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dc.contributor.authorGao, Jianzhao
dc.contributor.authorYang, Yuedong
dc.contributor.authorZhou, Yaoqi
dc.date.accessioned2019-06-07T01:41:08Z
dc.date.available2019-06-07T01:41:08Z
dc.date.issued2018
dc.identifier.issn1471-2105
dc.identifier.doi10.1186/s12859-018-2031-7
dc.identifier.urihttp://hdl.handle.net/10072/379888
dc.description.abstractBackground: Protein structure can be described by backbone torsion angles: rotational angles about the N-Cα bond (φ) and the Cα-C bond (ψ) or the angle between Cαi-1-Cαi-Cαi + 1 (θ) and the rotational angle about the Cαi-Cαi + 1 bond (τ). Thus, their accurate prediction is useful for structure prediction and model refinement. Early methods predicted torsion angles in a few discrete bins whereas most recent methods have focused on prediction of angles in real, continuous values. Real value prediction, however, is unable to provide the information on probabilities of predicted angles. Results: Here, we propose to predict angles in fine grids of 5° by using deep learning neural networks. We found that this grid-based technique can yield 2–6% higher accuracy in predicting angles in the same 5° bin than existing prediction techniques compared. We further demonstrate the usefulness of predicted probabilities at given angle bins in discrimination of intrinsically disorder regions and in selection of protein models. Conclusions: The proposed method may be useful for characterizing protein structure and disorder. The method is available at http://sparks-lab.org/server/SPIDER2/ as a part of SPIDER2 package.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherBioMed Central
dc.publisher.placeUnited Kingdom
dc.relation.ispartofchapter29
dc.relation.ispartofpagefrom1
dc.relation.ispartofpageto8
dc.relation.ispartofissue1
dc.relation.ispartofjournalBMC Bioinformatics
dc.relation.ispartofvolume19
dc.subject.fieldofresearchMathematical sciences
dc.subject.fieldofresearchBiological sciences
dc.subject.fieldofresearchOther biological sciences not elsewhere classified
dc.subject.fieldofresearchcode49
dc.subject.fieldofresearchcode31
dc.subject.fieldofresearchcode319999
dc.titleGrid-based prediction of torsion angle probabilities of protein backbone and its application to discrimination of protein intrinsic disorder regions and selection of model structures
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
gro.hasfulltextFull Text
gro.griffith.authorZhou, Yaoqi
gro.griffith.authorYang, Yuedong


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