Applied Bioinformatics

, Volume 5, Issue 1, pp 55–61

MHCPred 2.0

An Updated Quantitative T-Cell Epitope Prediction Server
  • Pingping Guan
  • Channa K. Hattotuwagama
  • Irini A. Doytchinova
  • Darren R. Flower
Application Note

DOI: 10.2165/00822942-200605010-00008

Cite this article as:
Guan, P., Hattotuwagama, C.K., Doytchinova, I.A. et al. Appl-Bioinformatics (2006) 5: 55. doi:10.2165/00822942-200605010-00008

Abstract

The accurate computational prediction of T-cell epitopes can greatly reduce the experimental overhead implicit in candidate epitope identification within genomic sequences. In this article we present MHCPred 2.0, an enhanced version of our online, quantitative T-cell epitope prediction server. The previous version of MHCPred included mostly alleles from the human leukocyte antigen A (HLA-A) locus. In MHCPred 2.0, mouse models are added and computational constraints removed. Currently the server includes 11 human HLA class I, three human HLA class II, and three mouse class I models. Additionally, a binding model for the human transporter associated with antigen processing (TAP) is incorporated into the new MHCPred. A tool for the design of heteroclitic peptides is also included within the server. To refine the veracity of binding affinities prediction, a confidence percentage is also now calculated for each peptide predicted.

Copyright information

© Adis Data Information BV 2006

Authors and Affiliations

  • Pingping Guan
    • 1
  • Channa K. Hattotuwagama
    • 1
  • Irini A. Doytchinova
    • 1
  • Darren R. Flower
    • 1
  1. 1.Edward Jenner Institute for Vaccine ResearchCompton, Newbury, BerkshireUK