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In Silico QSAR-Based Predictions of Class I and Class II MHC Epitopes

  • Channa K. Hattotuwagama
  • Irini A. Doytchinova
  • Pingping Guan
  • Darren R. Flower

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 were built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review two methods – a 2D-QSAR Additive-Partial Least Squares (PLS) and a 3D-QSAR Comparative Molecular Similarity Index Analysis (CoMISA) 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 Iterative Self-Consistent (ISC) PLS-based Additive Method 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 review a variety of 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-Kk, H2-Kb, and H2-Db HLA-DRB1*0101, HLA-DRB1*0401, and HLA-DRB1*0701, I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek.

In terms of reliability the resulting models 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, are freely available online at: http://www.jenner.ac.uk/ MHCPred.

Keywords

Partial Little Square Peptide Binding Anchor Residue Anchor Position Amino Acid Preference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC. 2008

Authors and Affiliations

  • Channa K. Hattotuwagama
    • 1
  • Irini A. Doytchinova
    • 1
  • Pingping Guan
    • 1
  • Darren R. Flower
    • 1
  1. 1.Edward Jenner Institute for Vaccine ResearchComptonUK

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