CNC - Center for Neuroscience and Cell Biology; Rua Larga, FMUC, Polo I, 1°andar, Universidade de Coimbra, 3004-517, Coimbra, Portugal.
Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, 3584CH, The Netherlands.
Sci Rep. 2017 Aug 14;7(1):8007. doi: 10.1038/s41598-017-08321-2.
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/ .
我们介绍了 SpotOn,这是一个用于识别和分类界面残基为热点(HS)和无热点(NS)的网络服务器。SpotON 实现了一种稳健的算法,在独立测试集上具有 0.95 的准确率和 0.98 的灵敏度。该预测器是使用集成机器学习方法和对小类进行上采样开发的。它使用基于蛋白质 3D 结构和序列的各种特征,在 53 个复合物上进行了训练。SpotOn 的网络界面可免费使用:http://milou.science.uu.nl/services/SPOTON/ 。