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Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery.
Zhang, Haiping; Fan, Hongjie; Wang, Jixia; Hou, Tao; Saravanan, Konda Mani; Xia, Wei; Kan, Hei Wun; Li, Junxin; Zhang, John Z H; Liang, Xinmiao; Chen, Yang.
  • Zhang H; Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Boulevard, Nanshan District, Shenzhen 518055, Guangdong Province, China.
  • Fan H; Ganjiang Chinese Medicine Innovation Center, Xinqizhou East Road 888, Ganjiang New Area, Nanchang 330000, China.
  • Wang J; Ganjiang Chinese Medicine Innovation Center, Xinqizhou East Road 888, Ganjiang New Area, Nanchang 330000, China.
  • Hou T; CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, No. 457 Zhongshan Road, Dalian 116023, China.
  • Saravanan KM; Ganjiang Chinese Medicine Innovation Center, Xinqizhou East Road 888, Ganjiang New Area, Nanchang 330000, China.
  • Xia W; CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, No. 457 Zhongshan Road, Dalian 116023, China.
  • Kan HW; Department of Biotechnology, Bharath Institute of Higher Education and Research, Agharam Road 173, Selaiyur, Chennai, Tamil Nadu 600073, India.
  • Li J; Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Boulevard, Nanshan District, Shenzhen 518055, Guangdong Province, China.
  • Zhang JZH; Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Boulevard, Nanshan District, Shenzhen 518055, Guangdong Province, China.
  • Liang X; Shenzhen Laboratory of Human Antibody Engineering, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Boulevard, Nanshan District, Shenzhen 518055, Guangdong Province, China.
  • Chen Y; Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Boulevard, Nanshan District, Shenzhen 518055, Guangdong Province, China.
Brief Bioinform ; 25(4)2024 May 23.
Article en En | MEDLINE | ID: mdl-38864340
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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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Receptores Acoplados a Proteínas G / Descubrimiento de Drogas Límite: Humans Idioma: En Año: 2024 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Receptores Acoplados a Proteínas G / Descubrimiento de Drogas Límite: Humans Idioma: En Año: 2024 Tipo del documento: Article