Deciphering the Structural Enigma of HLA Class-II Binding Peptides for Enhanced Immunoinformatics-based Prediction of Vaccine Epitopes

Vaccines remain the most efficacious
means to avoid and eliminate morbid diseases associated with high
morbidity and mortality. Clinical trials indicate the gaining impetus
of peptide vaccines against diseases for which an effective treatment
still remains obscure. CD4 T-cell-based peptide vaccines involve immunization
with antigenic determinants from pathogens or neoplastic cells that
possess the ability to elicit a robust T helper cell response, which
subsequently activates other arms of the immune system. The available in silico predictors of human leukocyte antigen II (HLA-II)
binding peptides are sequence-based techniques, which ostensibly have
balanced sensitivity and specificity. Structural analysis and understanding
of the cognate peptide and HLA-II interactions are essential to empirically
derive a successful peptide vaccine. However, the availability of
structure-based epitope prediction algorithms is inadequate compared
with sequence-based prediction methods. The present study is an attempt
to understand the structural aspects of HLA-II binders by analyzing
the Protein Data Bank (PDB) complexes of pHLA-II. Furthermore, we
mimic the peptide exchange mechanism and demonstrate the structural
implication of an acidic environment on HLA-II binders. Finally, we
discuss a structure-guided approach to decipher potential HLA-II binders
within an antigenic protein. This strategy may accurately predict
the peptide epitopes and thus aid in designing successful peptide
vaccines.