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Paggi JM, Belk JA, Hollingsworth SA, et al. Leveraging nonstructural data to predict structures and affinities of protein-ligand complexes. Proc Natl Acad Sci U S A. 2021;118(51)doi: 10.1073/pnas.2112621118.
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. (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), . https://doi.org/10.1073/pnas.2112621118
Paggi, Joseph 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 vol. 118,51 (2021). doi: https://doi.org/10.1073/pnas.2112621118
Paggi JM, Belk JA, Hollingsworth SA, Villanueva N, Powers AS, Clark MJ, Chemparathy AG, Tynan JE, Lau TK, Sunahara RK, Dror RO. Leveraging nonstructural data to predict structures and affinities of protein-ligand complexes. Proc Natl Acad Sci U S A. 2021 Dec 21;118(51). doi: 10.1073/pnas.2112621118. PMID: 34921117.
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Proc Natl Acad Sci U S A. 2021 Dec 21;118(51). doi: 10.1073/pnas.2112621118.

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

Joseph M Paggi, Julia A Belk, Scott A Hollingsworth, Nicolas Villanueva, Alexander S Powers, Mary J Clark, Augustine G Chemparathy, Jonathan E Tynan, Thomas K Lau, Roger K Sunahara, Ron O Dror

Affiliations

  1. Department of Computer Science, Stanford University, Stanford, CA 94305.
  2. Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305.
  3. Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305.
  4. Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305.
  5. Department of Pharmacology, University of California San Diego School of Medicine, La Jolla, CA 92093.
  6. Department of Chemistry, Stanford University, Stanford, CA 94305.
  7. Department of Computer Science, Stanford University, Stanford, CA 94305; [email protected].

PMID: 34921117 DOI: 10.1073/pnas.2112621118

Abstract

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.

Copyright © 2021 the Author(s). Published by PNAS.

Keywords: antipsychotics; artificial intelligence; drug design; structural biology; virtual screening

Conflict of interest statement

Competing interest statement: Stanford University has filed a patent application related to this work.

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