Utilize este identificador para referenciar este registo:
https://hdl.handle.net/10316/108366
Título: | SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots | Autor: | 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. |
Data: | 14-Ago-2017 | Projeto: | IF/00578/2014 SFRH/BPD/97650/2013 CENTRO-01-0145-FEDER-000008: BrainHealth 2020 |
Volume: | 7 | Número: | 1 | Resumo: | 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 | Direitos: | openAccess |
Aparece nas coleções: | I&D CNC - Artigos em Revistas Internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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SpotOn-High-Accuracy-Identification-of-ProteinProtein-Interface-HotSpotsScientific-Reports.pdf | 2.62 MB | Adobe PDF | Ver/Abrir |
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