Abstract
AbstractProtein-ligand docking is an established tool in drug discovery and development to narrow down potential therapeutics for experimental testing. However, a high-quality protein structure is required and often the protein is treated as fully or partially rigid. Here we develop an AI system that can predict the fully flexible all-atom structure of protein-ligand complexes directly from sequence information. We find that classical docking methods are still superior, but depend upon having crystal structures of the target protein. In addition to predicting flexible all-atom structures, predicted confidence metrics (plDDT) can be used to select accurate predictions as well as to distinguish between strong and weak binders. The advances presented here suggest that the goal of AI-based drug discovery is one step closer, but there is still a way to go to grasp the complexity of protein-ligand interactions fully. Umol is available at: https://github.com/patrickbryant1/Umol.
Funder
European Commission
Deutsche Forschungsgemeinschaft
Einstein Stiftung Berlin
Publisher
Springer Science and Business Media LLC
Reference40 articles.
1. Li, Y. Y. & Jones, S. J. M. Drug repositioning for personalized medicine. Genome Med 4, 1–14 (2012).
2. Corso, G., Stärk, H., Jing, B., Barzilay, R. & Jaakkola, T. DiffDock: diffusion steps, twists, and turns for molecular docking. Available: http://arxiv.org/abs/2210.01776 (2022).
3. Masha, K., Noh, J. J. & Dror, R. O. How accurately can one predict drug binding modes using AlphaFold models? Elife 12, RP89386 (2023).
4. Lu, W. et al. TANKBind: Trigonometry-aware neural networks for drug-protein binding structure prediction. Preprint at bioRxiv. p. 2022.06.06.495043. https://doi.org/10.1101/2022.06.06.495043 (2022).
5. Stärk, H., Ganea, O.-E., Pattanaik, L., Barzilay, R. & Jaakkola T. EquiBind: Geometric deep learning for drug binding structure prediction. Available: http://arxiv.org/abs/2202.05146 (2022).
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