AI Article Synopsis
- Accurate T-cell epitope prediction is essential for developing effective vaccines and plays a key role in adaptive immunity through antigen presentation.
- MHCPred is a web-based tool that utilizes a multivariate statistical method to predict how peptides bind to major histocompatibility complexes (MHC), focusing on both Class I and Class II alleles.
- The tool supports a variety of MHC alleles and can be accessed online for research in immunology and vaccinology at http://www.jenner.ac.uk/MHCPred.
Article Abstract
Accurate T-cell epitope prediction is a principal objective of computational vaccinology. As a service to the immunology and vaccinology communities at large, we have implemented, as a server on the World Wide Web, a partial least squares-based multivariate statistical approach to the quantitative prediction of peptide binding to major histocom- patibility complexes (MHC), the key checkpoint on the antigen presentation pathway within adaptive cellular immunity. MHCPred implements robust statistical models for both Class I alleles (HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3301, HLA-A*6801, HLA-A*6802 and HLA-B*3501) and Class II alleles (HLA-DRB*0401, HLA-DRB*0401 and HLA-DRB*0701). MHCPred is available from the URL: http://www.jenner.ac.uk/MHCPred.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC168917 | PMC |
| http://dx.doi.org/10.1093/nar/gkg510 | DOI Listing |
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