Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach.
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.
Buus
S
S
Division of Experimental Immunology, Institute of Medical Microbiology and Immunology, University of Copenhagen, Denmark. S.Buus@immi.ku.dk
Lauemøller
S L
SL
Worning
P
P
Kesmir
C
C
Frimurer
T
T
Corbet
S
S
Fomsgaard
A
A
Hilden
J
J
Holm
A
A
Brunak
S
S
D015234:HLA-A Antigens / Q000276:immunology* / Q000378:metabolism; D006801:Humans; D016571:Neural Networks, Computer; D010455:Peptides / Q000378:metabolism*; D011485:Protein Binding; D020543:Proteome / Q000378:metabolism
D015234:HLA-A Antigens; D010455:Peptides; D020543:Proteome