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

蛋白质是构建细胞结构的基本成分，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，经常形成分子机器，其中几个蛋白质结合在一起，并与其他生物分子结合，作为一个单一的实体，称为分子复合物。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个组成部分，其功能可以显着改变。蛋白质复合物通常比分离的蛋白质形成更稳定的结构，并且当来自不同分子的蛋白质链紧密聚集时，它们的形成会产生新的活性位点。因此，了解复合物的组成并将其作为离散的功能实体进行研究是至关重要的，以便真正了解细胞过程是如何工作的。该项目是伊利诺伊大学芝加哥分校和英国剑桥欧洲生物信息学研究所（EMBL-EBI）的国际合作项目。Complex Portal （www.ebi.ac.uk/complexportal）是一个百科全书式的数据库，通过手工整理和总结科学文献中已知功能的稳定大分子复合物的信息。该项目将通过使用机器学习算法来扩展Complex Portal的范围和相关性，该算法可以识别最可能代表细胞中存在的功能复合物的蛋白质组。这些复合物的预测将根据其他实验数据进行验证，如果可能的话，也将根据文献证据进行验证。我们还将在不同的细胞类型、组织和条件下进行大规模的蛋白质表达研究，以验证预测的复合物，并区分在不同条件下形成的复合物的变体。预测存在的高可信度的综合体将通过复杂门户网站提供，适当地确定为计算推断的数据，在那里它们将指导复杂门户管理员的工作，并大大增加可供研究人员作为参考实体的综合体数量。来自Reactome和PDB等其他资源的附加信息将被注释到这些条目中，并映射到已知会影响蛋白质相互作用强度和稳定性的氨基酸。这项工作将有助于加速复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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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. This project is an international collaboration between the University of Illinois at Chicago and the European Bioinformatics Institute (EMBL-EBI), Cambridge, UK. 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. This project will 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. 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. Additional information from other resources such as Reactome and PDB will be annotated to these entries and changes mapped 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 understanding of complexes as the molecular machines essential to biological processes and support basic and applied research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.