Designing Template-Free Predictor for Targeting Protein-Ligand Binding Sites with Classifier Ensemble and Spatial Clustering

Accurately identifying the protein-ligand binding sites or pockets is of significant importance for both protein function analysis and drug design. Although much progress has been made, challenges remain, especially when the 3D structures of target proteins are not available or no homology templates...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2013-07, Vol.10 (4), p.994-1008
Main Authors: Yu, Dong-Jun, Hu, Jun, Yang, Jing, Shen, Hong-Bin, Tang, Jinhui, Yang, Jing-Yu
Format: Article
Language:English
Subjects:
Binding Sites
Bioinformatics
classifier ensemble
Cluster Analysis
Computational Biology - methods
Databases, Protein
Feature extraction
ligand-specific prediction model
Ligands
Metals
Models, Statistical
Protein Binding
Protein sequence
Protein-ligand binding sites
Proteins - chemistry
Proteins - metabolism
spatial clustering
template-free
Training
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Summary:Accurately identifying the protein-ligand binding sites or pockets is of significant importance for both protein function analysis and drug design. Although much progress has been made, challenges remain, especially when the 3D structures of target proteins are not available or no homology templates can be found in the library, where the template-based methods are hard to be applied. In this paper, we report a new ligand-specific template-free predictor called TargetS for targeting protein-ligand binding sites from primary sequences. TargetS first predicts the binding residues along the sequence with ligand-specific strategy and then further identifies the binding sites from the predicted binding residues through a recursive spatial clustering algorithm. Protein evolutionary information, predicted protein secondary structure, and ligand-specific binding propensities of residues are combined to construct discriminative features; an improved AdaBoost classifier ensemble scheme based on random undersampling is proposed to deal with the serious imbalance problem between positive (binding) and negative (nonbinding) samples. Experimental results demonstrate that TargetS achieves high performances and outperforms many existing predictors. TargetS web server and data sets are freely available at: http://www.csbio.sjtu.edu.cn/bioinf/TargetS/ for academic use.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2013.104