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Are Deep Learning Structural Models Sufficiently Accurate for Free Energy Calculations? Application of FEP+ to AlphaFold2 Predicted Structures
Open accessPreprint — published as 10.1021/acs.jcim.2c00796 · Paper page
Abstract
The availability of AlphaFold2 has led to great excitement in the scientific community - particularly among drug hunters - due to the ability of the algorithm to predict protein structures with high accuracy. However, beyond globally accurate protein structure prediction, it remains to be determined whether ligand binding sites are predicted with sufficient accuracy in these structures to be useful in supporting computationally driven drug discovery programs. We explored this question by performing free energy perturbation (FEP) calculations on a set of well-studied protein-ligand complexes, where AlphaFold2 predictions were performed by removing all templates with >30% identity to the target protein from the training set. We observed that in most cases, the ΔΔG values for ligand transformations calculated with FEP, using these prospective AlphaFold2 structures, were comparable in accuracy to the corresponding calculations previously carried out using X-ray structures. We conclude that under the right circumstances, AlphaFold2 modeled structures are accurate enough to be used by physics-based methods such as FEP, in typical lead optimization stages of a drug discovery program.
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Highly accurate protein structure prediction with AlphaFold
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Metadata
- Authors
- Beuming, T., Martín, H., Díaz-Rovira, A. M., Díaz, L., Guallar, V., & Ray, S. S.
- Affiliations
- Barcelona Supercomputing Center (ES)
- Corresponding author
- Beuming, Thijs
- Ray, Soumya S.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- openRxiv
- Published
- 2022-08-16
- Volume
- 62
- Issue
- 18
- Pages
- 4351-4360
- ISSN
- 1549-9596 (ISSN-L)
- OpenAlex
- W4295757800
- Cited by
- 61
- References
- 1
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 24 · 2023: 21 · 2022: 1
- FWCI
- 7.57
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nd
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- Funders
- Ministerio de Ciencia e Innovación (PTQ2018-009991)
- Agencia Estatal de Investigación (AEI//10.13039/501100011033/, 501100011033, 10.13039/501100011033, MCIN/AEI/10.13039/501 100 011 033, 13039, .13039/501100011033, 10.13039)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › 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
- AlphaFold2, free energy perturbation, protein-ligand binding free energy, ligand binding site prediction, deep learning protein structure prediction, drug discovery, lead optimization, protein-ligand complex, binding affinity
- MeSH terms
- Deep Learning, Ligands, Models, Structural, Prospective Studies, Protein Binding, Proteins, Thermodynamics, Molecular Dynamics Simulation
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