Prediction of protein accessible surface areas by support vector regression

Proteins
Zheng Yuan, Bixing Huang

Abstract

A novel support vector regression (SVR) approach is proposed to predict protein accessible surface areas (ASAs) from their primary structures. In this work, we predict the real values of ASA in squared angstroms for residues instead of relative solvent accessibility. Based on protein residues, the mean and median absolute errors are 26.0 A(2) and 18.87 A(2), respectively. The correlation coefficient between the predicted and observed ASAs is 0.66. Cysteine is the best predicted amino acid (mean absolute error is 13.8 A(2) and median absolute error is 8.37 A(2)), while arginine is the least predicted amino acid (mean absolute error is 42.7 A(2) and median absolute error is 36.31 A(2)). Our work suggests that the SVR approach can be directly applied to the ASA prediction where data preclassification has been used.

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Citations

May 10, 2008·Bioinformatics·Jiangning SongTatsuya Akutsu
Oct 4, 2006·BMC Bioinformatics·Jiangning Song, Kevin Burrage
Aug 22, 2007·BMC Bioinformatics·Yungki ParkVolkhard Helms
Jan 6, 2009·BMC Bioinformatics·Darby Tien-Hao ChangChih-Peng Wu
Aug 4, 2009·BMC Structural Biology·Bent PetersenClaus Lundegaard
Dec 5, 2006·FEBS Letters·Yanhong LiLing Yang
Jan 13, 2005·Proteins·Zheng YuanRohan D Teasdale
Aug 21, 2007·Proteins·Zheng Yuan, Zhi-Xin Wang
Jul 25, 2006·Biophysical Chemistry·Y Hemajit SinghShandar Ahmad
Jun 19, 2013·Biochimie·Lichao ZhangLiang Kong
Jun 17, 2015·Journal of Biomolecular NMR·Noor E HafsaDavid S Wishart
Jun 21, 2015·Journal of Theoretical Biology·Sumaiya IqbalMd Tamjidul Hoque

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