Sensitive quantitative predictions of peptide‐MHC binding by a ‘Query by Committee’ artificial neural network approach

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Abstract
Abstract:  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.