dc.contributor.author | Gao, Jianzhao | |
dc.contributor.author | Yang, Yuedong | |
dc.contributor.author | Zhou, Yaoqi | |
dc.date.accessioned | 2019-06-07T01:41:08Z | |
dc.date.available | 2019-06-07T01:41:08Z | |
dc.date.issued | 2018 | |
dc.identifier.issn | 1471-2105 | |
dc.identifier.doi | 10.1186/s12859-018-2031-7 | |
dc.identifier.uri | http://hdl.handle.net/10072/379888 | |
dc.description.abstract | Background: 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.peerreviewed | Yes | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | BioMed Central | |
dc.publisher.place | United Kingdom | |
dc.relation.ispartofchapter | 29 | |
dc.relation.ispartofpagefrom | 1 | |
dc.relation.ispartofpageto | 8 | |
dc.relation.ispartofissue | 1 | |
dc.relation.ispartofjournal | BMC Bioinformatics | |
dc.relation.ispartofvolume | 19 | |
dc.subject.fieldofresearch | Mathematical sciences | |
dc.subject.fieldofresearch | Biological sciences | |
dc.subject.fieldofresearch | Other biological sciences not elsewhere classified | |
dc.subject.fieldofresearchcode | 49 | |
dc.subject.fieldofresearchcode | 31 | |
dc.subject.fieldofresearchcode | 319999 | |
dc.title | Grid-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.type | Journal article | |
dc.type.description | C1 - Articles | |
dc.type.code | C - Journal Articles | |
dcterms.license | http://creativecommons.org/licenses/by/4.0/ | |
dc.description.version | Version 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.hasfulltext | Full Text | |
gro.griffith.author | Zhou, Yaoqi | |
gro.griffith.author | Yang, Yuedong | |