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Revolutionizing GPCR–ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery
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Abstract
G-protein coupled receptors (GPCRs), crucial in various diseases, are targeted of over 40% of approved drugs. However, the reliable acquisition of experimental GPCRs structures is hindered by their lipid-embedded conformations. Traditional protein–ligand interaction models falter in GPCR–drug interactions, caused by limited and low-quality structures. Generalized models, trained on soluble protein–ligand pairs, are also inadequate. To address these issues, we developed two models, DeepGPCR_BC for binary classification and DeepGPCR_RG for affinity prediction. These models use non-structural GPCR–ligand interaction data, leveraging graph convolutional networks and mol2vec techniques to represent binding pockets and ligands as graphs. This approach significantly speeds up predictions while preserving critical physical–chemical and spatial information. In independent tests, DeepGPCR_BC surpassed Autodock Vina and Schrödinger Dock with an area under the curve of 0.72, accuracy of 0.68 and true positive rate of 0.73, whereas DeepGPCR_RG demonstrated a Pearson correlation of 0.39 and root mean squared error of 1.34. We applied these models to screen drug candidates for GPR35 (Q9HC97), yielding promising results with three (F545-1970, K297-0698, S948-0241) out of eight candidates. Furthermore, we also successfully obtained six active inhibitors for GLP-1R. Our GPCR-specific models pave the way for efficient and accurate large-scale virtual screening, potentially revolutionizing drug discovery in the GPCR field.
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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
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · cited 1×
“However, due to the unavailability of experimental 3D structures and binding pocket information for these GPCRs,we resorted to obtaining the AlphaFold-predicted 3D structures [33] and utilizing COFACTOR [34] to predict the ligand binding pocket (Fig.”
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Metadata
- Authors
- Zhang, H., Fan, H., Wang, J., Hou, T., Saravanan, K. M., Xia, W., Kan, H. W., Li, J., Zhang, J. Z. H., Liang, X., & Chen, Y.
- Affiliations
- Chinese Academy of Sciences (CN)
- Ganjiang Chinese Medicine Innovation Center , Xinqizhou East Road 888, Ganjiang New Area, Nanchang 330000 , China
- Dalian Institute of Chemical Physics (CN)
- Bharath University (IN)
- Corresponding author
- Zhang, Haiping
- Zhang, John Z H
- Liang, Xinmiao
- Chen, Yang
- ORCID
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-05-23
- Volume
- 25
- Issue
- 4
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- PMC
- PMC11167311
- OpenAlex
- W4399597376
- Cited by
- 25
- References
- 2
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 4
- FWCI
- 5.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Briefings in Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Science Foundation of China (62106253, 21933010, 32060209, 82103187, 22250710136, 22333006)
- Shenzhen Key Projects (JCYJ20220818100804009)
- Jiangxi Provincial Natural Science Foundation of China (20232BBH80012, 20224ACB216012)
- Liaoning Provincial Natural Science Foundation of China (2022-MS-017)
- Dalian High-Level Talent Support Program (2021RQ003)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- G protein-coupled receptors, graph convolutional networks, binding affinity prediction, virtual screening, GPR35, GLP-1 receptor, drug discovery, protein-ligand interactions
- MeSH terms
- Binding Sites, Humans, Ligands, Protein Binding, Receptors, G-Protein-Coupled, Drug Discovery, Molecular Docking Simulation
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