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https://hdl.handle.net/10316/108366| Title: | SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots | Authors: | Moreira, Irina S. Koukos, Panagiotis I. Melo, Rita Almeida, José G. Preto, Antonio J. Schaarschmidt, Joerg Trellet, Mikael Gümüş, Zeynep H Costa, Joaquim Bonvin, Alexandre M. J. J. |
Issue Date: | 14-Aug-2017 | Project: | Irina 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. | Volume: | 7 | Issue: | 1 | Abstract: | We 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/ . | URI: | https://hdl.handle.net/10316/108366 | ISSN: | 2045-2322 | DOI: | 10.1038/s41598-017-08321-2 | Rights: | openAccess |
| Appears in Collections: | I&D CNC - Artigos em Revistas Internacionais |
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| SpotOn-High-Accuracy-Identification-of-ProteinProtein-Interface-HotSpotsScientific-Reports.pdf | 2.62 MB | Adobe PDF | View/Open |
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