MHC class II binding prediction:a little help from a friend


Vaccines are the greatest single instrument of prophylaxis against infectious diseases, with immeasurable benefits to human wellbeing. The accurate and reliable prediction of peptide-MHC binding is fundamental to the robust identification of T-cell epitopes and thus the successful design of peptide- and protein-based vaccines. The prediction of MHC class II peptide binding has hitherto proved recalcitrant and refractory. Here we illustrate the utility of existing computational tools for in silico prediction of peptides binding to class II MHCs. Most of the methods, tested in the present study, detect more than the half of the true binders in the top 5% of all possible nonamers generated from one protein. This number increases in the top 10% and 15% and then does not change significantly. For the top 15% the identified binders approach 86%. In terms of lab work this means 85% less expenditure on materials, labour and time. We show that while existing caveats are well founded, nonetheless use of computational models of class II binding can still offer viable help to the work of the immunologist and vaccinologist.

Publication DOI:
Divisions: College of Health & Life Sciences > Aston Pharmacy School
College of Health & Life Sciences
College of Health & Life Sciences > Chronic and Communicable Conditions
Additional Information: Copyright © 2010 Ivan Dimitrov et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords: Biotechnology,Genetics,Molecular Biology,Molecular Medicine,Health, Toxicology and Mutagenesis
Publication ISSN: 1110-7251
Last Modified: 23 Jan 2024 17:46
Date Deposited: 21 Mar 2012 10:21
Full Text Link: http://www.hind ... bb/2010/705821/
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2010
Authors: Dimitrov, Ivan
Garnev, Panayot
Flower, Darren R (ORCID Profile 0000-0002-8542-7067)
Doytchinova, Irini



Version: Draft Version

License: Creative Commons Attribution

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