SpotOn: high accuracy identification of protein-protein interface hot-spots
Scientific reports, 2017•nature.com
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 …
(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 …
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/.
nature.com
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