Abstract
Background: For design of a subunit vaccine for tuberculosis, identification of antigenic T-cell epitope is of utmost importance. Several MHC prediction server are available that can accurately predict antigenic peptide of variable lengths. However, peptides predicted from one server not necessarily are predicted form another server, thus creating a confusing situation for scientists to choose a best epitope.
Method: Keeping the above problem in mind, we developed a comprehensive database of peptides of Mycobacterial proteins. Each protein was taken from PubMed and was run through different MHC prediction servers, with the results being compiled into one database.
Results: For each protein, PeMtb generates a set of three different mers of variable lengths (12 mer or 13-mer) based on: their ranking; with each mer being predicted for a plethora of MHC alleles. Researcher can choose the peptide (mers) that gives best binding affinity from most of the servers.
Conclusion: The database relieves the investigators of the painstaking task of searching various MHC prediction servers for the right epitope (T-cell epitope) for a particular Mycobacterial antigen. We trust and anticipate that PeMtb will be a practical platform for trial and computational analyses of antigenic peptides for Mycobacterium tuberculosis. All the resources and information can be accessed by PeMtb home page www.pemtb-amu.org.