Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach

Tissue Antigens. 2003 Nov;62(5):378-84. doi: 10.1034/j.1399-0039.2003.00112.x.


We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict binding vs non-binding peptides. Furthermore, quantitative ANN allowed a straightforward application of a 'Query by Committee' (QBC) principle whereby particularly information-rich peptides could be identified and subsequently tested experimentally. Iterative training based on QBC-selected peptides considerably increased the sensitivity without compromising the efficiency of the prediction. This suggests a general, rational and unbiased approach to the development of high quality predictions of epitopes restricted to this and other HLA molecules. Due to their quantitative nature, such predictions will cover a wide range of MHC-binding affinities of immunological interest, and they can be readily integrated with predictions of other events involved in generating immunogenic epitopes. These predictions have the capacity to perform rapid proteome-wide searches for epitopes. Finally, it is an example of an iterative feedback loop whereby advanced, computational bioinformatics optimize experimental strategy, and vice versa.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • HLA-A Antigens / immunology*
  • HLA-A Antigens / metabolism
  • Humans
  • Neural Networks, Computer*
  • Peptides / metabolism*
  • Protein Binding
  • Proteome / metabolism


  • HLA-A Antigens
  • Peptides
  • Proteome