Journal
JOURNAL OF THEORETICAL BIOLOGY
Volume 293, Issue -, Pages 143-150Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jtbi.2011.10.021
Keywords
Random forests; Multiple-patch model; Residue clustering
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Funding
- National Natural Science Foundation [11072048]
- National Basic Research Program of China [2009CB918501]
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Identifying protein-protein interaction sites provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Using a patch-based model for residue characterization, we trained random forest classifiers for residue-based interface prediction, which was followed by a clustering procedure to produce patches for patch-based interface prediction. For residue-based interface prediction, our method achieves a specificity rate of 0.7 and a sensitivity rate of 0.78. For patch-based interface prediction, a success rate of 0.80 is achieved. Based on same datasets, we also compare it with several published methods. The results show that our method is a successful predictor for residue-based and patch-based interface prediction. (C) 2011 Elsevier Ltd. All rights reserved.
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