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

Wen Liu,Tongbin Li,Xiangshan Meng,Qiqi Xu,Darren R Flower

doi:10.1186/1471-2105-7-182

Wen Liu, Tongbin Li + Show 3 more

Open Access

https://doi.org/10.1186/1471-2105-7-182

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Abstract

BackgroundThe 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.ResultsWe 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.ConclusionAs 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.

Highlights

The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response
In most published support vector machine regression (SVR) studies we have examined, these model parameters were determined one at a time, by first fixing all other parameters, letting the parameter take a range of different values, and identifying the value that corresponds to the best model performance assessed by cross-validation [20,21]
FuFAosirgescduHhr2ee-mK1akti(c1d5i4agpreapmtidoefst)h,ewfiitvhe-efnocldlocsrinogssp-avraalimdaettioern ssecahrecmhiengfomr othdeultersaininwghaicnhdlteeasvtein-ognoef-tohuet S(LVORMOH) cCromsso-dvealidcoatniostnruwcatesd A schematic diagram of the five-fold cross-validation scheme for the training and testing of the SVRMHC model constructed for H2-Kk (154 peptides), with enclosing parameter searching modules in which leave-one-out (LOO) cross-validation was used

Summary

Introduction

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. The T cell surface is enriched in a particular receptor protein: the T cell receptor or TCR, which binds to major histocompatibility complex proteins (MHCs) expressed on the surfaces of other cells. MHCs bind small peptide fragments derived from both host and pathogen proteins. It is the recognition of such complexes that lies at the heart of the cellular immune response. These short peptides are known as epitopes. The significance of non-peptide epitopes, such as lipids and carbohydrates, is understood increasingly well, peptidic B cell and T cell (page number not for citation purposes)

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