In silico prediction of peptide-MHC binding affinity using SVRMHC

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

doi:10.1007/978-1-60327-118-9_20

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 language	English
Title of host publication	Immunoinformatics
Subtitle of host publication	predicting Immunogenicity in silico
Editors	Darren R. Flowers
Place of Publication	Totowa, NJ (US)
Publisher	Humana Press
Pages	283-291
Number of pages	9
ISBN (Electronic)	978-1-60327-118-9
ISBN (Print)	978-1-58829-699-3
DOIs	https://doi.org/10.1007/978-1-60327-118-9_20
Publication status	Published - 16 Jul 2007

Publication series

Name	Methods in molecular biology™
Publisher	Humana Press
Volume	409
ISSN (Print)	1064-3745
ISSN (Electronic)	1940-6029

Keywords

bioinformatics
cell biology
human genetics
immunology
life sciences

Access to Document

10.1007/978-1-60327-118-9_20

http://link.springer.com/protocol/10.1007/978-1-60327-118-9_20

Fingerprint Dive into the research topics of 'In silico prediction of peptide-MHC binding affinity using SVRMHC'. Together they form a unique fingerprint.

5 Chapter (peer-reviewed)
1 Edited Book
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 › Edited Book
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

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title = "In silico prediction of peptide-MHC binding affinity using SVRMHC",

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.",

keywords = "bioinformatics , cell biology , human genetics , immunology , life sciences",

author = "Wen Liu and Ji Wan and Xiangshan Meng and Flower, {Darren R.} and Tongbin Li",

year = "2007",

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doi = "10.1007/978-1-60327-118-9_20",

language = "English",

isbn = "978-1-58829-699-3",

series = "Methods in molecular biology{\texttrademark}",

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pages = "283--291",

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booktitle = "Immunoinformatics",

address = "United States",

}

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 proceeding › Chapter (peer-reviewed)

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T1 - In silico prediction of peptide-MHC binding affinity using SVRMHC

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AU - Li, Tongbin

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N2 - 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.

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

KW - human genetics

KW - immunology

KW - life sciences

UR - http://link.springer.com/protocol/10.1007/978-1-60327-118-9_20

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DO - 10.1007/978-1-60327-118-9_20

M3 - Chapter (peer-reviewed)

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T3 - Methods in molecular biology™

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A2 - Flowers, Darren R.

PB - Humana Press

CY - Totowa, NJ (US)

ER -

In silico prediction of peptide-MHC binding affinity using SVRMHC

Abstract

Publication series

Keywords

Access to Document

Immunoinformatics: predicting immunogenicity in silico

Immunoinformatics and the in silico prediction of immunogenicity: an introduction

Molecular dynamics simulations: bring biomolecular structures alive on a computer

Cite this