Learning a molecular shape space for the adaptive immune system

学习适应性免疫系统的分子形状空间

• 批准号: 10467050
• 负责人: Armita Nourmohammad
• 金额: $ 36.84万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10467050
• 关键词: 3-Dimensional; Adaptive Immune System; Address; Affinity; Amino Acids; Antibodies; B-Lymphocytes; Binding; Biological; Biological Process; Biophysics; Catalytic Domain; Collaborations; Complex; Computer Models; Data; Framework Regions; Immune; Immune Targeting; Immunologic Receptors; Learning; Machine Learning; Modeling; Molecular; Neighborhoods; Property; Protein Subunits; Proteins; Shapes; Specific qualifier value; Specificity; Structure; Surface; T-Cell Receptor; Techniques; Thinness; biophysical properties; cell type; design; innovation; machine learning method; molecular recognition; molecular shape; novel; pathogen; protein function; protein protein interaction; protein structure; receptor; response; three dimensional structure

Project Summary The adaptive immune system consists of highly diverse B- and T-cell receptors, which can recognize and neutralize a multitude of diverse pathogens. Immune recognition relies on molecular interactions between immune receptors and pathogens, which in turn is determined by the complementarity of their 3D structures and amino acid compositions, i.e., their shapes. Immune shape space has been previously introduced as an abstraction for such molecular recognition to explain how immune repertoires are organized to counter diverse pathogens. However, the relationships between immune receptor sequence, shape, and specificity are very difficult to quantify in practice. We propose to use recent advances in machine learning and the wealth of molecular data to infer an effective shape space, grounded in biophysics of protein interactions. The key is to find a representation of proteins in general, and of immune receptors, in particular, that reflects the relevant biophysical properties that determine a protein receptor’s stability, function, and interaction with pathogens. Representation learning is a powerful technique in machine learning that uses large amounts of data to infer a reduced representation. Since protein function is closely related to the 3D structure, we will develop novel machine learning methods that use atomic coordinates of a protein structure as input and, through transformations that respect the physical symmetries in the data, learn representations that reflect biophysical properties of proteins and protein-protein interactions. We believe a key innovation in our approach is the analysis of amino acid neighborhoods within 3D protein structures. The distribution of these neighborhoods will reveal how they differ at the surface, in the bulk, and at functionally important regions such as catalytic sites. The learned protein representation will enable us to characterize how specific compositions of amino acid neighborhoods are the building blocks of protein structure and protein function. We will transfer the representation of protein universe to immune receptors to learn the immune shape space. The leaned immune shape space will enable us to address how affinity and specificity are encoded by immune receptors in different cell types. We will study how the modular structure of immune receptors, with separate pathogen engaging and framework regions, enables receptors to diversify and target a multitude of pathogens, without compromising their stability. We will use the complementary aspect of shape recognition to predict the antigenic targets of the immune receptors, and through collaborations, we will experimentally validate our predictions. Our approach opens a new path towards interpretable computational models of proteins and immune receptors that describe how biological properties and biological function emerge from protein subunits. Additionally, the inferred molecular representations can be used as a generative model, where desired properties, such as antigenic targets, are specified and new proteins can be generated.

项目概要 适应性免疫系统由高度多样化的 B 细胞和 T 细胞受体组成，可以识别和 中和多种不同的病原体 免疫识别依赖于分子之间的相互作用。 免疫受体和病原体，这又是由它们的 3D 结构的互补性决定的 氨基酸组成，即它们的形状，之前已作为一个介绍。 对这种分子识别的抽象，以解释免疫库是如何组织起来对抗多样化的 然而，免疫受体序列、形状和特异性之间的关系非常密切。 我们建议利用机器学习的最新进展和丰富的经验。 分子数据推断出有效的形状空间，其关键是基于蛋白质相互作用的生物物理学。 找到一般蛋白质的表示，特别是免疫受体的表示，反映相关的 决定蛋白质受体的稳定性、功能以及与病原体相互作用的生物物理特性。 表示学习是机器学习中的一项强大技术，它使用大量数据来 由于蛋白质功能与 3D 结构密切相关，我们将开发新的表示。 使用蛋白质结构的原子坐标作为输入的机器学习方法，通过 尊重数据中物理对称性的转换，学习反映生物物理的表示 我们相信我们的方法的一个关键创新是蛋白质的特性和蛋白质-蛋白质相互作用。 分析 3D 蛋白质结构内的氨基酸邻域将这些邻域的分布。 揭示它们在表面、本体和功能重要区域（例如催化位点）上的差异。 学到的蛋白质表示将使我们能够表征氨基酸的特定组成如何 邻域是蛋白质结构和蛋白质功能的组成部分。 蛋白质宇宙向免疫受体的表示，以了解免疫形状空间。 形状空间将使我们能够解决不同免疫受体如何编码亲和力和特异性的问题 我们将研究免疫受体的模块化结构如何与单独的病原体结合和作用。 框架区，使受体能够多样化并针对多种病原体，而不会影响 我们将利用形状识别的互补方面来预测抗原靶点。 免疫受体，通过合作，我们将通过实验验证我们的预测。 我们的方法为蛋白质和免疫的可解释计算模型开辟了一条新途径 描述蛋白质亚基如何产生生物特性和生物功能的受体。 此外，如果需要，推断的分子表示可以用作生成模型 诸如抗原靶标之类的特性被指定，并且可以生成新的蛋白质。