Research article

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Wen Liu¹ , Xiangshan Meng¹ , Qiqi Xu¹ , Darren R Flower² and Tongbin Li¹

¹  Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA

²  The Jenner Institute, University of Oxford, Compton, Berkshire RG20 7NN, UK

author email corresponding author email

BMC Bioinformatics 2006, 7:182doi:10.1186/1471-2105-7-182

Published:	31 March 2006

Abstract

Background

The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities.

Results

We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides.

Conclusion

As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.