SpotOn: determination of Hot-Spots at protein-protein interfaces

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SpotOn is a robust algorithm developed to identify and classify the interfacial residues as Hot-Spots (HS) and Null-Spots (NS) with a final accuracy of 0.95 and a sensitivity of 0.95 on an independent test set.

The predictor was developed using an ensemble learning algorithm with up-sampling of the minor class and was trained on a large number of complexes and on a high number of different structural- and evolutionary sequence-based features.

SpotOn webserver

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• SPOTON server

Supported by

This work is co-funded by the Horizon 2020 projects EOSC-hub (grant number 777536) and BioExcel (grant numbers 823830 and 675728).

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no [MEMBRANEPROT 659826].

Reference for use of the server

When using the SpotOn server please cite:

Melo et al (2016)
A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces.
Int. J. Mol. Sci. 17, 1215.

Moreira and Koukos et al. (2017)
SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots.
Scientific Reports 7:8007.