In Silico QSAR-Based Predictions of Class I and Class II MHC Epitopes - Immunoinformatics - page 61

Chapter 4
In Silico QSAR-Based Predictions of Class I and Class II
MHC Epitopes
Channa K. Hattotuwagama, Irini A. Doytchinova, Pingping Guan,
and Darren R. Flower
Edward Jenner Institute for Vaccine Research, Compton, Berkshire RG20 7NN, UK,
[e-mail]
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 speci-
ficity 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 devel-
oped extension to the Additive method for the affinity prediction of class II peptides. The
QSAR methods presented here have established themselves as immunoinformatic tech-
niques 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 computa-
tional 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.