Research Infrastructure: 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。该项目是伊利诺伊大学芝加哥分校和英国剑桥的欧洲生物信息学研究所(EMBL-EBI)之间的国际合作。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。该项目将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。来自其他资源如Reactome和PDB的额外信息将被注释到这些条目和映射到氨基酸的变化，这些氨基酸已知会影响蛋白质与完整数据库中复杂结合界面的相互作用强度和稳定性。这项工作将有助于促进对复合体作为生物过程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NSF的法定使命，并通过使用基金会的智力优势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.