Improved Prediction of Protein Ligand-Binding Sites Using Random Forests

Author(s): Zhijun Qiu, Xicheng Wang.

Journal Name: Protein & Peptide Letters

Volume 18 , Issue 12 , 2011

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Abstract:

This article describes a novel method for predicting ligand-binding sites of proteins. This method uses only 8 structural properties as input vector to train 9 random forest classifiers which are combined to predict binding residues. These predicted binding residues are then clustered into some predicted ligand-binding sites. According to our measurement criterion, this method achieved a success rate of 0.914 in the bound state dataset and 0.800 in the unbound state dataset, which are better than three other methods: Q-SiteFinder, SCREEN and Moritas method. It indicates that the proposed method here is successful for predicting ligand-binding sites.

Keywords: ligand-binding site prediction, patch-based residue characterization, random forests, Q-SiteFinder, SCREEN, Morita's method, X-ray crystallography, NMR, hydrophobicity, pocket depth, jackknife test, ASA, Feature vector for residue, PSAIA, Solvation energyligand-binding site prediction, patch-based residue characterization, random forests, Q-SiteFinder, SCREEN, Morita's method, X-ray crystallography, NMR, hydrophobicity, pocket depth, jackknife test, ASA, Feature vector for residue, PSAIA, Solvation energy

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Article Details

VOLUME: 18
ISSUE: 12
Year: 2011
Page: [1212 - 1218]
Pages: 7
DOI: 10.2174/092986611797642788
Price: $65

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