SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots
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 ma...
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| Main Authors: | , , , , , , , , , |
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| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2017
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| Subjects: | |
| Online Access: | https://doaj.org/article/bcfd8d40f8554bfebb790c4ceb05f28f |
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| Summary: | 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/ . |
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