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dc.contributor.authorDevaurs, Didier
Antunes, Dinler A.
Kavraki, Lydia E.
dc.date.accessioned 2019-01-08T15:37:44Z
dc.date.available 2019-01-08T15:37:44Z
dc.date.issued 2018
dc.identifier.citation Devaurs, Didier, Antunes, Dinler A. and Kavraki, Lydia E.. "Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data." International Journal of Molecular Sciences, 19, no. 11 (2018) MDPI: https://doi.org/10.3390/ijms19113406.
dc.identifier.urihttps://hdl.handle.net/1911/104979
dc.description.abstract Both 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.
dc.language.iso eng
dc.publisher MDPI
dc.rights This 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).
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.title Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data
dc.type Journal article
dc.citation.journalTitle International Journal of Molecular Sciences
dc.subject.keywordhydrogen exchange
mass spectrometry
nuclear magnetic resonance
protein conformational sampling
protein structure
dc.citation.volumeNumber 19
dc.citation.issueNumber 11
dc.identifier.digital ijms-19-03406
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.3390/ijms19113406
dc.identifier.pmcid PMC6280153
dc.identifier.pmid 30384411
dc.type.publication publisher version
dc.citation.articleNumber 3406


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