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

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 mod...

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Autores principales: Paggi, Joseph M., Belk, Julia A., Hollingsworth, Scott A., Villanueva, Nicolas, Powers, Alexander S., Clark, Mary J., Chemparathy, Augustine G., Tynan, Jonathan E., Lau, Thomas K., Sunahara, Roger K., Dror, Ron O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8713799/
https://www.ncbi.nlm.nih.gov/pubmed/34921117
http://dx.doi.org/10.1073/pnas.2112621118
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author Paggi, Joseph M.
Belk, Julia A.
Hollingsworth, Scott A.
Villanueva, Nicolas
Powers, Alexander S.
Clark, Mary J.
Chemparathy, Augustine G.
Tynan, Jonathan E.
Lau, Thomas K.
Sunahara, Roger K.
Dror, Ron O.
author_facet Paggi, Joseph M.
Belk, Julia A.
Hollingsworth, Scott A.
Villanueva, Nicolas
Powers, Alexander S.
Clark, Mary J.
Chemparathy, Augustine G.
Tynan, Jonathan E.
Lau, Thomas K.
Sunahara, Roger K.
Dror, Ron O.
author_sort Paggi, Joseph M.
collection PubMed
description 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.
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spelling pubmed-87137992022-01-21 Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes Paggi, Joseph M. Belk, Julia A. Hollingsworth, Scott A. Villanueva, Nicolas Powers, Alexander S. Clark, Mary J. Chemparathy, Augustine G. Tynan, Jonathan E. Lau, Thomas K. Sunahara, Roger K. Dror, Ron O. Proc Natl Acad Sci U S A Biological Sciences 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. National Academy of Sciences 2021-12-17 2021-12-21 /pmc/articles/PMC8713799/ /pubmed/34921117 http://dx.doi.org/10.1073/pnas.2112621118 Text en Copyright © 2021 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Biological Sciences
Paggi, Joseph M.
Belk, Julia A.
Hollingsworth, Scott A.
Villanueva, Nicolas
Powers, Alexander S.
Clark, Mary J.
Chemparathy, Augustine G.
Tynan, Jonathan E.
Lau, Thomas K.
Sunahara, Roger K.
Dror, Ron O.
Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes
title Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes
title_full Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes
title_fullStr Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes
title_full_unstemmed Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes
title_short Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes
title_sort leveraging nonstructural data to predict structures and affinities of protein–ligand complexes
topic Biological Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8713799/
https://www.ncbi.nlm.nih.gov/pubmed/34921117
http://dx.doi.org/10.1073/pnas.2112621118
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