Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data

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dc.citation.articleNumber3406en_US
dc.citation.issueNumber11en_US
dc.citation.journalTitleInternational Journal of Molecular Sciencesen_US
dc.citation.volumeNumber19en_US
dc.contributor.authorDevaurs, Didieren_US
dc.contributor.authorAntunes, Dinler A.en_US
dc.contributor.authorKavraki, Lydia E.en_US
dc.date.accessioned2019-01-08T15:37:44Zen_US
dc.date.available2019-01-08T15:37:44Zen_US
dc.date.issued2018en_US
dc.description.abstractBoth experimental and computational methods are available to gather information about a protein's conformational space and interpret changes in protein structure. However, experimentally observing and computationally modeling large proteins remain critical challenges for structural biology. Our work aims at addressing these challenges by combining computational and experimental techniques relying on each other to overcome their respective limitations. Indeed, despite its advantages, an experimental technique such as hydrogen-exchange monitoring cannot produce structural models because of its low resolution. Additionally, the computational methods that can generate such models suffer from the curse of dimensionality when applied to large proteins. Adopting a common solution to this issue, we have recently proposed a framework in which our computational method for protein conformational sampling is biased by experimental hydrogen-exchange data. In this paper, we present our latest application of this computational framework: generating an atomic-resolution structural model for an unknown protein state. For that, starting from an available protein structure, we explore the conformational space of this protein, using hydrogen-exchange data on this unknown state as a guide. We have successfully used our computational framework to generate models for three proteins of increasing size, the biggest one undergoing large-scale conformational changes.en_US
dc.identifier.citationDevaurs, Didier, Antunes, Dinler A. and Kavraki, Lydia E.. "Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data." <i>International Journal of Molecular Sciences,</i> 19, no. 11 (2018) MDPI: https://doi.org/10.3390/ijms19113406.en_US
dc.identifier.digitalijms-19-03406en_US
dc.identifier.doihttps://doi.org/10.3390/ijms19113406en_US
dc.identifier.urihttps://hdl.handle.net/1911/104979en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.subject.keywordhydrogen exchangeen_US
dc.subject.keywordmass spectrometryen_US
dc.subject.keywordnuclear magnetic resonanceen_US
dc.subject.keywordprotein conformational samplingen_US
dc.subject.keywordprotein structureen_US
dc.titleRevealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Dataen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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