title: Predicting Protein-Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids.
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.
authors: Institute of Biomedical Informatics, National Yang-Ming University, Taipei 112, Taiwan. kzvito@gmail.com.
authors: Tzu-Hao
authors: TH
authors: Kuo
authors: Institute of Biomedical Informatics, National Yang-Ming University, Taipei 112, Taiwan. kbli@ym.edu.tw.|Office of Information Management, National Yang-Ming University Hospital, Yilan 260, Taiwan. kbli@ym.edu.tw.
authors: Kuo-Bin
authors: KB
authors: Li
mesh_terms: D000596:Amino Acids / Q000737:chemistry / Q000378:metabolism; D000818:Animals; D030562:Databases, Protein; D006801:Humans; D064267:Intrinsically Disordered Proteins / Q000737:chemistry / Q000378:metabolism; D000069550:Machine Learning; D008954:Models, Biological; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000379:methods*; D060066:Protein Interaction Maps; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*
chemical_list: D000596:Amino Acids; D064267:Intrinsically Disordered Proteins; D011506:Proteins
keywords: Protein–Protein Interaction; intrinsically-disorder protein; machine learning algorithms