Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report

Abstract

Many different solutions to predicting the cognate epitope target of a T-cell receptor (TCR) have been proposed. However several questions on the advantages and disadvantages of these different approaches remain unresolved, as most methods have only been evaluated within the context of their initial publications and data sets. Here, we report the findings of the first public TCR-epitope prediction benchmark performed on 23 prediction models in the context of the ImmRep 2022 TCR-epitope specificity workshop. This benchmark revealed that the use of paired-chain alpha-beta, as well as CDR1/2 or V/J information, when available, improves classification obtained with CDR3 data, independent of the underlying approach. In addition, we found that straight-forward distance-based approaches can achieve a respectable performance when compared to more complex machine-learning models. Finally, we highlight the need for a truly independent follow-up benchmark and provide recommendations for the design of such a next benchmark. ### Competing Interest Statement This manuscript concerns a meeting report of the IMMREP2022 workshop on TCR-epitope specificity. Many of the authors involved have been involved in the creation of the tools being benchmarked, this has been clearly listed in the report (see Table 1).