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Int. J. Mol. Sci. 2016, 17(11), 1788; doi:10.3390/ijms17111788

Predicting Protein–Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids

1
and
1,2,*
1
Institute of Biomedical Informatics, National Yang-Ming University, Taipei 112, Taiwan
2
Office of Information Management, National Yang-Ming University Hospital, Yilan 260, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Christo Z. Christov
Received: 8 September 2016 / Revised: 14 October 2016 / Accepted: 18 October 2016 / Published: 26 October 2016
(This article belongs to the Collection Proteins and Protein-Ligand Interactions)
View Full-Text   |   Download PDF [513 KB, uploaded 26 October 2016]   |  

Abstract

Information about the interface sites of Protein–Protein Interactions (PPIs) is useful for many biological research works. However, despite the advancement of experimental techniques, the identification of PPI sites still remains as a challenging task. Using a statistical learning technique, we proposed a computational tool for predicting PPI interaction sites. As an alternative to similar approaches requiring structural information, the proposed method takes all of the input from protein sequences. In addition to typical sequence features, our method takes into consideration that interaction sites are not randomly distributed over the protein sequence. We characterized this positional preference using protein complexes with known structures, proposed a numerical index to estimate the propensity and then incorporated the index into a learning system. The resulting predictor, without using structural information, yields an area under the ROC curve (AUC) of 0.675, recall of 0.597, precision of 0.311 and accuracy of 0.583 on a ten-fold cross-validation experiment. This performance is comparable to the previous approach in which structural information was used. Upon introducing the B-factor data to our predictor, we demonstrated that the AUC can be further improved to 0.750. The tool is accessible at http://bsaltools.ym.edu.tw/predppis. View Full-Text
Keywords: Protein–Protein Interaction; intrinsically-disorder protein; machine learning algorithms Protein–Protein Interaction; intrinsically-disorder protein; machine learning algorithms
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Kuo, T.-H.; Li, K.-B. Predicting Protein–Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids. Int. J. Mol. Sci. 2016, 17, 1788.

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