000013412 001__ 13412 000013412 005__ 20250218124812.0 000013412 02470 $$ahttps://doi.org/10.1021/acs.jctc.1c00960$$2doi 000013412 037__ $$aTEXTUAL 000013412 037__ $$bArticle 000013412 041__ $$aeng 000013412 245__ $$aPrediction and Validation of a Protein’s Free Energy Surface Using Hydrogen Exchange and (Importantly) Its Denaturant Dependence 000013412 269__ $$a2021-12-22 000013412 336__ $$aArticle 000013412 520__ $$aThe denaturant dependence of hydrogen–deuterium exchange (HDX) is a powerful measurement to identify the breaking of individual H-bonds and map the free energy surface (FES) of a protein including the very rare states. Molecular dynamics (MD) can identify each partial unfolding event with atomic-level resolution. Hence, their combination provides a great opportunity to test the accuracy of simulations and to verify the interpretation of HDX data. For this comparison, we use Upside, our new and extremely fast MD package that is capable of folding proteins with an accuracy comparable to that of all-atom methods. The FESs of two naturally occurring and two designed proteins are so generated and compared to our NMR/HDX data. We find that Upside’s accuracy is considerably improved upon modifying the energy function using a new machine-learning procedure that trains for proper protein behavior including realistic denatured states in addition to stable native states. The resulting increase in cooperativity is critical for replicating the HDX data and protein stability, indicating that we have properly encoded the underlying physiochemical interactions into an MD package. We did observe some mismatch, however, underscoring the ongoing challenges faced by simulations in calculating accurate FESs. Nevertheless, our ensembles can identify the properties of the fluctuations that lead to HDX, whether they be small-, medium-, or large-scale openings, and can speak to the breadth of the native ensemble that has been a matter of debate. 000013412 536__ $$oNational Institute of General Medical Sciences$$cGM55694 000013412 536__ $$oNational Institute of General Medical Sciences$$cR01 GM130122 000013412 536__ $$oNational Science Foundation$$cMCB-1517221 000013412 536__ $$oNational Science Foundation$$cMCB-2023077 000013412 536__ $$oBayer AG 000013412 536__ $$oBoehringer Ingelheim 000013412 536__ $$oBristol Myers Squibb 000013412 536__ $$oGenentech 000013412 536__ $$oOntario Genomics Institute 000013412 536__ $$oEUbOPEN$$c875510 000013412 536__ $$oJanssen 000013412 536__ $$oMerck KGaA 000013412 536__ $$oPfizer 000013412 536__ $$oTakeda 000013412 540__ $$a<p>© 2021 The Authors.</p> <p>This publication is licensed under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a>.</p> 000013412 542__ $$fCC BY 000013412 690__ $$aBiological Sciences Division 000013412 690__ $$aPhysical Sciences Division 000013412 690__ $$aThe College 000013412 691__ $$aBiochemistry and Molecular Biology 000013412 691__ $$aBiological Sciences 000013412 691__ $$aBiophysical Sciences 000013412 691__ $$aChemistry 000013412 7001_ $$aPeng, Xiangda$$uUniversity of Chicago 000013412 7001_ $$aBaxa, Michael$$uUniversity of Chicago 000013412 7001_ $$aFaruk, Nabil$$uUniversity of Chicago 000013412 7001_ $$aSachleben, Joseph R.$$uUniversity of Chicago 000013412 7001_ $$1https://orcid.org/0000-0001-6356-2607$$2ORCID$$aPintscher, Sebastian$$uUniversity of Chicago 000013412 7001_ $$aGagnon, Isabelle A.$$uUniversity of Chicago 000013412 7001_ $$aHouliston, Scott$$uUniversity of Toronto 000013412 7001_ $$1https://orcid.org/0000-0002-4971-3250$$2ORCID$$aArrowsmith, Cheryl H.$$uUniversity of Toronto 000013412 7001_ $$1https://orcid.org/0000-0001-6240-2599$$2ORCID$$aFreed, Karl F.$$uUniversity of Chicago 000013412 7001_ $$1https://orcid.org/0000-0003-2253-5631$$2ORCID$$aRocklin, Gabriel J.$$uNorthwestern University 000013412 7001_ $$1https://orcid.org/0000-0002-2871-7244$$2ORCID$$aSosnick, Tobin R.$$uUniversity of Chicago 000013412 773__ $$tJournal of Chemical Theory and Computation 000013412 8564_ $$yArticle$$9a9ce6aa6-01ce-4753-929e-f8d0dd47b166$$s10959534$$uhttps://knowledge.uchicago.edu/record/13412/files/peng-et-al-2021-prediction-and-validation-of-a-protein-s-free-energy-surface-using-hydrogen-exchange-and-%28importantly%29.pdf$$ePublic 000013412 8564_ $$ySupporting information$$9e902f558-6502-4ccf-8d5e-381d271c5bd1$$s4151318$$uhttps://knowledge.uchicago.edu/record/13412/files/ct1c00960_si_001.pdf$$ePublic 000013412 908__ $$aI agree 000013412 909CO $$ooai:uchicago.tind.io:13412$$pGLOBAL_SET 000013412 983__ $$aArticle