Leveraging nonstructural data to predict structures and affinities of protein-ligand complexes.

Autor:	Paggi JM; Department of Computer Science, Stanford University, Stanford, CA 94305.; Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305.; Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305.; Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305., Belk JA; Department of Computer Science, Stanford University, Stanford, CA 94305.; Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305.; Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305.; Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305., Hollingsworth SA; Department of Computer Science, Stanford University, Stanford, CA 94305.; Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305.; Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305.; Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305., Villanueva N; Department of Pharmacology, University of California San Diego School of Medicine, La Jolla, CA 92093., Powers AS; Department of Computer Science, Stanford University, Stanford, CA 94305.; Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305.; Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305.; Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305.; Department of Chemistry, Stanford University, Stanford, CA 94305., Clark MJ; Department of Pharmacology, University of California San Diego School of Medicine, La Jolla, CA 92093., Chemparathy AG; Department of Computer Science, Stanford University, Stanford, CA 94305.; Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305.; Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305.; Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305., Tynan JE; Department of Computer Science, Stanford University, Stanford, CA 94305.; Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305.; Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305.; Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305., Lau TK; Department of Computer Science, Stanford University, Stanford, CA 94305.; Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305.; Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305.; Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305., Sunahara RK; Department of Pharmacology, University of California San Diego School of Medicine, La Jolla, CA 92093., Dror RO; Department of Computer Science, Stanford University, Stanford, CA 94305; ron.dror@stanford.edu.; Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305.; Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305.; Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305.
Jazyk:	angličtina
Zdroj:	Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2021 Dec 21; Vol. 118 (51).
DOI:	10.1073/pnas.2112621118
Abstrakt:	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: physics-based 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. Competing Interests: Competing interest statement: Stanford University has filed a patent application related to this work. (Copyright © 2021 the Author(s). Published by PNAS.)
Databáze:	MEDLINE
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