, methods that are time-consuming and resource-intensive. Here we show that Chai-1, a publicly available structure prediction tool that accepts user-defined ligands, is able to accurately predict covalent K-Ras(G12C) complexes without using a multiple sequence alignment (MSA). Chai-1 yields pocket-aligned RMSDs View larger version (37K): org.highwire.dtl.DTLVardef@bcbad5org.highwire.dtl.DTLVardef@8e2baborg.highwire.dtl.DTLVardef@1d507a2org.highwire.dtl.DTLVardef@e851ef_HPS_FORMAT_FIGEXP M_FIG C_FIG