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dc.contributor.authorBedoucha, Pierre
dc.contributor.authorReuter, Nathalie
dc.contributor.authorHauser, Helwig
dc.contributor.authorByska, Jan
dc.date.accessioned2021-08-11T12:18:32Z
dc.date.available2021-08-11T12:18:32Z
dc.date.created2020-09-30T21:26:38Z
dc.date.issued2020-05-22
dc.identifier.issn0097-8493
dc.identifier.urihttps://hdl.handle.net/11250/2767387
dc.description.abstractWhen studying the function of proteins, biochemists utilize normal mode decomposition to enable the analysis of structural changes on time scales that are too long for molecular dynamics simulation. Such a decomposition yields a high-dimensional parameter space that is too large to be analyzed exhaustively. We present a novel approach to reducing and exploring this vast space through the means of interactive visualization. Our approach enables the inference of relevant protein function from single structure dynamics through protein tunnel analysis while considering normal mode combinations spanning the whole normal modes space. Our solution, based on multiple linked 2D and 3D views, enables the quick and flexible exploration of individual modes and their effect on the dynamics of tunnels with relevance for the protein function. Once an interesting motion is identified, the exploration of possible normal mode combinations is steered via a visualization-based recommendation system. This helps to quickly identify a narrow, yet relevant set of normal modes that can be investigated in detail. Our solution is the result of close cooperation between visualization and the domain. The versatility and efficiency of our approach are demonstrated in several case studies.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleVisual exploration of large normal mode spaces to study protein flexibilityen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionacceptedVersionen_US
dc.rights.holderCopyright 2020 Elsevieren_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2
dc.identifier.doi10.1016/j.cag.2020.05.025
dc.identifier.cristin1835889
dc.source.journalComputers & graphicsen_US
dc.source.pagenumber73-83en_US
dc.relation.projectNorges forskningsråd: 288008en_US
dc.relation.projectNorges forskningsråd: 251247en_US
dc.identifier.citationComputers & graphics. 2020, 90, 73-83.en_US
dc.source.volume90en_US


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal