Prediction of Epitopes Using Neural Network Based Methods

J Immunol Methods. 2011 Nov 30;374(1-2):26-34. doi: 10.1016/j.jim.2010.10.011. Epub 2010 Oct 31.

Abstract

In this paper, we describe the methodologies behind three different aspects of the NetMHC family for prediction of MHC class I binding, mainly to HLAs. We have updated the prediction servers, NetMHC-3.2, NetMHCpan-2.2, and a new consensus method, NetMHCcons, which, in their previous versions, have been evaluated to be among the very best performing MHC:peptide binding predictors available. Here we describe the background for these methods, and the rationale behind the different optimization steps implemented in the methods. We go through the practical use of the methods, which are publicly available in the form of relatively fast and simple web interfaces. Furthermore, we will review results obtained in actual epitope discovery projects where previous implementations of the described methods have been used in the initial selection of potential epitopes. Selected potential epitopes were all evaluated experimentally using ex vivo assays.

MeSH terms

  • Algorithms
  • Alleles
  • Amino Acid Sequence
  • Epitope Mapping / methods*
  • Epitopes, T-Lymphocyte / genetics
  • Epitopes, T-Lymphocyte / metabolism*
  • Histocompatibility Antigens Class I / genetics
  • Histocompatibility Antigens Class I / metabolism*
  • Humans
  • Internet
  • Models, Molecular
  • Molecular Sequence Data
  • Neural Networks, Computer*
  • Peptides / genetics
  • Peptides / immunology
  • Peptides / metabolism

Substances

  • Epitopes, T-Lymphocyte
  • Histocompatibility Antigens Class I
  • Peptides