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

Fingerprint

Major Histocompatibility Complex
Computer Simulation
Peptides
Cellular Immunity
Epitopes
Learning
Proteins

Keywords

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

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). Totowa, NJ (US): Humana Press. https://doi.org/10.1007/978-1-60327-118-9_20
Liu, Wen ; Wan, Ji ; Meng, Xiangshan ; Flower, Darren R. ; Li, Tongbin. / In silico prediction of peptide-MHC binding affinity using SVRMHC. Immunoinformatics: predicting Immunogenicity in silico. editor / Darren R. Flowers. Totowa, NJ (US) : Humana Press, 2007. pp. 283-291 (Methods in molecular biology™).
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Liu, W, Wan, J, Meng, X, Flower, DR & Li, T 2007, In silico prediction of peptide-MHC binding affinity using SVRMHC. in DR Flowers (ed.), Immunoinformatics: predicting Immunogenicity in silico. Methods in molecular biology™, vol. 409, Humana Press, Totowa, NJ (US), pp. 283-291. https://doi.org/10.1007/978-1-60327-118-9_20

In silico prediction of peptide-MHC binding affinity using SVRMHC. / Liu, Wen; Wan, Ji; Meng, Xiangshan; Flower, Darren R.; Li, Tongbin.

Immunoinformatics: predicting Immunogenicity in silico. ed. / Darren R. Flowers. Totowa, NJ (US) : Humana Press, 2007. p. 283-291 (Methods in molecular biology™; Vol. 409).

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

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AB - 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.

KW - bioinformatics

KW - cell biology

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KW - immunology

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Liu W, Wan J, Meng X, Flower DR, Li T. In silico prediction of peptide-MHC binding affinity using SVRMHC. In Flowers DR, editor, Immunoinformatics: predicting Immunogenicity in silico. Totowa, NJ (US): Humana Press. 2007. p. 283-291. (Methods in molecular biology™). https://doi.org/10.1007/978-1-60327-118-9_20