It’s not just the models, it’s the data!
It’s not just the models, it’s the data!
Accurate prediction of TCR reactivity forms a holy grail in immunology and large language models and computational structure predictions provide a path to achieve this. Importantly, current TCR-pMHC prediction models have been trained and evaluated using historical data of unknown quality.
Here, we develop and utilize a high-throughput synthetic platform for TCR assembly and evaluation to experimentally assess claimed reactivity of VDJdb-deposited TCRs for 8 well-studied viral epitopes, jointly forming approx. 40% of the TCR-pMHC pairs in this database, using a standardized readout of TCR function. Strikingly, this analysis demonstrates that claimed TCR reactivity is only confirmed for approximately 50% of evaluated entries. Intriguingly, the use of TCRbridge to analyze AlphaFold3 confidence metrics for these TCR-pMHC pairs reveals a substantial performance in distinguishing functionally validating and non-validating TCRs even though AlphaFold3 was not trained on this task. These data demonstrate the utility of the validated VDJdb (TCRvdb) database that we generated, and we provide TCRvdb as a resource to the community to support training and evaluation of improved predictive TCR specificity models.
Marius Messemaker Bio
Marius Messemaker is a PhD student in Ton Schumacher’s lab at the Netherlands Cancer Institute, where he combines wet-lab and computational approaches to decode how TCRs recognize antigens. His work focuses on developing and utilising high-throughput TCR reactivity screening methods to generate large-scale datasets, which he then uses to build computational models of T cell antigen recognition.
During his master’s, he trained in single-cell genomic method development in the lab of Alexander van Oudenaarden at the Hubrecht institute, and later applied these techniques to map immune cell states in the tumor microenvironment in response to immunotherapy, working in the labs of Mikael Pittet and Allon Klein at MGH & Harvard Medical School. This experience sparked a strong interest in cancer immunology, ultimately leading him to focus on T cell recognition and antigen specificity in his PhD research.