Permissive Specificity in Peptide Binding to HLA-DR

描述（由申请方提供）：当CD 4 + T细胞识别主要组织相容性复合物（MHCII）II类分子呈递的肽抗原时，适应性免疫应答开始。MHCII分子的两个突出特征是它们的多态性和每个等位基因结合大量肽的能力。我们的长期目标是正式描述肽识别过程的三个阶段：1）肽-MHCII复合物的形成，2）复合物是否被选择用于呈递，3）复合物是否被T细胞识别。预测表位和T细胞对它们的应答在MHCII发挥重要作用的两个实际领域，疫苗接种和移植中是重要的。本申请的目的是详细表征这三个阶段中的第一阶段，即，HA-DR（DR）结合肽的预测。这里提出的研究将产生一个热力学描述的绑定过程足以提供一个基础，产生预测算法。预测结合的能力将是取得全面进展的重要一步。MHCII和肽代表灵活的结合系统，具有特异性和容许性。这种灵活性是由热力学现象，特别是协同性的存在和等温熵-焓补偿（IEEC）的发生证明。在一种相互作用受其他相互作用影响的系统中观察到协同性，并与蛋白质折叠有关。IEEC描述了熵（增加结构空间的搜索）补偿较差结合能（焓）的能力。这是容许性背后的驱动机制，因为存在亲和力范围，其中具有差结合特性的肽具有找到允许结合的构象的可测量的概率。为了在结合预测模型中包括灵活性，需要更深入地理解在确定分子水平上肽结合的容许特异性时协同性和IEEC之间的相互作用。为了将我们的研究应用于所有DR等位基因，我们还必须测量限定不同等位基因的多态性盒的贡献。因此，在目标1中，我们将研究肽/DR复合物形成中涉及的能量学，解卷积成能量和熵分量，并且我们将IEEC的程度与协同性相关联。在目标2中，我们将定义DR残基对亲和力、折叠和能量学的生物物理贡献，以概述对所有HLA等位基因有效的结合模型。肽结合通常在辅助分子HLA-DM（DM）存在下发生。我们有证据表明，DM影响肽结合通过设置一个许可阈值。因此，在目的3中，我们将研究DR-肽-DM在热力学方面的相互作用，将DM活性与各种pDR复合物的热力学特征相关联。在目标4中，我们概述了一个初始的肽结合预测模型，包括来自前三个目标的结构和热力学数据，我们建议通过预测随机生成的抗原序列内的结合肽来验证这种方法。

DESCRIPTION (provided by applicant): The adaptive immune response starts when CD4+ T cells recognize peptide antigens presented by class II molecules of the Major Histocompatibility Complex (MHCII). Two outstanding features of MHCII molecules are their polymorphism and the ability of each allele to bind a large panoply of peptides. Our long-term goal is to formally describe the three stages of the peptide recognition process: 1) formation of a peptide-MHCII complex, 2) will the complex be selected for presentation, and 3) will the complex be recognized by a T cell. Predicting epitopes and T cell responses to them is important in two practical areas where MHCII plays a significant role, vaccination and trasnplantation. The goal of the present application is to characterize in detail the first of these three stages, i.e., the prediction of HA-DR (DR) binding peptides. The research proposed here will generate a thermodynamic description of the binding process sufficient to provide a basis for generating predictive algorithms. The ability to predict binding will be an important step in making progress overall. The MHCII and peptides represent a flexible binding system, featuring specificity and permissiveness. This flexibility is evidenced by thermodynamic phenomena, in particular the presence of cooperativity and the occurrence of isothermal entropy-enthalpy compensation (iEEC). Cooperativity is observed in systems where one interaction is affected by other interactions and is associated with protein folding. iEEC describes the ability of entropy (increased search of structural space) to compensate for poorer binding energy (enthalpy). This is the driving mechanism behind permissiveness, in that there is an affinity range in which a peptide with poor binding characteristics has a measurable probability of finding a conformation that allows binding. To include flexibility in a binding prediction model, a deeper understanding of the interaction between cooperativity and iEEC in determining the permissive specificity of peptide binding at the molecular level is needed. To apply our studies to all DR alleles we have to also measure the contribution of the polymorphic cassettes that define the different alleles. Therefore, in Aim 1 we will investigate the energetics involved in peptide/DR complex formation, deconvoluted into enthalpic and entropic components and we will correlate the extent of iEEC to cooperativity. In Aim 2 we will define the biophysical contribution of DR residues to affinity, folding, and energetics to outline a binding model valid for all HLA alleles. Peptide binding usually takes place in the presence of an adjunct molecule, HLA-DM (DM). We have evidence that DM affects peptide binding by setting a permissiveness threshold. Therefore in Aim 3 we will investigate the DR-peptide-DM interaction in thermodynamic terms, correlating DM activity to the thermodynamic signatures of various pDR complexes. In Aim 4 we outline an initial peptide-binding prediction model encompassing the structural and thermodynamic data derived in the first three aims and we propose to validate this approach by predicting binding peptides within randomly generated antigenic sequences.