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Modeling receptor flexibility in the structure-based design of KRAS(G12C) inhibitors
KRAS has long been referred to as an ‘undruggable’ target due to its high affinity for its cognate ligands (GDP and GTP) and its lack of readily exploited allosteric binding pockets. Recent progress in the development of covalent inhibitors of KRAS(G12C) has revealed that occupancy of an allosteric...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9512760/ https://www.ncbi.nlm.nih.gov/pubmed/35930206 http://dx.doi.org/10.1007/s10822-022-00467-0 |
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author | Zhu, Kai Li, Cui Wu, Kingsley Y. Mohr, Christopher Li, Xun Lanman, Brian |
author_facet | Zhu, Kai Li, Cui Wu, Kingsley Y. Mohr, Christopher Li, Xun Lanman, Brian |
author_sort | Zhu, Kai |
collection | PubMed |
description | KRAS has long been referred to as an ‘undruggable’ target due to its high affinity for its cognate ligands (GDP and GTP) and its lack of readily exploited allosteric binding pockets. Recent progress in the development of covalent inhibitors of KRAS(G12C) has revealed that occupancy of an allosteric binding site located between the α3-helix and switch-II loop of KRAS(G12C)—sometimes referred to as the ‘switch-II pocket’—holds great potential in the design of direct inhibitors of KRAS(G12C). In studying diverse switch-II pocket binders during the development of sotorasib (AMG 510), the first FDA-approved inhibitor of KRAS(G12C), we found the dramatic conformational flexibility of the switch-II pocket posing significant challenges toward the structure-based design of inhibitors. Here, we present our computational approaches for dealing with receptor flexibility in the prediction of ligand binding pose and binding affinity. For binding pose prediction, we modified the covalent docking program CovDock to allow for protein conformational mobility. This new docking approach, termed as FlexCovDock, improves success rates from 55 to 89% for binding pose prediction on a dataset of 10 cross-docking cases and has been prospectively validated across diverse ligand chemotypes. For binding affinity prediction, we found standard free energy perturbation (FEP) methods could not adequately handle the significant conformational change of the switch-II loop. We developed a new computational strategy to accelerate conformational transitions through the use of targeted protein mutations. Using this methodology, the mean unsigned error (MUE) of binding affinity prediction were reduced from 1.44 to 0.89 kcal/mol on a set of 14 compounds. These approaches were of significant use in facilitating the structure-based design of KRAS(G12C) inhibitors and are anticipated to be of further use in the design of covalent (and noncovalent) inhibitors of other conformationally labile protein targets. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10822-022-00467-0. |
format | Online Article Text |
id | pubmed-9512760 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-95127602022-09-28 Modeling receptor flexibility in the structure-based design of KRAS(G12C) inhibitors Zhu, Kai Li, Cui Wu, Kingsley Y. Mohr, Christopher Li, Xun Lanman, Brian J Comput Aided Mol Des Article KRAS has long been referred to as an ‘undruggable’ target due to its high affinity for its cognate ligands (GDP and GTP) and its lack of readily exploited allosteric binding pockets. Recent progress in the development of covalent inhibitors of KRAS(G12C) has revealed that occupancy of an allosteric binding site located between the α3-helix and switch-II loop of KRAS(G12C)—sometimes referred to as the ‘switch-II pocket’—holds great potential in the design of direct inhibitors of KRAS(G12C). In studying diverse switch-II pocket binders during the development of sotorasib (AMG 510), the first FDA-approved inhibitor of KRAS(G12C), we found the dramatic conformational flexibility of the switch-II pocket posing significant challenges toward the structure-based design of inhibitors. Here, we present our computational approaches for dealing with receptor flexibility in the prediction of ligand binding pose and binding affinity. For binding pose prediction, we modified the covalent docking program CovDock to allow for protein conformational mobility. This new docking approach, termed as FlexCovDock, improves success rates from 55 to 89% for binding pose prediction on a dataset of 10 cross-docking cases and has been prospectively validated across diverse ligand chemotypes. For binding affinity prediction, we found standard free energy perturbation (FEP) methods could not adequately handle the significant conformational change of the switch-II loop. We developed a new computational strategy to accelerate conformational transitions through the use of targeted protein mutations. Using this methodology, the mean unsigned error (MUE) of binding affinity prediction were reduced from 1.44 to 0.89 kcal/mol on a set of 14 compounds. These approaches were of significant use in facilitating the structure-based design of KRAS(G12C) inhibitors and are anticipated to be of further use in the design of covalent (and noncovalent) inhibitors of other conformationally labile protein targets. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10822-022-00467-0. Springer International Publishing 2022-08-05 2022 /pmc/articles/PMC9512760/ /pubmed/35930206 http://dx.doi.org/10.1007/s10822-022-00467-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhu, Kai Li, Cui Wu, Kingsley Y. Mohr, Christopher Li, Xun Lanman, Brian Modeling receptor flexibility in the structure-based design of KRAS(G12C) inhibitors |
title | Modeling receptor flexibility in the structure-based design of KRAS(G12C) inhibitors |
title_full | Modeling receptor flexibility in the structure-based design of KRAS(G12C) inhibitors |
title_fullStr | Modeling receptor flexibility in the structure-based design of KRAS(G12C) inhibitors |
title_full_unstemmed | Modeling receptor flexibility in the structure-based design of KRAS(G12C) inhibitors |
title_short | Modeling receptor flexibility in the structure-based design of KRAS(G12C) inhibitors |
title_sort | modeling receptor flexibility in the structure-based design of kras(g12c) inhibitors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9512760/ https://www.ncbi.nlm.nih.gov/pubmed/35930206 http://dx.doi.org/10.1007/s10822-022-00467-0 |
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