Tyrosine Kinase Ligand-Receptor Pair Prediction by Using Support Vector Machine

Advances in Bioinformatics
Masayuki YarimizuKentaro Shimizu

Abstract

Receptor tyrosine kinases are essential proteins involved in cellular differentiation and proliferation in vivo and are heavily involved in allergic diseases, diabetes, and onset/proliferation of cancerous cells. Identifying the interacting partner of this protein, a growth factor ligand, will provide a deeper understanding of cellular proliferation/differentiation and other cell processes. In this study, we developed a method for predicting tyrosine kinase ligand-receptor pairs from their amino acid sequences. We collected tyrosine kinase ligand-receptor pairs from the Database of Interacting Proteins (DIP) and UniProtKB, filtered them by removing sequence redundancy, and used them as a dataset for machine learning and assessment of predictive performance. Our prediction method is based on support vector machines (SVMs), and we evaluated several input features suitable for tyrosine kinase for machine learning and compared and analyzed the results. Using sequence pattern information and domain information extracted from sequences as input features, we obtained 0.996 of the area under the receiver operating characteristic curve. This accuracy is higher than that obtained from general protein-protein interaction pair predictions.

References

Apr 20, 1990·Cell·A Ullrich, J Schlessinger
Oct 5, 1990·Journal of Molecular Biology·S F AltschulD J Lipman
Dec 19, 2003·Nucleic Acids Research·Lukasz SalwinskiDavid Eisenberg
Mar 20, 2004·Science·Martin E M NobleLouise N Johnson
Jul 7, 2010·Cell·Mark A Lemmon, Joseph Schlessinger
Aug 13, 2011·BMC Bioinformatics·Ernesto IacucciYves Moreau
Dec 1, 2012·Nucleic Acids Research·Aron Marchler-BauerStephen H Bryant
Nov 21, 2013·Nucleic Acids Research·UNKNOWN UniProt Consortium

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Methods Mentioned

BETA
feature extraction
interaction prediction
feature extractions

Software Mentioned

BLASTclust
LIBSVM
Struct2Net

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