An automated framework for understanding structural variations in the binding grooves of MHC class II molecules
Artikel i vetenskaplig tidskrift, 2010

BACKGROUND: MHC/HLA class II molecules are important components of the immune system and play a critical role in processes such as phagocytosis. Understanding peptide recognition properties of the hundreds of MHC class II alleles is essential to appreciate determinants of antigenicity and ultimately to predict epitopes. While there are several methods for epitope prediction, each differing in their success rates, there are no reports so far in the literature to systematically characterize the binding sites at the structural level and infer recognition profiles from them. RESULTS: Here we report a new approach to compare the binding sites of MHC class II molecules using their three dimensional structures. We use a specifically tuned version of our recent algorithm, PocketMatch. We show that our methodology is useful for classification of MHC class II molecules based on similarities or differences among their binding sites. A new module has been used to define binding sites in MHC molecules. Comparison of binding sites of 103 MHC molecules, both at the whole groove and individual sub-pocket levels has been carried out, and their clustering patterns analyzed. While clusters largely agree with serotypic classification, deviations from it and several new insights are obtained from our study. We also present how differences in sub-pockets of molecules associated with a pair of autoimmune diseases, narcolepsy and rheumatoid arthritis, were captured by PocketMatch13. CONCLUSION: The systematic framework for understanding structural variations in MHC class II molecules enables large scale comparison of binding grooves and sub-pockets, which is likely to have direct implications towards predicting epitopes and understanding peptide binding preferences.


Kalidas Yeturu

Indian Institute of Science

Tapani Utriainen

Chalmers, Data- och informationsteknik, Datavetenskap

Graham Kemp

Chalmers, Data- och informationsteknik, Datavetenskap

Nagasuma Chandra

Indian Institute of Science

BMC Bioinformatics

14712105 (eISSN)

Vol. 11 Suppl 1 S55- S55



Bioinformatik och systembiologi



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