期刊
MOLECULAR INFORMATICS
卷 35, 期 3-4, 页码 125-135出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/minf.201500108
关键词
Random Forest; Machine Learning; Cheminformatics; Drug Design; Allosteric site
类别
资金
- Scottish Universities Life Sciences Alliance (SULSA)
We created a computational method to identify allosteric sites using a machine learning method trained and tested on protein structures containing bound ligand molecules. The Random Forest machine learning approach was adopted to build our three-way predictive model. Based on descriptors collated for each ligand and binding site, the classification model allows us to assign protein cavities as allosteric, regular or orthosteric, and hence to identify allosteric sites. 43 structural descriptors per complex were derived and were used to characterize individual protein-ligand binding sites belonging to the three classes, allosteric, regular and orthosteric. We carried out a separate validation on a further unseen set of protein structures containing the ligand 2-(N-cyclohexylamino) ethane sulfonic acid (CHES).
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