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

蛋白质是构建细胞结构和作为使细胞发挥功能的工具的重要组成部分。然而，蛋白质并不是孤立运作的，通常形成分子机器，其中几种蛋白质结合在一起，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，因为通过改变复合物的单个组分，其功能可以显著改变。蛋白质复合物通常也比孤立的蛋白质形成更稳定的结构，并且它们的形成产生了新的活性位点，因为来自不同分子的蛋白质链紧密组装。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，以真正了解细胞过程如何工作至关重要。该项目是位于芝加哥的伊利诺伊大学和位于英国剑桥的欧洲生物信息学研究所（EMBL-EBI）之间的国际合作项目。Complex Portal（www.ebi.ac.uk/complexportal）是一个数据库，通过人工管理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物的蛋白质组。这些复合物的预测将验证对其他实验数据，并在可能的情况下，也对文献证据。我们还将使用不同细胞类型，组织和条件下的蛋白质表达的大规模研究来验证预测的复合物，并区分不同条件下形成的复合物的变体。预测以高置信度存在的复合体将通过Complex Portal网站提供，适当地识别为计算推断的数据，在那里它们将指导Complex Portal策展人的工作，并显着增加可供研究人员作为参考实体的复合体的数量。来自其他资源（如Reactome和PDB）的额外信息将注释到这些条目，并将变化映射到已知影响蛋白质相互作用强度和稳定性的氨基酸，以与来自IntAct数据库的复杂结合界面结合。这项工作将有助于加速理解复合物作为生物过程中必不可少的分子机器，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.