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dc.contributor.authorSharma, Alok
dc.contributor.authorLysenko, Artem
dc.contributor.authorLopez, Yosvany
dc.contributor.authorDehzangi, Abdollah
dc.contributor.authorSharma, Ronesh
dc.contributor.authorReddy, Hamendra
dc.contributor.authorSattar, Abdul
dc.contributor.authorTsunoda, Tatsuhiko
dc.date.accessioned2019-07-04T12:37:57Z
dc.date.available2019-07-04T12:37:57Z
dc.date.issued2019
dc.identifier.issn1471-2164
dc.identifier.doi10.1186/s12864-018-5206-8
dc.identifier.urihttp://hdl.handle.net/10072/385917
dc.description.abstractBackground: Post-translational modifications are viewed as an important mechanism for controlling protein function and are believed to be involved in multiple important diseases. However, their profiling using laboratory-based techniques remain challenging. Therefore, making the development of accurate computational methods to predict post-translational modifications is particularly important for making progress in this area of research. Results: This work explores the use of four half-sphere exposure-based features for computational prediction of sumoylation sites. Unlike most of the previously proposed approaches, which focused on patterns of amino acid co-occurrence, we were able to demonstrate that protein structural based features could be sufficiently informative to achieve good predictive performance. The evaluation of our method has demonstrated high sensitivity (0.9), accuracy (0.89) and Matthew’s correlation coefficient (0.78–0.79). We have compared these results to the recently released pSumo-CD method and were able to demonstrate better performance of our method on the same evaluation dataset. Conclusions: The proposed predictor HseSUMO uses half-sphere exposures of amino acids to predict sumoylation sites. It has shown promising results on a benchmark dataset when compared with the state-of-the-art method.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherBMC
dc.relation.ispartofconferencename17th Annual International Conference on Bioinformatics (InCoB) - Genomics
dc.relation.ispartofdatefrom2018-09-26
dc.relation.ispartofdateto2018-09-28
dc.relation.ispartoflocationNew Delhi, INDIA
dc.relation.ispartofissue9
dc.relation.ispartofjournalBMC GENOMICS
dc.relation.ispartofvolume19
dc.subject.fieldofresearchMedical and Health Sciences
dc.subject.fieldofresearchBiological Sciences
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchcode11
dc.subject.fieldofresearchcode06
dc.subject.fieldofresearchcode08
dc.titleHseSUMO: Sumoylation site prediction using half-sphere exposures of amino acids residues
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). 2019 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.authorSharma, Alok
gro.griffith.authorSattar, Abdul


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