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.

摘要 主要组织相容性复合物（MHC）通过呈递抗原来引导免疫应答 细胞表面上的片段并与T细胞受体（TCR）相互作用。近年来不少 T细胞疗法已经成功地将T细胞工程化以靶向MHC-抗原复合物 与癌症和其他疾病有关。然而，大多数T细胞疗法需要识别 与感兴趣的MHC-抗原复合物相互作用的TCR， 过程我们的提议旨在通过一种计算算法来加快这一搜索过程 这将预测TCR是否会与感兴趣的MHC等位基因相互作用。目前的筛选 低频率TCR的测定具有高的假阳性率。研究人员可以使用我们的工具， 在运行前计算过滤TCR候选者与特定MHC等位基因的相互作用 昂贵的验证实验。在本提案中，我们将首先通过 我们将在公共TCR-MHC相互作用数据上训练原型算法。然后我们将进行 新的四聚体染色实验，解决了两个主要挑战，为发展一个 跨多个MHC等位基因的算法：缺乏除A*02以外的等位基因的相互作用数据， 以及现有公共数据中有限的抗原多样性。这些实验将提供 四种常见MHC等位基因的800种抗原的TCR-MHC数据：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组分的重要性（例如，α或β通道， CDR 3）在预测TCR-MHC相互作用中的作用。我们的工作将产生第一个计算工具， 帮助T细胞治疗开发人员根据MHC特异性筛选TCR候选者。超越细胞 该工具还将帮助研究人员在MHC等位基因发挥作用的疾病中跟踪T细胞。 主要角色。