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Dynamic-GLEP: a dynamics-informed deep learning framework for ligand efficacy prediction in representative Class A GPCRs

文献类型：期刊论文

作者Chen, Zhiyi7,8; Hao, Yongxin6,7; Su, Yuhong5; Agren, Hans1; Chen, Mingan4,7; Fan, Zhehuan3,7; Cao, Duanhua2,7; Xiong, Jiacheng3,7; Zhang, Wei3,7; Liu, Jin2,7
刊名BRIEFINGS IN BIOINFORMATICS
出版日期2026
卷号27期号:1页码:15
关键词GPCR ligand efficacy molecular dynamics conformational ensembles deep learning structure-based drug design transfer learning
ISSN号1467-5463
DOI10.1093/bib/bbag049
英文摘要G protein-coupled receptors (GPCRs) represent the largest membrane protein family and remain central targets in drug discovery. Ligand efficacy reflects the ability to modulate receptor conformational states and extends beyond binding affinity to underpin functional selectivity. However, most computational approaches still emphasize affinity prediction, with limited capacity to capture the conformational dynamics driving efficacy. Here, we introduce Dynamic-GLEP, a structure- and mechanism-aware framework that integrates molecular dynamics (MD)-derived conformational ensembles with transfer learning on equivariant graph neural networks. By constructing multi-conformation receptor-ligand complexes and fine-tuning the EquiScore model, Dynamic-GLEP identifies conformation-dependent interaction features to distinguish agonists from nonagonists. Applied to the 5-HT1A receptor, the framework achieved an area under the curve (AUC) of 0.74 in cross-validation and 0.71 on an external Food and Drug Administration (FDA)-related dataset. Comparative analyses showed that Holo-based models are advantageous for scaffold optimization, whereas Apo-derived ensembles provided greater adaptability to chemically diverse ligands. Furthermore, extension to the adenosine A2A receptor yielded high performance (AUC > 0.85), underscoring the method's robustness and transferability under data-scarce conditions. Collectively, these results highlight Dynamic-GLEP as a reliable and interpretable platform for ligand efficacy prediction in Class A GPCRs, with broad potential to support virtual screening, candidate prioritization, and mechanism-driven drug design. [GRAPHICS]
WOS关键词PROTEIN ; DOCKING
资助项目SIMM-SHUTCM Traditional Chinese Medicine Innovation Joint Research Program[E2G805H] ; Shanghai Municipal Science and Technology Major Project, Strategic Priority Research Program of the Chinese Academy of Sciences[XDB0850000] ; Fund of Youth Innovation Promotion Association[2022077] ; National Academic Infrastructure for Supercomputing in Sweden (NAISS) for offering computational resources at Alvis[NAISS 2023/3-40] ; China Postdoctoral Science Foundation[2024 M763419] ; National Natural Science Foundation of China[82404513] ; National Natural Science Foundation of China[82273855] ; National Natural Science Foundation of China[T2225002] ; National Key Research and Development Program of China[2022YFC3400504] ; National Key Research and Development Program of China[2023YFC2305904] ; Shanghai Sailing Program[22YF1460800] ; Postdoctoral Fellowship Program of CPSF[GZC20232846]
WOS研究方向Biochemistry & Molecular Biology ; Mathematical & Computational Biology
语种英语
WOS记录号WOS:001689131700001
出版者OXFORD UNIV PRESS
源URL[http://119.78.100.183/handle/2S10ELR8/322954]  
专题国家级研究中心_原创新药研究全国重点实验室
通讯作者Cheng, Xi; Wang, Dingyan; Teng, Dan
作者单位1.Uppsala Univ, Dept Phys & Astron, Div Xray Photon Sci, Box 516, SE-75120 Uppsala, Sweden
2.Zhejiang Univ, Coll Pharmaceut Sci, Innovat Inst Artificial Intelligence Med, 866 Yuhangtang Rd, Hangzhou 310058, Zhejiang, Peoples R China
3.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China
4.ShanghaiTech Univ, Sch Phys Sci & Technol, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China
5.Lingang Lab, 2380 Hechuan Rd, Shanghai 200031, Peoples R China
6.Univ Sci & Technol China, Div Life Sci & Med, 443 Huangshan Rd, Hefei 230026, Anhui, Peoples R China
7.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China
8.Nanjing Univ, Sch Life Sci, 163 Xianlin Ave, Nanjing 210023, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Zhiyi,Hao, Yongxin,Su, Yuhong,et al. Dynamic-GLEP: a dynamics-informed deep learning framework for ligand efficacy prediction in representative Class A GPCRs[J]. BRIEFINGS IN BIOINFORMATICS,2026,27(1):15.
APA Chen, Zhiyi.,Hao, Yongxin.,Su, Yuhong.,Agren, Hans.,Chen, Mingan.,...&Teng, Dan.(2026).Dynamic-GLEP: a dynamics-informed deep learning framework for ligand efficacy prediction in representative Class A GPCRs.BRIEFINGS IN BIOINFORMATICS,27(1),15.
MLA Chen, Zhiyi,et al."Dynamic-GLEP: a dynamics-informed deep learning framework for ligand efficacy prediction in representative Class A GPCRs".BRIEFINGS IN BIOINFORMATICS 27.1(2026):15.

入库方式： OAI收割

来源：上海药物研究所

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