8dnj
From Proteopedia
(Difference between revisions)
| Line 5: | Line 5: | ||
<table><tr><td colspan='2'>[[8dnj]] is a 3 chain structure with sequence from [https://en.wikipedia.org/wiki/Homo_sapiens Homo sapiens]. Full crystallographic information is available from [http://oca.weizmann.ac.il/oca-bin/ocashort?id=8DNJ OCA]. For a <b>guided tour on the structure components</b> use [https://proteopedia.org/fgij/fg.htm?mol=8DNJ FirstGlance]. <br> | <table><tr><td colspan='2'>[[8dnj]] is a 3 chain structure with sequence from [https://en.wikipedia.org/wiki/Homo_sapiens Homo sapiens]. Full crystallographic information is available from [http://oca.weizmann.ac.il/oca-bin/ocashort?id=8DNJ OCA]. For a <b>guided tour on the structure components</b> use [https://proteopedia.org/fgij/fg.htm?mol=8DNJ FirstGlance]. <br> | ||
</td></tr><tr id='method'><td class="sblockLbl"><b>[[Empirical_models|Method:]]</b></td><td class="sblockDat" id="methodDat">X-ray diffraction, [[Resolution|Resolution]] 1.81Å</td></tr> | </td></tr><tr id='method'><td class="sblockLbl"><b>[[Empirical_models|Method:]]</b></td><td class="sblockDat" id="methodDat">X-ray diffraction, [[Resolution|Resolution]] 1.81Å</td></tr> | ||
| - | <tr id='ligand'><td class="sblockLbl"><b>[[Ligand|Ligands:]]</b></td><td class="sblockDat" id="ligandDat"><scene name='pdbligand=GDP:GUANOSINE-5-DIPHOSPHATE'>GDP</scene>, <scene name='pdbligand=MG:MAGNESIUM+ION'>MG</scene>, <scene name='pdbligand=U4U:1-[( | + | <tr id='ligand'><td class="sblockLbl"><b>[[Ligand|Ligands:]]</b></td><td class="sblockDat" id="ligandDat"><scene name='pdbligand=GDP:GUANOSINE-5-DIPHOSPHATE'>GDP</scene>, <scene name='pdbligand=MG:MAGNESIUM+ION'>MG</scene>, <scene name='pdbligand=U4U:1-[(4~{a}~{R})-8-(2-chloranyl-6-oxidanyl-phenyl)-9-ethynyl-7-fluoranyl-1,2,4,4~{a},5,11-hexahydropyrazino[2,1-c][1,4]benzoxazepin-3-yl]propan-1-one'>U4U</scene></td></tr> |
<tr id='resources'><td class="sblockLbl"><b>Resources:</b></td><td class="sblockDat"><span class='plainlinks'>[https://proteopedia.org/fgij/fg.htm?mol=8dnj FirstGlance], [http://oca.weizmann.ac.il/oca-bin/ocaids?id=8dnj OCA], [https://pdbe.org/8dnj PDBe], [https://www.rcsb.org/pdb/explore.do?structureId=8dnj RCSB], [https://www.ebi.ac.uk/pdbsum/8dnj PDBsum], [https://prosat.h-its.org/prosat/prosatexe?pdbcode=8dnj ProSAT]</span></td></tr> | <tr id='resources'><td class="sblockLbl"><b>Resources:</b></td><td class="sblockDat"><span class='plainlinks'>[https://proteopedia.org/fgij/fg.htm?mol=8dnj FirstGlance], [http://oca.weizmann.ac.il/oca-bin/ocaids?id=8dnj OCA], [https://pdbe.org/8dnj PDBe], [https://www.rcsb.org/pdb/explore.do?structureId=8dnj RCSB], [https://www.ebi.ac.uk/pdbsum/8dnj PDBsum], [https://prosat.h-its.org/prosat/prosatexe?pdbcode=8dnj ProSAT]</span></td></tr> | ||
</table> | </table> | ||
| Line 16: | Line 16: | ||
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 alpha3-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. | 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 alpha3-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. | ||
| - | Modeling receptor flexibility in the structure-based design of KRAS(G12C) inhibitors.,Zhu K, Li C, Wu KY, Mohr C, Li X, Lanman B J Comput Aided Mol Des. 2022 Aug | + | Modeling receptor flexibility in the structure-based design of KRAS(G12C) inhibitors.,Zhu K, Li C, Wu KY, Mohr C, Li X, Lanman B J Comput Aided Mol Des. 2022 Aug;36(8):591-604. doi: 10.1007/s10822-022-00467-0. , Epub 2022 Aug 5. PMID:35930206<ref>PMID:35930206</ref> |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.<br> | From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.<br> | ||
Current revision
Crystal structure of human KRAS G12C covalently bound with AstraZeneca WO2020/178282A1 compound 76
| |||||||||||
