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: 8045569
• 负责人: Chien-Fu Jeff Wu
• 金额: $ 29.65万
• 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/8045569
• 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）呈现的不同肽，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，它的机制是知之甚少，至少部分是由于缺乏适当的工具来分析的初始识别事件的尺度小到单分子相互作用和短暂的亚秒，这是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-两种技术探测TCR/pMHC相互作用的第一秒-以及其他两种单键方法（解结合力和键寿命测定）将用于拟议的研究，以研究TCR/pMHC，CD 8/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，需要统计建模方法进行数据分析。具体地，上述测定中的三个的数据是以固定间隔的二元粘附分数的时间序列或解粘力或键寿命的连续值的形式。的 热涨落分析的数据是以随机间隔的交替键寿命和等待时间的形式。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利用率。新的一类时间序列模型将被用来量化的相关性的附着力得分，松解力和债券的寿命，这表现为记忆效应，即，T细胞“记忆”先前粘附事件并改变下一粘附事件的发生概率和解结合力的概率密度和寿命的能力。混合物分布将用于确定所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。在热波动试验中，将采用变点公式进行统计估计。这些统计模型将通过实验进行测试，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。 公共卫生关系：TCR/pMHC相互作用的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细胞应答中具有核心作用。将敏感的单分子实验与统计建模相结合，将使我们能够提取理解T细胞识别不同肽所需的新信息，从而可能导致基于改变的肽配体的新疗法。