Computational algorithm to predict interacting MHC alleles from TCR sequences

从 TCR 序列预测相互作用的 MHC 等位基因的计算算法

• 批准号: 10384615
• 负责人: Binbin Chen
• 金额: $ 25.66万
• 依托单位: VCREATE, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-10 至 2023-02-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10384615
• 关键词: Address; Algorithms; Alleles; Ankylosing spondylitis; Antigen Targeting; Antigens; Autoimmune Diseases; Biological Assay; Biological Sciences; Cancer Patient; Cell Therapy; Cell surface; Complex; Computational algorithm; Data; Data Set; Disease; Frequencies; Goals; Grant; Human; Immune response; Individual; Interest Group; Libraries; Major Histocompatibility Complex; Malignant Neoplasms; Measures; Output; Patients; Performance; Phase; Play; Population; Probability; Process; Research; Research Personnel; Resistance; Role; Running; Services; Specificity; Speed; Stains; Structure; T cell therapy; T-Cell Receptor; T-Lymphocyte; Testing; Therapeutic; Time; Training; Tumor Tissue; Validation; Work; Yeasts; antigen binding; base; clinically relevant; complex data; computerized tools; cost; engineered T cells; experimental study; human data; improved; interest; machine learning algorithm; novel therapeutics; prediction algorithm; prototype; screening; tool

Abstract Major histocompatibility complexes (MHC) guide immune response by presenting antigen fragments on a cell’s surface and interacting with T-cell receptors (TCRs). In recent years, many T-cell therapies have successfully engineered T-cells to target MHC-antigen complexes associated with cancers and other diseases. However, most T-cell therapies require identifying a TCR that interacts with an MHC-antigen complex of interest, a slow and expensive search process. Our proposal aims to speed up this search process through a computational algorithm that will predict whether a TCR will interact with an MHC allele of interest. Current screening assays for low frequency TCRs have high false positive rates. Researchers can use our tool to computationally filter TCR candidates for interaction with a specific MHC allele before running expensive validation experiments. In this proposal, we will first validate our approach through a prototype algorithm that we will train on public TCR-MHC interaction data. We will then conduct new tetramer staining experiments that address two major challenges for developing an algorithm across multiple MHC alleles: the lack of interaction data for alleles other than A*02, and the limited antigen diversity in existing public data. These experiments will provide TCR-MHC data across 800 antigens for four common MHC alleles: A*01:01, A*02:01, A*11:01, and B*07:02. Finally, we will construct and validate computational algorithms for each MHC allele and evaluate the importance of various TCR components (e.g., alpha or beta chan, CDR3) in predicting TCR-MHC interaction. Our work will result in the first computational tool to help T-cell therapy developers filter TCR candidates based on MHC specificity. Beyond cell therapies, this tool will also help researchers track T-cells in diseases where MHC alleles play a major role.

抽象的 主要的组织相容性复合物（MHC）指南免疫反应通过呈现抗原 细胞表面上的碎片与T细胞受体相互作用（TCR）。 T细胞疗法已成功地设计了T细胞，以靶向MHC抗原复合物 但是，与癌症和其他疾病相关。 与MHC-抗原感兴趣的TCR相互作用，这是一种缓慢而昂贵的搜索 流程。我们的建议旨在通过计算算法加快搜索过程 这将预测与感兴趣的MHC等位基因相互作用的TCR Loquency TCR的测定较高的假阳性率。 在运行之前，计算过滤TCR候选者与特定的MHC等位基因相互作用 昂贵的验证实验。 原型算法训练公共TCR-MHC交互数据。 新的四聚体染色实验，解决了开发两个主要挑战 跨多个MHC等位基因的算法：A*02以外的其他Allels缺乏交互数据 这些实验的抗原多样性有限。 四个常见MHC的800抗原的TCR-MHC数据Alleless：A*01：01，A*02：01，A*11：01， 和b*07：02。最后，我们将为每个MHC构建和验证计算算法 等位基因并评估各种TCR组件的重要性（例如，Alpha或Beta Chan， CDR3）在预测TCR-MHC相互作用时，我们的工作将导致第一个计算工具 帮助T细胞疗法开发人员过滤基于MHC特异性的TCR候选 疗法，此工具还将帮助细胞在MHC Allels发挥的疾病中的T细胞T细胞 主要角色。