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A Random Forest Model for Predicting Allosteric and Functional Sites on Proteins
Author(s) -
Chen Ava S.Y.,
Westwood Nicholas J.,
Brear Paul,
Rogers Graeme W.,
Mavridis Lazaros,
Mitchell John B. O.
Publication year - 2016
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201500108
Subject(s) - allosteric regulation , random forest , ligand (biochemistry) , computational biology , computer science , quantitative structure–activity relationship , artificial intelligence , chemistry , machine learning , set (abstract data type) , biology , biochemistry , receptor , programming language
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).