Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity: in silico bioinformatic step-by-step guide using quantitative structure-activity relationships

Channa K. Hattotuwagama, Irini A. Doytchinova, Darren R Flower

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

Abstract

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.
Original languageEnglish
Title of host publicationImmunoinformatics
Subtitle of host publicationpredicting Immunogenicity in silico
EditorsDarren R. Flowers
Place of PublicationTotowa, NJ (US)
PublisherHumana Press
Pages227-245
Number of pages19
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

  • major histocompatibility complex
  • peptides/epitopes
  • QSAR
  • additive methods
  • CoMSIA

Fingerprint Dive into the research topics of 'Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity: in silico bioinformatic step-by-step guide using quantitative structure-activity relationships'. Together they form a unique fingerprint.

  • 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/ReportEdited 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 proceedingChapter (peer-reviewed)

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

    Liu, W., Wan, J., Meng, X., Flower, D. R. & Li, T., 16 Jul 2007, Immunoinformatics: predicting Immunogenicity in silico. Flowers, D. R. (ed.). Totowa, NJ (US): Humana Press, p. 283-291 9 p. (Methods in molecular biology™; vol. 409).

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

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