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dc.contributor.authorHvidsten, Torgeir
dc.contributor.authorLægreid, Astrid
dc.contributor.authorKryshtafovych, Andriy
dc.contributor.authorAndersson, Gunnar
dc.contributor.authorFidelis, Krzysztof
dc.contributor.authorKomorowski, J
dc.date.accessioned2015-10-29T13:39:58Z
dc.date.accessioned2016-03-11T14:15:02Z
dc.date.available2015-10-29T13:39:58Z
dc.date.available2016-03-11T14:15:02Z
dc.date.issued2009
dc.identifier.citationPLoS ONE 2009, 4(7):e6266nb_NO
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/11250/2382089
dc.description.abstractBackground:Sequence similarity to characterized proteins provides testable functional hypotheses for less than 50% of the proteins identified by genome sequencing projects. With structural genomics it is believed that structural similarities may give functional hypotheses for many of the remaining proteins. Methodology/Principal Findings:We provide a systematic analysis of the structure-function relationship in proteins using the novel concept of local descriptors of protein structure. A local descriptor is a small substructure of a protein which includes both short- and long-range interactions. We employ a library of commonly reoccurring local descriptors general enough to assemble most existing protein structures. We then model the relationship between these local shapes and Gene Ontology using rule-based learning. Our IF-THEN rule model offers legible, high resolution descriptions that combine local substructures and is able to discriminate functions even for functionally versatile folds such as the frequently occurring TIM barrel and Rossmann fold. By evaluating the predictive performance of the model, we provide a comprehensive quantification of the structure-function relationship based only on local structure similarity. Our findings are, among others, that conserved structure is a stronger prerequisite for enzymatic activity than for binding specificity, and that structurebased predictions complement sequence-based predictions. The model is capable of generating correct hypotheses, as confirmed by a literature study, even when no significant sequence similarity to characterized proteins exists. Conclusions/Significance:Our approach offers a new and complete description and quantification of the structure-function relationship in proteins. By demonstrating how our predictions offer higher sensitivity than using global structure, and complement the use of sequence, we show that the presented ideas could advance the development of meta-servers in function prediction.nb_NO
dc.language.isoengnb_NO
dc.publisherPublic Library of Sciencenb_NO
dc.titleA comprehensive analysis of the structure-function relationship in proteins based on local structure similaritynb_NO
dc.typePeer reviewednb_NO
dc.date.updated2015-10-29T13:39:58Z
dc.source.volume4nb_NO
dc.source.journalPLoS ONEnb_NO
dc.source.issue7nb_NO
dc.identifier.doi10.1371/journal.pone.0006266
dc.identifier.cristin343243
dc.description.localcodeThis is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.nb_NO


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