Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/108366
DC FieldValueLanguage
dc.contributor.authorMoreira, Irina S.-
dc.contributor.authorKoukos, Panagiotis I.-
dc.contributor.authorMelo, Rita-
dc.contributor.authorAlmeida, José G.-
dc.contributor.authorPreto, Antonio J.-
dc.contributor.authorSchaarschmidt, Joerg-
dc.contributor.authorTrellet, Mikael-
dc.contributor.authorGümüş, Zeynep H-
dc.contributor.authorCosta, Joaquim-
dc.contributor.authorBonvin, Alexandre M. J. J.-
dc.date.accessioned2023-08-25T13:15:47Z-
dc.date.available2023-08-25T13:15:47Z-
dc.date.issued2017-08-14-
dc.identifier.issn2045-2322pt
dc.identifier.urihttps://hdl.handle.net/10316/108366-
dc.description.abstractWe present SpotOn, a web server to identify and classify interfacial residues as Hot-Spots (HS) and Null-Spots (NS). SpotON implements a robust algorithm with a demonstrated accuracy of 0.95 and sensitivity of 0.98 on an independent test set. The predictor was developed using an ensemble machine learning approach with up-sampling of the minor class. It was trained on 53 complexes using various features, based on both protein 3D structure and sequence. The SpotOn web interface is freely available at: http://milou.science.uu.nl/services/SPOTON/ .pt
dc.language.isoengpt
dc.relationIrina S. Moreira acknowledges support by the Fundação para a Ciência e a Tecnologia (FCT) Investigator programme - IF/00578/2014 (co-financed by European Social Fund and Programa Operacional Potencial Humano), and a Marie Skłodowska-Curie Individual Fellowship MSCA-IF-2015 [MEMBRANEPROT 659826]. This work was also financed by the European Regional Development Fund (ERDF), through the Centro 2020 Regional Operational Programme under project CENTRO-01-0145-FEDER-000008: BrainHealth 2020, and through the COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation and Portuguese national funds via FCT, under project POCI-01-0145-FEDER-007440. Rita Melo acknowledges support from the FCT (FCT—SFRH/BPD/97650/2013). Jörg Schaarschmidt acknowledges support from the European H2020 e-Infrastructure grant West-Life grant no. 675858. Mikael Trellet acknowledges support from the European H2020 e-Infrastructure grants West-Life grant no. 675858 and BioExcel grant no. 675728. Panagiotis Koukos and Alexandre Bonvin acknowledge financial support from the Dutch Foundation for Scientific Research (NWO) (TOP-PUNT grant 718.015.001). Zeynep H. Gümüş acknowledges financial support from start-up funds at Icahn School of Medicine at Mount Sinai.pt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subject.meshAnimalspt
dc.subject.meshBinding Sitespt
dc.subject.meshHumanspt
dc.subject.meshMachine Learningpt
dc.subject.meshProtein Bindingpt
dc.subject.meshProtein Interaction Mappingpt
dc.subject.meshSequence Analysis, Proteinpt
dc.subject.meshSoftwarept
dc.titleSpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spotspt
dc.typearticle-
degois.publication.firstPage8007pt
degois.publication.issue1pt
dc.peerreviewedyespt
dc.identifier.doi10.1038/s41598-017-08321-2pt
degois.publication.volume7pt
dc.date.embargo2017-08-14*
uc.date.periodoEmbargo0pt
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.openairetypearticle-
item.languageiso639-1en-
item.fulltextCom Texto completo-
item.cerifentitytypePublications-
crisitem.author.researchunitCNC - Center for Neuroscience and Cell Biology-
crisitem.author.orcid0000-0003-2970-5250-
crisitem.author.orcid0000-0003-4203-2230-
Appears in Collections:I&D CNC - Artigos em Revistas Internacionais
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