| Summary: | Computational T-cell epitope prediction tools have been previously devised to predict potential human leukocyte antigen (HLA) binding peptides from protein sequences. These tools are complements of Enzyme-linked immunosorbent spot (ELISpot) assays - a very commonly applied immunological technique that is used both to identify regions of pathogen genomes that trigger an immune response and to characterize the relationships between an individual's complement of HLA alleles and the degree of immunity that they display. If computational tools could accurately predict HLA-peptide binding, then these tools might be useable as a cheap and reliable alternative to ELISpot assays. A web-based IFN γ ELISpot assay dataset sharing resource, called IMMUNO-SHARE, was developed to enable the simple and straightforward storage and dissemination amongst researchers of large volumes of IFN γ ELISpot assay data. Such experimental data was next used to make HLA-peptide binding predictions with four frequently used T-cell epitope prediction tools - netMHC 3.2, IEDB_ANN, IEDB_ARB Matrix and IEDB_SMM. The predictive performances of all four tools individually and collectively was statistically assessed using non-parametric Spearman rank-order correlation tests. It was found that none of the four tested tools yielded binding affinity predictions that were detectably correlated with the observed ELISpot data. High false positive rates, where high predicted binding affinities between peptides and patient HLAs corresponded in these patients with no appreciable immune responses, were apparent for all four of the tested methods. The low degree of correlation between ELISpot data and HLA-peptide binding predictions and in particular, high false positive rates and relatively low true positive and true negative rates, indicate that the four tested tools would require substantial improvement before they could be seen as a viable alternative to ELISpot assays. Given that the accuracy of predictions of each of the four methods tested is largely dependent on both the quantity and quality of known true binder and true non-binder datasets that were used to train the HLA-peptide binding prediction methods implemented by the tools, it is plausible that the accuracy of these tools could be increased with larger training datasets. Retraining either the current methods or the next generation of prediction tools would therefore be greatly facilitated by the availability of large quantities of publically available HLA-peptide binding interaction information. It is hoped that IMMUNO-SHARE or some other ELISpot data sharing resource could eventually meet this need.
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