气候变化领域集成服务门户(CCPortal): Leveraging nonstructural data to predict structures and affinities of protein-ligand complexes

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DOI	10.1073/pnas.2112621118
	Leveraging nonstructural data to predict structures and affinities of protein-ligand complexes
	Paggi J.M.; Belk J.A.; Hollingsworth S.A.; Villanueva N.; Powers A.S.; Clark M.J.; Chemparathy A.G.; Tynan J.E.; Lau T.K.; Sunahara R.K.; Dror R.O.
发表日期	2021
ISSN	0027-8424
卷号	118 期号:51
英文摘要	Over the past five decades, tremendous effort has been devoted to computational methods for predicting properties of ligands-i.e., molecules that bind macromolecular targets. Such methods, which are critical to rational drug design, fall into two categories: physicsbased methods, which directly model ligand interactions with the target given the target's three-dimensional (3D) structure, and ligand-based methods, which predict ligand properties given experimental measurements for similar ligands. Here, we present a rigorous statistical framework to combine these two sources of information. We develop a method to predict a ligand's pose-the 3D structure of the ligand bound to its target-that leverages a widely available source of information: a list of other ligands that are known to bind the same target but for which no 3D structure is available. This combination of physics-based and ligand-based modeling improves pose prediction accuracy across all major families of drug targets. Using the same framework, we develop a method for virtual screening of drug candidates, which outperforms standard physics-based and ligand-based virtual screening methods. Our results suggest broad opportunities to improve prediction of various ligand properties by combining diverse sources of information through customized machine-learning approaches. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Antipsychotics; Artificial intelligence; Drug design; Structural biology; Virtual screening
语种	英语
scopus关键词	dopamine 2 receptor; ligand; neuroleptic agent; protein binding; artificial intelligence; binding site; chemical structure; chemistry; drug design; drug effect; gene expression regulation; metabolism; molecular docking; procedures; protein conformation; structure activity relation; Antipsychotic Agents; Artificial Intelligence; Binding Sites; Drug Design; Gene Expression Regulation; Ligands; Molecular Docking Simulation; Molecular Structure; Protein Binding; Protein Conformation; Receptors, Dopamine D2; Structure-Activity Relationship
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/250914
作者单位	Department of Computer Science, Stanford University, Stanford, CA 94305, United States; Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, United States; Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, United States; Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, United States; Department of Pharmacology, University of California San Diego School of Medicine, La JollaCA 92093, United States; Department of Chemistry, Stanford University, Stanford, CA 94305, United States
推荐引用方式 GB/T 7714	Paggi J.M.,Belk J.A.,Hollingsworth S.A.,et al. Leveraging nonstructural data to predict structures and affinities of protein-ligand complexes[J],2021,118(51).
APA	Paggi J.M..,Belk J.A..,Hollingsworth S.A..,Villanueva N..,Powers A.S..,...&Dror R.O..(2021).Leveraging nonstructural data to predict structures and affinities of protein-ligand complexes.Proceedings of the National Academy of Sciences of the United States of America,118(51).
MLA	Paggi J.M.,et al."Leveraging nonstructural data to predict structures and affinities of protein-ligand complexes".Proceedings of the National Academy of Sciences of the United States of America 118.51(2021).