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 proceedingChapter (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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