bioRxivPharmacology & ToxicologyPreprint
AlphaFold2 structures template ligand discovery
J. Lyu, N. Kapolka, R. Gumpper, A. Alon, L. Wang, M. Jain, X. Alvarez, K. Sakamoto, Y. Kim, J. DiBerto, K. Kim, T. Tummino, S. Huang, J. Irwin, O. Tarkhanova, Y. Moroz, G. Skiniotis, A. Kruse, B. Shoichet, B. Roth
Abstract
AlphaFold2 (AF2) and RosettaFold have greatly expanded the number of structures available for structure-based ligand discovery, even though retrospective studies have cast doubt on their direct usefulness for that goal. Here, we tested unrefined AF2 models prospectively, comparing experimental hit-rates and affinities from large library docking against AF2 models vs the same screens targeting experimental structures of the same receptors. In retrospective docking screens against the {sigma}2 and the 5-HT2A receptors, the AF2 structures struggled to recapitulate ligands that we had previously found docking against the receptors experimental structures, consistent with published results. Prospective large library docking against the AF2 models, however, yielded similar hit rates for both receptors versus docking against experimentally-derived structures; hundreds of molecules were prioritized and tested against each model and each structure of each receptor. The success of the AF2 models was achieved despite differences in orthosteric pocket residue conformations for both targets versus the experimental structures. Intriguingly, against the 5-HT2A receptor the most potent, subtype-selective agonists were discovered via docking against the AF2 model, not the experimental structure. To understand this from a molecular perspective, a cryoEM structure was determined for one of the more potent and selective ligands to emerge from docking against the AF2 model of the 5-HT2A receptor. Our findings suggest that AF2 models may sample conformations that are relevant for ligand discovery, much extending the domain of applicability of structure-based ligand discovery.
- assay: protein structure
- research focus: drug response / drug discovery
- approach: deep learning
- approach: benchmark study
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