DECODING THE INTERACTIONS BETWEEN T CELL RECEPTORS AND PEPTIDE-MHC

总结 同源肽-主要组织相容性复合物的常规TCR识别 pMHC是病原体和病理相关自身蛋白的适应性免疫识别的核心。 尽管使用AlphaFold等工具在蛋白质-蛋白质相互作用的结构预测方面取得了实质性进展， 和RoseTTAFold，从TCR序列从头预测TCR特异性（靶pMHC）尚未被证实。 实现了事实上，在最大的策划TCR特异性数据库中，只有约105个独特的TCR：pMHC 已经策划了分配，并且这些集中在<100个独特的pMHC表位上。在我们之前的工作中， 我们确定，需要超过200个识别相同表位的独特受体才能可靠地预测 以前未观察到的受体是否属于同一特异性组。这项工作表明， 一旦数据足够密集，特异性的局部预测就变得可行。因此，目前 可用数据的稀疏性质是推进该领域的主要限制。因此，我们的中央 一个假设是，推进TCR特异性的预测模型需要显著增加TCR特异性， 策划的TCR-pMHC数据的规模和多样性，这反过来又需要新的方法， 生成如此有用的数据集。在三个目标中，我们将解决目前表位的主要局限性， 发现和TCR表征管道。在目标1中，我们将改进关联单链的方法 TCR序列与特异性肽的结合，用于生成精心策划的TCRα或TCRβ关联的大型文库 单个抗原决定簇。除了支持我们的中心目标外，这些数据还将具有显著的独立性。 用于免疫分析和诊断。在目标2中，我们将建立分配配对链的方法 从单细胞实验到表位库的TCR β数据，扩展了我们最近报道的反向表位 发现管道。目标3将整合公共数据和目标1和2产生的数据， 结构和计算方法来产生改进的从头特异性预测算法。 这些目标将通过访问我们收集的纵向采样的PBMC来实现，这些PBMC来自>4000 来自不同祖先和感染史的精心策划的队列中的人类。

Summary Conventional ɑβ T cell receptor (TCR) recognition of a cognate peptide-Major Histocompatibility Complex (pMHC) is central to adaptive immune recognition of pathogens and pathologically associated self-proteins. Despite substantial progress in structural prediction of protein-protein interactions with tools such as AlphaFold and RoseTTAFold, de novo prediction of TCR specificity (target pMHC) from TCR sequence has not yet been realized. Indeed, within the largest databases of curated TCR specificities, only ~105 unique TCR:pMHC assignments have been curated, and these are focused on <100 unique pMHC epitopes. In our previous work, we established that >200 unique receptors recognizing the same epitope are required to confidently predict whether a previously unobserved receptor belongs to the same specificity group. This work demonstrates that once data are sufficiently dense, local prediction of specificity becomes feasible. It follows that the current sparse nature of the available data is the major restriction to advancing the field. Thus, our central hypothesis is that advancing predictive models for TCR specificity requires a dramatic increase in the magnitude and diversity of curated TCR-pMHC data, which in turn requires new approaches for generating such useful data sets. In three Aims, we will address major limitations of the current epitope discovery and TCR characterization pipelines. In Aim 1, we will improve methods for relating single chain TCR sequences to specific peptides for generating large libraries of well-curated TCRα or TCRβ associations with individual epitopes. In addition to supporting our central goal, these data will have significant independent utility for immune profiling and diagnostics. In Aim 2, we will establish methods for assigning paired chain TCRɑβ data from single cell experiments to epitope pools, extending our recently reported reverse epitope discovery pipeline. Aim 3 will integrate public data and the data generated in Aims 1 and 2, with novel structural and computational approaches to generate improved de novo specificity prediction algorithms. These Aims will be accomplished by accessing our collection of longitudinally sampled PBMCs from >4000 humans across well-curated cohorts from diverse ancestries and infection histories.