DECODING THE INTERACTIONS BETWEEN T CELL RECEPTORS AND PEPTIDE-MHC

解码 T 细胞受体和肽-MHC 之间的相互作用

• 批准号: 10682160
• 负责人: Paul G. Thomas
• 金额: $ 89.07万
• 依托单位: ST. JUDE CHILDREN'S RESEARCH HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-20 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10682160
• 关键词: 3-Dimensional; Address; Antigens; Award; Binding; Biological; Cells; Collaborations; Collection; Complex; Data; Data Set; Databases; Diagnostic; Epitopes; Gene Expression; Genetic Transcription; Goals; Human; Immune; Immunologist; Individual; Infection; Libraries; Major Histocompatibility Complex; Methods; Modeling; Nature; Pathologic; Peptide Receptor; Peptide/MHC Complex; Peptides; Peripheral Blood Mononuclear Cell; Productivity; Proteins; Protocols documentation; Recording of previous events; Reporting; Sampling; Source; Specificity; Structure; T Chain; T-Cell Antigen Receptor Specificity; T-Cell Receptor; TCR Activation; Therapeutic; Training; Work; cohort; combinatorial; complex data; computer framework; experimental study; improved; innovation; novel; novel strategies; pathogen; prediction algorithm; predictive modeling; programs; protein protein interaction; receptor; tool; 3-Dimensional; Address; Antigens; Award; Binding; Biological; Cells; Collaborations; Collection; Complex; Data; Data Set; Databases; Diagnostic; Epitopes; Gene Expression; Genetic Transcription; Goals; Human; Immune; Immunologist; Individual; Infection; Libraries; Major Histocompatibility Complex; Methods; Modeling; Nature; Pathologic; Peptide Receptor; Peptide/MHC Complex; Peptides; Peripheral Blood Mononuclear Cell; Productivity; Proteins; Protocols documentation; Recording of previous events; Reporting; Sampling; Source; Specificity; Structure; T Chain; T-Cell Antigen Receptor Specificity; T-Cell Receptor; TCR Activation; Therapeutic; Training; Work; cohort; combinatorial; complex data; computer framework; experimental study; improved; innovation; novel; novel strategies; pathogen; prediction algorithm; predictive modeling; programs; protein protein interaction; receptor; tool

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.

概括 常规的ɑβT细胞受体（TCR）识别同源肽 - 莫约尔组织相容性复合物 （PMHC）对于病原体和病理相关的自蛋白的适应性免疫认知至关重要。 尽管在蛋白质 - 蛋白质相互作用的结构预测与αFOLD等工具的结构预测方面取得了长足进展 Rosettafold，TCR序列的TCR特异性（目标PMHC）的从头预测尚未 实现。实际上，在最大的TCR规范的最大数据库中，仅〜105唯一TCR：PMHC 作业已经策划，这些分配的重点是<100个独特的PMHC表位。在我们以前的工作中 我们确定> 200个独特的接收器认识到同一情节需要自信地预测 先前未观察到的受体是否属于相同的特异性组。这项工作表明 一旦数据充分密集，特异性的局部预测就会可行。因此，电流 可用数据的稀疏性质是推进该领域的主要限制。那，我们的中心 假设是，提高TCR特异性的预测模型需要急剧增加 策划的TCR-PMHC数据的大小和多样性，进而需要新的方法 生成这样有用的数据集。在三个目标中，我们将解决当前episodetope的主要局限性 发现和TCR表征管道。在AIM 1中，我们将改善关联单链的方法 TCR序列到特定的Petides生成大型曲线的TCRα或TCRβ关联的库 与单个表位。除了支持我们的中心目标外，这些数据还将具有重要的独立 免疫分析和诊断的实用程序。在AIM 2中，我们将建立分配配对链的方法 从单细胞实验到表位池的TCRβ数据，扩展了我们最近报道的反向表位 发现管道。 AIM 3将将公共数据和目标1和2中产生的数据与新颖 结构和计算方法，以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> 4000的纵向采样PBMC来实现 来自潜水祖先和感染历史的良好策划人群的人类。