SVR-PAIRWISE method to predict MHC-II binding peptides

  • Authors:

  • Juan Liu;Lian Wang;Shanfeng Zhu

  • Affiliations:

  • School of Computer, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.;School of Computer, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.;Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, 220 Handan Road, Shanghai 200433, China

  • Venue:
  • International Journal of Bioinformatics Research and Applications
  • Year:
  • 2010

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Abstract

Peptides binding to MHC molecules is of great importance in the process of triggering and initiating immune responses. There are two main kinds of MHC molecules, MHC class I and class II, and the prediction of MHC-II binding peptides is much more difficult due to their variable lengths, which makes it difficult to construct a preferable prediction model by using most of the existing methods. This paper presents a method, called as SVR-PAIRWISE, to combine Support Vector Regression (SVR) and pairwise alignment, to quantitatively predict the MHC-II binding peptides. The comparison results with some popular methods show its satisfying performances.