Static energy analysis of MHC class I and class II peptide-binding affinity

Matthew N. Davies; Darren R. Flower

doi:10.1007/978-1-60327-118-9_23

Static energy analysis of MHC class I and class II peptide-binding affinity

Matthew N. Davies, Darren R. Flower

Research output: Chapter in Book/Published conference output › Chapter (peer-reviewed)

Abstract

Antigenic peptide is presented to a T-cell receptor (TCR) through the formation of a stable complex with a major histocompatibility complex (MHC) molecule. Various predictive algorithms have been developed to estimate a peptide's capacity to form a stable complex with a given MHC class II allele, a technique integral to the strategy of vaccine design. These have previously incorporated such computational techniques as quantitative matrices and neural networks. A novel predictive technique is described, which uses molecular modeling of predetermined crystal structures to estimate the stability of an MHC class II-peptide complex. The structures are remodeled, energy minimized, and annealed before the energetic interaction is calculated.

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	309-320
Number of pages	12
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_23
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

computational biology
computer simulation
protein databases
histocompatibility antigens class I
histocompatibility antigens class II
humans
immunogenetics
major histocompatibility complex
peptides
protein binding
quantitative structure-activity relationship
software
thermodynamics
simulated annealing
energy minimization
antigenic peptides
MHC

Access to Document

10.1007/978-1-60327-118-9_23

Cite this

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title = "Static energy analysis of MHC class I and class II peptide-binding affinity",

abstract = "Antigenic peptide is presented to a T-cell receptor (TCR) through the formation of a stable complex with a major histocompatibility complex (MHC) molecule. Various predictive algorithms have been developed to estimate a peptide's capacity to form a stable complex with a given MHC class II allele, a technique integral to the strategy of vaccine design. These have previously incorporated such computational techniques as quantitative matrices and neural networks. A novel predictive technique is described, which uses molecular modeling of predetermined crystal structures to estimate the stability of an MHC class II-peptide complex. The structures are remodeled, energy minimized, and annealed before the energetic interaction is calculated.",

keywords = "computational biology, computer simulation, protein databases, histocompatibility antigens class I, histocompatibility antigens class II, humans, immunogenetics, major histocompatibility complex, peptides, protein binding, quantitative structure-activity relationship, software, thermodynamics, simulated annealing, energy minimization, antigenic peptides, MHC",

author = "Davies, {Matthew N.} and Flower, {Darren R.}",

year = "2007",

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doi = "10.1007/978-1-60327-118-9_23",

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booktitle = "Immunoinformatics",

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}

Static energy analysis of MHC class I and class II peptide-binding affinity. / Davies, Matthew N.; Flower, Darren R.
Immunoinformatics: predicting immunogenicity in silico. ed. / Darren R. Flowers. Totowa, NJ (US): Humana Press, 2007. p. 309-320 (Methods in molecular biology™; Vol. 409).

Research output: Chapter in Book/Published conference output › Chapter (peer-reviewed)

TY - CHAP

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AU - Davies, Matthew N.

AU - Flower, Darren R.

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N2 - Antigenic peptide is presented to a T-cell receptor (TCR) through the formation of a stable complex with a major histocompatibility complex (MHC) molecule. Various predictive algorithms have been developed to estimate a peptide's capacity to form a stable complex with a given MHC class II allele, a technique integral to the strategy of vaccine design. These have previously incorporated such computational techniques as quantitative matrices and neural networks. A novel predictive technique is described, which uses molecular modeling of predetermined crystal structures to estimate the stability of an MHC class II-peptide complex. The structures are remodeled, energy minimized, and annealed before the energetic interaction is calculated.

AB - Antigenic peptide is presented to a T-cell receptor (TCR) through the formation of a stable complex with a major histocompatibility complex (MHC) molecule. Various predictive algorithms have been developed to estimate a peptide's capacity to form a stable complex with a given MHC class II allele, a technique integral to the strategy of vaccine design. These have previously incorporated such computational techniques as quantitative matrices and neural networks. A novel predictive technique is described, which uses molecular modeling of predetermined crystal structures to estimate the stability of an MHC class II-peptide complex. The structures are remodeled, energy minimized, and annealed before the energetic interaction is calculated.

KW - computational biology

KW - computer simulation

KW - protein databases

KW - histocompatibility antigens class I

KW - histocompatibility antigens class II

KW - humans

KW - immunogenetics

KW - major histocompatibility complex

KW - peptides

KW - protein binding

KW - quantitative structure-activity relationship

KW - software

KW - thermodynamics

KW - simulated annealing

KW - energy minimization

KW - antigenic peptides

KW - MHC

UR - http://link.springer.com/protocol/10.1007%2F978-1-60327-118-9_23

U2 - 10.1007/978-1-60327-118-9_23

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M3 - Chapter (peer-reviewed)

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SN - 978-1-58829-699-3

T3 - Methods in molecular biology™

SP - 309

EP - 320

BT - Immunoinformatics

A2 - Flowers, Darren R.

PB - Humana Press

CY - Totowa, NJ (US)

ER -

Static energy analysis of MHC class I and class II peptide-binding affinity

Abstract

Publication series

Keywords

Access to Document

Other files and links

Fingerprint

Immunoinformatics: predicting immunogenicity in silico

Immunoinformatics and the in silico prediction of immunogenicity: an introduction

In silico prediction of peptide-MHC binding affinity using SVRMHC

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