Towards a comprehensive multiscale 3D human interactome network

项目摘要/摘要 几乎所有的蛋白质都通过与其他蛋白质相互作用来发挥作用。平均而言，一种蛋白质与~5 目前人类相互作用组中的其他蛋白质伙伴。因此，准确地进行数据采集具有十分重要的意义。 确定每种相互作用的界面，以便了解每种蛋白质如何与不同的 合作伙伴执行不同的职能。在我们之前的自然生物技术研究中，我们实现了一个 蛋白质组尺度同源建模方法生成第一个三维人类结构相互作用组： 通过共晶结构在原子分辨率下确定了该网络中每个相互作用的界面 和同调模型。使用我们的3D交互组，我们发现在1800个已知的疾病基因中 与两个或多个临床上明显的疾病有关，同一基因上的一对突变，但不同的 与不同伙伴的接口明显更有可能导致不同的疾病。 然而，只有4150个人类蛋白质相互作用具有共晶结构，2921个具有高质量 同调模型。~50,000个交互(占当前人类交互组的87%)不适用于当前 结构建模方法。在这里，我们建议开发一种大数据驱动的机器学习方法 整合生物物理化学、进化、结构和种群遗传特征，以确定相互作用- 整个人类互动组的特定接口。因为许多人无法使用几个关键功能 蛋白质和相互作用，我们提出了一种创新的方法，使用随机森林分类器的集合， 命名为集合蛋白质接口分类器(EPIC)，以解决这种大规模的非随机缺失数据 问题(目标1)。我们的大规模并行克隆-序列和整合蛋白质互作组的高通量 微扰屏蔽(INPOINT)管线！独一无二地使我们能够执行实时实验参数 优化(在第2年和第4年中，我们将克隆~1,500个突变并检查它们对~2,500个交互的影响 每年反复评估和提炼史诗；目标2)。最后，我们将构建一个全面的 所有已知的人类蛋白质-蛋白质相互作用的多尺度3D交互组：我们将收集/生成原子- 尽可能解析相互作用的结构模型(共晶体结构和同源模型)； 我们将准确地确定整个人类相互作用的特定界面残基和结构域 互动组。我们将部署一个互动的门户网站来传播我们的结果，并允许功能基因组 在我们的结构相互作用组的背景下进行推理(目标3)。 我们全面的多尺度3D人类互动组和随附的门户网站将大大减少 对大量蛋白质及其分子进行系统结构分析的进入壁垒 相互作用，并为基因组研究中的此类分析打开闸门。

PROJECT SUMMARY/ABSTRACT Almost all proteins function through interacting with other proteins. On average, a protein interacts with ~5 other protein partners in the current human interactome. Therefore, it is of great importance to accurately determine the interface of each interaction, in order to understand how each protein works with different partners to carry out different functions. In our previous Nature Biotechnology study, we implemented a proteome-scale homology modeling approach to generate the first 3D human structural interactome: the interface for each interaction in this network was determined at atomic resolution through co-crystal structures and homology models. Using our 3D interactome, we found that, among >1,800 known disease genes associated with two or more clinically distinctly disorders, pairs of mutations on the same gene but in different interfaces with different partners are significantly more likely to cause distinct diseases. However, only 4,150 human protein interactions have co-crystal structures and 2,921 have high-quality homology models. ~50,000 interactions (87% of the current human interactome) are not amenable to current structural modeling methods. Here, we propose to develop a big-data-driven machine-learning approach integrating biophysiochemical, evolutionary, structural, and population genetic features to identify interaction- specific interfaces for the whole human interactome. Because several key features are unavailable for many proteins and interactions, we propose an innovative approach to use an ensemble of random forest classifiers, named Ensemble Protein Interface Classifier (EPIC), to address this large-scale non-random missing data problem (Aim 1). The high throughput of our massively parallel Clone-seq and INtegrated PrOtein INteractome perTurbation screening (InPOINT) pipeline! uniquely enables us to perform real-time experimental parameter optimization (in Years 2-4 we will clone ~1,500 mutations and examine their impact on ~2,500 interactions every year to iteratively evaluate and refine EPIC; Aim 2). Finally, we will construct a comprehensive multiscale 3D interactome for all known human protein-protein interactions: we will collect/generate atomic- resolution structural models for interactions whenever possible (co-crystal structures and homology models); we will accurately determine interaction-specific interface residues and domains for the whole human interactome. We will deploy an interactive web portal to disseminate our results and allow functional genomic inference in the context of our structural interactome (Aim 3). Our comprehensive multiscale 3D human interactome and the accompanying web portal will greatly reduce the barrier-to-entry for performing systematic structural analysis on a large number of proteins and their interactions, and open the flood gates for such analyses in genomic studies.