Quantification of the Impact of Structure Quality on Predicted Binding Free Energy Accuracy
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Abstract
Relative binding free energy (RBFE) calculations have emerged as a powerful tool in drug discovery, capable of achieving experimental-level accuracy. However, the accuracy is compromised by a multitude of factors, including the initial structure modeling. The current study contributes to the quantification of the impact of initial structure modeling on the accuracy across a diverse set of activity cliff pairs. Along with providing a quantitative relation between the resolution of the crystal structure and free energy accuracy, we also demonstrate the incorporation of a secondary solvation tool (SOLVATE) to increase the free energy accuracy, especially when crystal waters are missing. The study also evaluates the reliability of AI-predicted structures in RBFE calculations, showing their effectiveness in predicting RBFE directionality and assigning nominal resolutions to the predicted structures based on free energy accuracy. These findings provide a set of recommendations for the development of more robust RBFE protocols, informing the use of structural data, solvation techniques, and AI-predicted protein models in drug discovery.
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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
Accurate structure prediction of biomolecular interactions with AlphaFold 3
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861 · see citing works
Deep mutational scanning: a new style of protein science
Fowler, D. M., & Fields, S. (2014) · Nature Methods · doi:10.1038/nmeth.3027 · Cited by 1,287 · see citing works
Impact of protein conformational diversity on AlphaFold predictions
Saldaño, T., Escobedo, N., Marchetti, J., Zea, D. J., Mac Donagh, J., Velez Rueda, A. J., Gonik, E., Melani, A. G., Novomisky Nechcoff, J., Salas, M. N., et al. (2021) · doi:10.1101/2021.10.27.466189 · Cited by 196 · see citing works
Predictions of Ligand Selectivity from Absolute Binding Free Energy Calculations
Aldeghi, M., Heifetz, A., Bodkin, M. J., Knapp, S., & Biggin, P. C. (2017) · Journal of the American Chemical Society · doi:10.1021/jacs.6b11467 · Cited by 177 · see citing works
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- Authors
- Behera, S., Hahn, D. F., Wilson, C. J., Marsili, S., Tresadern, G., Gapsys, V., & de Groot, B. L.
- Affiliations
- Max Planck Institute for Multidisciplinary Sciences (DE)
- Janssen (Belgium) (BE)
- Cilag (Switzerland) (CH)
- Computational Biomolecular Dynamics Group
- Corresponding author
- Gapsys, Vytautas
- ORCID
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2025-06-30
- Volume
- 65
- Issue
- 13
- Pages
- 6927-6938
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- PMC
- PMC12264970
- OpenAlex
- W4411780513
- Cited by
- 14
- References
- 5
- Citations by year
- 2026: 9 · 2025: 5
- FWCI
- 7.24
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Chemical Information and Modeling (publishedVersion, cc-by)
- ChemRxiv (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion, cc-by)
- Funders
- European Commission (HORIZON-EUROHPC-JU-2021-COE-01-02)
- Johnson & Johnson Innovative Medicine
- 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
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- relative binding free energy, structural quality, activity cliffs, solvates, structure-based drug design
- MeSH terms
- Models, Molecular, Protein Binding, Protein Conformation, Proteins, Thermodynamics, Drug Discovery
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