Please use this identifier to cite or link to this item: 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: IF/00578/2014 
SFRH/BPD/97650/2013 
CENTRO-01-0145-FEDER-000008: BrainHealth 2020 
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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