Check our new Scientific Reports article describing 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/
- I.S. Moreira, P.I. Koukos, R. Melo, J.G. Almeida, A.J. Preto, J. Schaarschmidt, M. Trellet, Z.H. Gümüs, J. Costa and A.M.J.J. Bonvin. SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots. Sci. Reports. 7:8007 (2017).
