| Summary: | Abstract Background The RATE tool was recently developed to computationally infer the HLA restriction of given epitopes from immune response data of HLA typed subjects without additional cumbersome experimentation. Results Here, RATE was validated using experimentally defined restriction data from a set of 191 tuberculosis-derived epitopes and 63 healthy individuals with MTB infection from the Western Cape Region of South Africa. Using this experimental dataset, the parameters utilized by the RATE tool to infer restriction were optimized, which included relative frequency (RF) of the subjects responding to a given epitope and expressing a given allele as compared to the general test population and the associated p-value in a Fisher’s exact test. We also examined the potential for further optimization based on the predicted binding affinity of epitopes to potential restricting HLA alleles, and the absolute number of individuals expressing a given allele and responding to the specific epitope. Different statistical measures, including Matthew’s correlation coefficient, accuracy, sensitivity and specificity were used to evaluate performance of RATE as a function of these criteria. Based on our results we recommend selection of HLA restrictions with cutoffs of p-value < 0.01 and RF ≥ 1.3. The usefulness of the tool was demonstrated by inferring new HLA restrictions for epitope sets where restrictions could not be experimentally determined due to lack of necessary cell lines and for an additional data set related to recognition of pollen derived epitopes from allergic patients. Conclusions Experimental data sets were used to validate RATE tool and the parameters used by the RATE tool to infer restriction were optimized. New HLA restrictions were identified using the optimized RATE tool.
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