Structure prediction of protein-ligand complexes from sequence information with Umol
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Abstract
Protein-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 .
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2022) · doi:10.1101/2022.12.09.519842 · Cited by 2,369 · see citing works
De novo design of protein structure and function with RFdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2023) · Nature · doi:10.1038/s41586-023-06415-8 · Cited by 2,325 · see citing works
Uni-Mol: A Universal 3D Molecular Representation Learning Framework
Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., & Ke, G. (2022) · doi:10.26434/chemrxiv-2022-jjm0j · Cited by 81 · see citing works
Orešič, M., Seppänen-Laakso, T., Sun, D., Tang, J., Therman, S., Viehman, R., Mustonen, U., van Erp, T. G., Hyötyläinen, T., Thompson, P., et al. (2012) · Genome Medicine · doi:10.1186/gm300 · Cited by 5 · see citing works
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AlphaFold2 enables accurate deorphanization of ligands to single-pass receptors
Banhos Danneskiold-Samsøe, N., Kavi, D., Jude, K. M., Nissen, S. B., Wat, L. W., Coassolo, L., Zhao, M., Santana-Oikawa, G. A., Broido, B. B., Garcia, K. C., et al. (2024) · Cell Systems · doi:10.1016/j.cels.2024.10.004 · Cited by 35 · see citing works
- Authors
- Bryant, P., Kelkar, A., Guljas, A., Clementi, C., & Noé, F.
- Affiliations
- Stockholm University (SE)
- Freie Universität Berlin (DE)
- Corresponding author
- Bryant, Patrick
- Venue
- Nature Communications
- Published
- 2024-05-28
- Volume
- 15
- Issue
- 1
- Pages
- 4536-4536
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC11133481
- OpenAlex
- W4399070999
- Cited by
- 75
- References
- 6
- Citations by year
- 2026: 19 · 2025: 36 · 2024: 19 · 2023: 1
- FWCI
- 15.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission (ERC CoG 772230)
- Deutsche Forschungsgemeinschaft (SFB 1114 porjects A04 and C03, RTG 2433 Project Q04, SFB/TRR 186 Project A12)
- Einstein Stiftung Berlin (0420815101)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Keywords
- protein-ligand docking, sequence-based structure prediction, protein flexibility
- MeSH terms
- Binding Sites, Ligands, Protein Binding, Protein Conformation, Proteins, Software, Drug Discovery, Molecular Docking Simulation
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