PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking

doi:10.1101/2024.01.31.578200

	PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking
	Zhang, Runze 1,2; Jiang, Xinyu 1,2; Cao, Duanhua 1,3; Yu, Jie1,4,5 ; Chen, Mingan1,4,6 ; Fan, Zhehuan 1,2; Kong, Xiangtai 1,2; Xiong, Jiacheng 1,2; Zhang, Zimei 1,7; Zhang, Wei 1,2; Ni, Shengkun 1,2; Wang, Yitian 1,2; Gao, Shenghua5 ; Zheng, Mingyue 1,2
	2024-02-03
状态	已发表
摘要	Structure-based drug design (SBDD) relies on accurate knowledge of protein structure and ligand-binding conformations. However, most of the static conformations obtained by advanced methods such as structural biology and de novo protein folding algorithms often don't meet the needs for drug design. We introduce PackDock, a flexible docking method that combines "conformation selection" and "induced fit" mechanisms in a two-stage docking pipeline. The core module of this method is PackPocket, which uses a diffusion model to explore the side-chain conformation space in ligand binding pockets, both with or without a ligand. We evaluate our method using several tests that reflect real-world application scenarios. (1) Side-chain packing and Re-docking experiments validate the ability of PackDock to predict accurate side-chain conformations and ligand conformations. (2) Cross-docking experiments with apo and non-homologous ligand-induced holo structures align with real docking scenarios, demonstrating PackDock's practical value. (3) Docking experiments with hypothetical models show that PackPocket can potentially conduct SBDD starting from protein sequence information only. Additionally, we found that PackDock can identify key amino acid conformation changes, which may provide insights for lead compound optimization. We demonstrate PackDock can accurately predict the complex conformations in various application scenarios, by combining the conformation selection theory and the induced fit theory, and by using the ability of PackPocket to accurately predict the side chain conformations in the pocket region. We believe this method can improve the usability of existing structures, providing a new perspective for the SBDD community.
语种	英语
DOI	10.1101/2024.01.31.578200
相关网址	查看原文
出处	bioRxiv
收录类别	PPRN.PPRN
WOS记录号	PPRN:87477863
WOS类目	Computer Science, Interdisciplinary Applications
资助项目	National Key Research and Development Program of China["2022YFC3400504","T2225002","KF-202301","E2G805H"] ; National Natural Science Foundation of China["2023YFC2305904","82273855"]
文献类型	预印本
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/381284
专题	信息科学与技术学院物质科学与技术学院信息科学与技术学院_PI研究组_高盛华组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.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China 2.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China 3.Zhejiang Univ, Innovat Inst Artificial Intelligence Med Zhejiang Univ, Coll Pharmaceut Sci, Hangzhou 310058, Zhejiang, Peoples R China 4.Lingang Lab, Shanghai 200031, Peoples R China 5.ShanghaiTech Univ, Sch Informat Sci & Technol, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China 6.Shanghai Tech Univ, Sch Phys Sci & Technol, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China 7.Univ Sci & Technol China, Div Life Sci & Med, Hefei 230026, Anhui, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Runze,Jiang, Xinyu,Cao, Duanhua,et al. PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking. 2024.