Abstract
Recognition of peptides bound to major histocompatibility complex (MHC) class I molecules by T lymphocytes is an essential part of immune surveillance. Each MHC allele has a characteristic peptide binding preference, which can be captured in prediction algorithms, allowing for the rapid scan of entire pathogen proteomes for peptide likely to bind MHC. Here we make public a large set of 48,828 quantitative peptide-binding affinity measurements relating to 48 different mouse, human, macaque, and chimpanzee MHC class I alleles. We use this data to establish a set of benchmark predictions with one neural network method and two matrix-based prediction methods extensively utilized in our groups. In general, the neural network outperforms the matrix-based predictions mainly due to its ability to generalize even on a small amount of data. We also retrieved predictions from tools publicly available on the internet. While differences in the data used to generate these predictions hamper direct comparisons, we do conclude that tools based on combinatorial peptide libraries perform remarkably well. The transparent prediction evaluation on this dataset provides tool developers with a benchmark for comparison of newly developed prediction methods. In addition, to generate and evaluate our own prediction methods, we have established an easily extensible web-based prediction framework that allows automated side-by-side comparisons of prediction methods implemented by experts. This is an advance over the current practice of tool developers having to generate reference predictions themselves, which can lead to underestimating the performance of prediction methods they are not as familiar with as their own. The overall goal of this effort is to provide a transparent prediction evaluation allowing bioinformaticians to identify promising features of prediction methods and providing guidance to immunologists regarding the reliability of prediction tools.
Highlights
Cytotoxic T lymphocytes of the vertebrate immune system monitor cells for infection by viruses or intracellular bacteria by scanning their surface for peptides bound to major histocompatibility complex (MHC) class I molecules
MHC molecule were recorded in multiple assays, the geometric mean of the IC50 values was taken as the consensus value in the final dataset
No additional homology reduction was performed on the peptide sequences, because this should be done by the tool developers, who may prefer to use different homologyreduction approaches that are best optimized for their specific methods
Summary
Cytotoxic T lymphocytes of the vertebrate immune system monitor cells for infection by viruses or intracellular bacteria by scanning their surface for peptides bound to major histocompatibility complex (MHC) class I molecules (reviewed in [1]). Cells presenting peptides derived from nonself proteins, such as viruses or bacteria, can trigger a T-cell immune response leading to the destruction of the cell. Many computational algorithms have been created to predict which peptides contained in a pathogen are likely T-cell epitopes [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25] Such tools allow for the rapid scan of the proteome of a pathogen, and are being widely used in the immunological community.
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