Learning a molecular shape space for the adaptive immune system

项目摘要 适应性免疫系统由高度多样化的B和T细胞受体组成，其可以识别并 中和多种病原体免疫识别依赖于分子间的相互作用， 免疫受体和病原体，这反过来又由它们的3D结构的互补性决定， 氨基酸组成，即，它们的形状免疫形状空间以前曾作为 这种分子识别的抽象，以解释免疫库是如何组织起来对抗不同的 病原体然而，免疫受体序列、形状和特异性之间的关系是非常复杂的。 在实践中难以量化。我们建议利用机器学习的最新进展和 分子数据来推断有效的形状空间，以蛋白质相互作用的生物物理学为基础。关键是要 找到一般蛋白质的代表，特别是免疫受体，反映了相关的 生物物理特性决定蛋白质受体的稳定性、功能和与病原体的相互作用。 表示学习是机器学习中的一种强大技术，它使用大量数据来 推断简化表示。由于蛋白质功能与3D结构密切相关，我们将开发新的 使用蛋白质结构的原子坐标作为输入的机器学习方法， 尊重数据中的物理对称性的变换，学习反映生物物理的表示， 蛋白质的性质和蛋白质-蛋白质相互作用。我们认为，我们方法的一个关键创新是， 分析3D蛋白质结构中的氨基酸邻域。这些街区的分布 揭示了它们在表面、本体和功能重要区域（如催化位点）的差异。 所学习的蛋白质表示将使我们能够表征氨基酸的特定组成 邻域是蛋白质结构和蛋白质功能的构建块。我们将把 将蛋白质宇宙表示为免疫受体以学习免疫形状空间。精益免疫 形状空间将使我们能够解决亲和力和特异性是如何编码的免疫受体在不同的 细胞类型。我们将研究免疫受体的模块化结构如何与单独的病原体接合， 框架区，使受体多样化，并针对多种病原体，而不损害 他们的稳定性。我们将使用形状识别的互补方面来预测抗原的目标， 免疫受体，并通过合作，我们将实验验证我们的预测。 我们的方法为蛋白质和免疫系统的可解释计算模型开辟了一条新的道路。 描述生物学特性和生物学功能如何从蛋白质亚基产生的受体。 此外，在需要时，推断的分子表示可以用作生成模型 例如抗原靶的特性被指定，并且可以产生新的蛋白质。

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.