In silico prediction of peptide-MHC binding affinity using SVRMHC

Wen Liu, Ji Wan, Xiangshan Meng, Darren R. Flower, Tongbin Li

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed)

Abstract

The binding between peptide epitopes and major histocompatibility complex (MHC) proteins is a major event in the cellular immune response. Accurate prediction of the binding between short peptides and class I or class II MHC molecules is an important task in immunoinformatics. SVRMHC which is a novel method to model peptide-MHC binding affinities based on support rector machine regression (SVR) is described in this chapter. SVRMHC is among a small handful of quantitative modeling methods that make predictions about precise binding affinities between a peptide and an MHC molecule. As a kernel-based learning method, SVRMHC has rendered models with demonstrated appealing performance in the practice of modeling peptide-MHC binding.
Original languageEnglish
Title of host publicationImmunoinformatics
Subtitle of host publicationpredicting Immunogenicity in silico
EditorsDarren R. Flowers
Place of PublicationTotowa, NJ (US)
PublisherHumana Press
Pages283-291
Number of pages9
ISBN (Electronic)978-1-60327-118-9
ISBN (Print)978-1-58829-699-3
DOIs
Publication statusPublished - 16 Jul 2007

Publication series

NameMethods in molecular biology™
PublisherHumana Press
Volume409
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

Keywords

  • bioinformatics
  • cell biology
  • human genetics
  • immunology
  • life sciences

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    • Research Output

    • 5 Chapter (peer-reviewed)
    • 1 Anthology
    • 1 Foreword/postscript

    Immunoinformatics: predicting immunogenicity in silico

    Flower, D. R. (ed.), 16 Jul 2007, Totowa, NJ (US): Humana Press. 438 p. (Methods in molecular biology; vol. 409)

    Research output: Book/Report › Anthology

    • Immunoinformatics and the in silico prediction of immunogenicity: an introduction

    Flower, D. R., 16 Jul 2007, Immunoinformatics: predicting immunogenicity in silico. Flower, D. R. (ed.). Totowa, NJ (US): Humana Press, p. 1-15 15 p. (Methods in molecular biology™; vol. 409).

    Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed)

    • Molecular dynamics simulations: bring biomolecular structures alive on a computer

    Wan, S., Coveney, P. V. & Flower, D. R., 16 Jul 2007, Immunoinformatics: predicting immunogenicity in silico. Flowers, D. R. (ed.). Totowa, NJ (US): Humana Press, p. 321-339 19 p. (Methods in molecular biology™; vol. 409).

    Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed)

    • Cite this

    Liu, W., Wan, J., Meng, X., Flower, D. R., & Li, T. (2007). In silico prediction of peptide-MHC binding affinity using SVRMHC. In D. R. Flowers (Ed.), Immunoinformatics: predicting Immunogenicity in silico (pp. 283-291). (Methods in molecular biology™; Vol. 409). Humana Press. https://doi.org/10.1007/978-1-60327-118-9_20