2021BBSRC-NSF/BIO UniPlex - Genome-Wide Protein Complex Prediction and Validation

蛋白质既是构建细胞结构的重要组成部分，也是使细胞发挥功能的工具。然而，蛋白质并不是孤立地运作的，通常会形成分子机器，在分子机器中，几种蛋白质结合在一起，并与其他生物分子一起作为一个单一的实体，称为分子复合体。这提供了巨大的多功能性和调节能力，因为通过改变复合体的单一成分，其功能可以显著改变。蛋白质复合体通常也比孤立的蛋白质形成更稳定的结构，它们的形成创造了新的活性部位，因为来自不同分子的蛋白质链在近距离聚集在一起。因此，了解复合体的组成并将它们作为离散的功能实体进行研究，以便真正了解细胞过程是如何工作的，这是至关重要的。Complex门户网站(www.ebi.ac.uk/Complex PORTAL)是一个百科全书式的数据库，它通过人工整理整理和汇总科学文献中有关稳定的、具有已知功能的大分子络合物的信息。复杂门户(CP)馆长现在已经完成了来自面包酵母(酿酒酵母)和肠道细菌大肠杆菌的所有稳定分子复合体的初稿，这两种模式生物都被广泛用于基本生物过程的研究。该项目的下一个大目标是对所有的人类复合体(人类复合体)进行完整的注释。CP收到了研究界的多项请求，要求显著加快人类数据的注释速度，但手动整理很费力，而且只能部分满足需求。文献中有多种类型的数据可以表明不同的蛋白质构成同一复合体的一部分：免疫共沉淀研究，通过选定的蛋白质诱饵将结合在一起的蛋白质纯化出来；邻近数据集，使用细菌酶标记在细胞中非常接近的蛋白质；或共分离实验，其中细胞被打破，共同纯化的蛋白质被鉴定出来。有一些公共数据库汇编了关于单个蛋白质如何相互结合(完整)的数据；这些蛋白质参与的过程被称为通路(Reactome)；或者捕捉两个或更多结合在一起的蛋白质的三维结构(WWPDB)。我们建议通过使用机器学习算法来扩大复杂门户的范围和相关性，这些算法可以从使用上述技术生成的大型数据集中识别最有可能代表细胞中存在的功能复合体的蛋白质组。这些对络合物的预测将与其他实验数据进行验证，并在可能的情况下与文献证据进行验证。我们还将使用对不同细胞类型、组织和条件下蛋白质表达的大规模研究来验证预测的复合体，并区分在不同条件下形成的复合体的变体。预计将高度可信地存在的复合体将通过复杂门户网站提供，该网站被适当地确定为计算推断数据，在那里它们将指导复杂门户网站馆长的工作，并极大地增加研究人员可用作参考实体的复合体的数量。我们将向这些条目添加来自其他资源(如Reactome和PDB)的进一步信息，并将已知影响蛋白质相互作用强度和稳定性的氨基酸的变化映射到完整数据库中的复杂结合界面。这项工作将有助于加快我们对络合物作为生物过程中必不可少的分子机器的理解，并支持基础和应用研究。

Proteins are essential components that both build cellular structures and work as the tools that make the cell function. However, proteins do not operate in isolation and often form molecular machines in which several proteins bind together and with other biomolecules to act as a single entity called a molecular complex. This provides tremendous versatility and regulatory capacities, since by changing a single component of the complex, its function can be dramatically altered. Protein complexes often also form more stable structures than isolated proteins, and their formation creates new active sites as protein chains from different molecules assemble in close proximity. It is therefore of crucial importance to know the composition of complexes and study them as discrete functional entities in order to truly understand how cellular processes work. The Complex Portal (www.ebi.ac.uk/complexportal) is an encyclopaedic database that collates and summarizes information on stable, macromolecular complexes of known function from the scientific literature through manual curation. Complex Portal (CP) curators have now completed a first draft of all the stable molecular complexes from baker's yeast (Saccharomyces cerevisiae) and the gut bacteria Escherichia coli, both model organisms widely used for the study of basic biological processes. The next big goal for the project is the complete annotation of the all human complexes (the human complexome). The CP has had multiple requests from the research community to significantly speed up the annotation of human data, but manual curation is laborious, and can only partially meet demand. There are multiple types of data available in the literature that can indicate that different proteins form part of the same complex: co-immunoprecipitation studies, where proteins that bind together are purified out via a selected protein bait; proximity data sets, which tag proteins which are very close together in a cell using a bacterial enzyme, or co-fractionation experiments, where cells are broken apart and proteins that co-purify together are identified. There are public databases that compile data about how individual proteins bind each other (IntAct); the processes in which such proteins take part, called pathways (Reactome); or capture the 3D structure of two or more proteins bound together (wwPDB). We propose to extend the scope and relevance of the Complex Portal by using machine learning algorithms that can identify groups of proteins that are most likely to represent functional complexes which exist in the cell from large datasets generated using the techniques described above. These predictions of complexes will be validated against other experimental data and, where possible, also against literature evidence. We will also use large scale studies of protein expression in different cell types, tissues, and conditions to validate the predicted complexes and to differentiate between variants of complexes formed in different conditions. Complexes predicted to exist at high confidence will be made available through the Complex Portal website, properly identified as computationally inferred data, where they will both guide the work of Complex Portal curators and dramatically increase the amount of complexes available for researchers as reference entities. We will add further information from other resources such as Reactome and PDB to these entries and map changes to amino acids which are known to affect protein interaction strength and stability to complex binding interfaces from the IntAct database. This work will help accelerate our understanding of complexes as the molecular machines essential to biological processes and support basic and applied research.