We are pleased to announce the publication of this paper in the journal Scientific Reports.


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/.


Irina S. Moreira, Panagiotis I. Koukos, Rita Melo, Jose G. Almeida, Antonio J. Preto, Joerg Schaarschmidt, Mikael Trellet, Zeynep H. Gümüş, Joaquim Costa & Alexandre M. J. J. Bonvin “SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots” Scientific Reports volume 7, Article number: 8007(2017) doi:10.1038/s41598-017-08321-2