Statistical Modeling of Receptor/Ligand Binding Kinetics on the T cell Surface

T 细胞表面受体/配体结合动力学的统计模型

• 批准号: 8132564
• 负责人: Chien-Fu Jeff Wu
• 金额: $ 29.24万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8132564
• 关键词: Adhesions; Binding; Biological Assay; CD8 Antigens; Cell surface; Characteristics; Data; Data Analyses; Disease; Drug Formulations; Event; Frequencies; Human; Kinetics; Ligand Binding; Ligands; MHC Interaction; Major Histocompatibility Complex; Measures; Memory; Methods; Modeling; Nature; Peptides; Population; Population Heterogeneity; Probability; Research; Resolution; Role; Series; Statistical Models; T cell response; T-Cell Receptor; T-Lymphocyte; TCR Activation; Techniques; Testing; Time; adaptive immunity; base; density; interest; pathogen; public health relevance; receptor; research study; single bond; single molecule; statistics; stem; tool

DESCRIPTION (provided by applicant): How T cell receptor (TCR) discriminates different peptides presented by the major histocompatibility complex molecule (pMHC) is a central question in adaptive immunity that defends humans against disease-causing pathogens. Yet its mechanism is poorly understood due at least partly to the lack of appropriate tools to analyze the initial recognition events at scales as small as single molecular interactions and as brief as subseconds, which are beyond the temporal and spatial resolutions of standard techniques. Adhesion frequency and thermal fluctuation assays - two techniques probing the first seconds of TCR/pMHC interactions - as well as two other single-bond methods (unbinding force and bond lifetime assays) will be used in the proposed research to study the dynamics of TCR/pMHC, CD8/MHC interactions and their crosstalk. Because of the inherent stochastic nature of single molecular interactions, statistical modeling approach is required for data analysis. Specifically, the data of three of the above assays are in the form of time series of binary adhesion scores or continuous values of unbinding forces or bond lifetimes with fixed intervals. The data of the thermal fluctuation assay are in the form of alternating bond lifetime and waiting time of random intervals. While some information can be obtained by using descriptive statistics, more sophisticated statistical modeling will enable us to greatly increase the understanding and utilities of the data. New class of time series models will be used to quantify the correlations of the adhesion scores, unbinding forces and bond lifetimes, which are manifested as memory effects, i.e., T cell's ability to "remember" the previous adhesion event and to alter the probability of occurrence and the probability densities of unbinding forces and lifetimes of the next adhesion event. Mixture distribution will be used to determine whether the measured single-bonds events consist of a homogeneous population of single states or a heterogeneous population of multi-state mixture. Change-point formulation will be employ for statistical estimation in the thermal fluctuation assay. These statistical models will be tested experimentally and modified as needed to extract the fundamental characteristics of TCR interaction with different peptides. PUBLIC HEALTH RELEVANCE: The sustained interest in the kinetic analysis of TCR/pMHC interactions stems from a fundamental hypothesis that the interaction parameters have a central role in determining the subsequent T cell response. Combination of sensitive single-molecule experiments with statistical modeling will allow us to extract new information required for understanding of T cell recognition of different peptides potentially leading to new therapies based on altered peptide ligands.

描述（由申请人提供）：T细胞受体（TCR）如何区分主要组织相容性复合体分子（pMHC）呈递的不同肽是保护人类免受致病病原体侵害的适应性免疫的核心问题。然而，人们对其机制了解甚少，至少部分原因是缺乏适当的工具来分析小至单分子相互作用和短至亚秒的初始识别事件，这超出了标准技术的时间和空间分辨率。粘附频率和热波动测定——两种探测 TCR/pMHC 相互作用第一秒的技术——以及另外两种单键方法（解离力和键寿命测定）将在拟议的研究中用于研究 TCR/pMHC、CD8/MHC 相互作用及其串扰的动力学。 由于单分子相互作用固有的随机性，数据分析需要统计建模方法。具体而言，上述测定中的三个测定的数据采用二元粘附分数的时间序列或具有固定间隔的解开力或键寿命的连续值的形式。这 热波动测定的数据采用交替键寿命和随机间隔的等待时间的形式。虽然一些信息可以通过使用描述性统计来获得，但更复杂的统计建模将使我们能够大大提高对数据的理解和效用。新一类时间序列模型将用于量化粘附分数、解绑力和键寿命之间的相关性，这些相关性表现为记忆效应，即T细胞“记住”先前的粘附事件并改变下一个粘附事件发生的概率以及解绑力和寿命的概率密度的能力。混合分布将用于确定测量的单键事件是否由单一状态的同质群体或多状态混合物的异质群体组成。变化点公式将用于热波动测定中的统计估计。这些统计模型将进行实验测试并根据需要进行修改，以提取 TCR 与不同肽相互作用的基本特征。 公共健康相关性：对 TCR/pMHC 相互作用动力学分析的持续兴趣源于一个基本假设，即相互作用参数在确定随后的 T 细胞反应中起着核心作用。敏感的单分子实验与统计模型的结合将使我们能够提取理解 T 细胞对不同肽的识别所需的新信息，从而可能导致基于改变的肽配体的新疗法。