权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
8328957
负责人：
Chien-Fu Jeff Wu
金额：
$ 28.96万
依托单位：
GEORGIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-01 至 2014-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8328957
关键词：
Adhesions Affect Affinity Agonist Antigen-Presenting Cells Antigens Autoimmune Diseases Binding Biochemistry Biological Assay Biophysics CD8 Antigens CD8B1 gene Cell membrane Cell surface Characteristics Complement Cytoskeleton Data Data Analyses Disease Dissociation Docking Drug Formulations Environment Event Failure Feedback Foundations Frequencies Health Human Immune Immune system Immunologist Intercellular adhesion molecule 1 Kinetics Left Ligand Binding Ligands Link Liquid substance MHC Interaction Major Histocompatibility Complex Measurement Measures Membrane Memory Methods Metric Modeling Molecular Conformation Nature Organism Outcome Paper Peptides Phase Population Population Heterogeneity Probability Process Property Recruitment Activity Research Resolution Role Series Signal Transduction Solutions Statistical Models Structure Study Section Surface System T cell response T-Cell Receptor T-Lymphocyte TCR Activation Techniques Testing Time adaptive immunity base complement C2a density empowered experience interest method development molecular scale novel pathogen receptor receptor binding research study response single bond single molecule statistics stem tool two-dimensional

项目摘要

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.

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）呈现的不同肽是适应性免疫保护人类免受致病病原体侵害的核心问题。然而，其机制尚不清楚，至少部分原因是缺乏适当的工具来分析小到单分子相互作用和短到亚秒级的初始识别事件，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相互作用的第一秒的技术-以及其他两种单键方法（解结合力和键寿命测定）将用于拟议的研究中，以研究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，数据分析需要采用统计建模的方法。具体而言，上述三种分析的数据均以二元粘附分数的时间序列或固定间隔的解结合力或键寿命的连续值的形式存在。热波动试验的数据是随机间隔的交替键寿命和等待时间。虽然可以通过使用描述性统计获得一些信息，但更复杂的统计建模将使我们能够大大增加对数据的理解和使用。新的时间序列模型将用于量化粘附分数、解结合力和键寿命之间的相关性，这表现为记忆效应，即T细胞“记住”先前粘附事件的能力，并改变下一次粘附事件发生的概率和解结合力的概率密度和寿命。混合分布将用于确定测量的单键事件是由单一状态的均匀种群还是多状态混合物的异质种群组成。在热波动试验中，将采用变点公式进行统计估计。这些统计模型将在实验中进行测试，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。