ShanghaiTech University Knowledge Management System
| SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction | |
| 2024-11-01 | |
| 发表期刊 | NATURE METHODS (IF:36.1[JCR-2023],45.6[5-Year]) |
| ISSN | 1548-7091 |
| EISSN | 1548-7105 |
| DOI | 10.1038/s41592-024-02516-y |
| 摘要 | ["Accurately predicting protein-ligand interactions is crucial for understanding cellular processes. We introduce SurfDock, a deep-learning method that addresses this challenge by integrating protein sequence, three-dimensional structural graphs and surface-level features into an equivariant architecture. SurfDock employs a generative diffusion model on a non-Euclidean manifold, optimizing molecular translations, rotations and torsions to generate reliable binding poses. Our extensive evaluations across various benchmarks demonstrate SurfDock's superiority over existing methods in docking success rates and adherence to physical constraints. It also exhibits remarkable generalizability to unseen proteins and predicted apo structures, while achieving state-of-the-art performance in virtual screening tasks. In a real-world application, SurfDock identified seven novel hit molecules in a virtual screening project targeting aldehyde dehydrogenase 1B1, a key enzyme in cellular metabolism. This showcases SurfDock's ability to elucidate molecular mechanisms underlying cellular processes. These results highlight SurfDock's potential as a transformative tool in structural biology, offering enhanced accuracy, physical plausibility and practical applicability in understanding protein-ligand interactions.","SurfDock is a method for predicting protein-ligand complex structures by leveraging multimodal protein information and generative diffusion frameworks. Its results can be generalized to unseen proteins and real-world settings."] |
| URL | 查看原文 |
| 收录类别 | PPRN.PPRN ; SCI |
| 语种 | 英语 |
| 资助项目 | National Natural Science Foundation of China["T2225002","82273855","82204278"] ; Strategic Priority Research Program of the Chinese Academy of sciences[XDB0850000] ; National Key Research and Development Program of China["2022YFC3400504","2023YFC2305904"] ; SIMM-SHUTCM Traditional Chinese Medicine Innovation Joint Research Program[E2G805H] ; Youth Innovation Promotion Association CAS[2023296] |
| WOS研究方向 | Biochemistry & Molecular Biology |
| WOS类目 | Biochemical Research Methods |
| WOS记录号 | WOS:001365170700001 |
| 出版者 | NATURE PORTFOLIO |
| 引用统计 | 正在获取...
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| 文献类型 | 期刊论文 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/381372 |
| 专题 | 物质科学与技术学院 信息科学与技术学院 物质科学与技术学院_博士生 信息科学与技术学院_博士生 |
| 通讯作者 | Zheng, Mingyue |
| 作者单位 | 1.Zhejiang Univ, Innovat Inst Artificial Intelligence Med, Coll Pharmaceut Sci, Hangzhou, Zhejiang, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai, Peoples R China 3.ShanghaiTech Univ, Sch Phys Sci & Technol, Shanghai, Peoples R China 4.Lingang Lab, Shanghai, Peoples R China 5.Univ Chinese Acad Sci, Beijing, Peoples R China 6.Nanchang Univ, Nanchang, Peoples R China 7.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China 8.Tsinghua Univ, Inst AI Ind Res AIR, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cao, Duanhua,Chen, Mingan,Zhang, Runze,et al. SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction[J]. NATURE METHODS,2024. |
| APA | Cao, Duanhua.,Chen, Mingan.,Zhang, Runze.,Wang, Zhaokun.,Huang, Manlin.,...&Zheng, Mingyue.(2024).SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction.NATURE METHODS. |
| MLA | Cao, Duanhua,et al."SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction".NATURE METHODS (2024). |
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