Pokharel Yuba Raj, Saarela Jani, Szwajda Agnieszka, Rupp Christian, Rokka Anne, Lal Kumar Karna Shibendra, Teittinen Kaisa, Corthals Garry, Kallioniemi Olli, Wennerberg Krister, Aittokallio Tero, Westermarck Jukka
From the ‡Institute for Molecular Medicine Finland FIMM, University of Helsinki, PO Box 20, FIN-00014 Helsinki, Finland; §Centre for Biotechnology, ‖Faculty of Life Science and Biotechnology, South Asian University, New Delhi 110021, India;
From the ‡Institute for Molecular Medicine Finland FIMM, University of Helsinki, PO Box 20, FIN-00014 Helsinki, Finland;
Mol Cell Proteomics. 2015 Dec;14(12):3274-83. doi: 10.1074/mcp.M115.050773. Epub 2015 Oct 23.
High content protein interaction screens have revolutionized our understanding of protein complex assembly. However, one of the major challenges in translation of high content protein interaction data is identification of those interactions that are functionally relevant for a particular biological question. To address this challenge, we developed a relevance ranking platform (RRP), which consist of modular functional and bioinformatic filters to provide relevance rank among the interactome proteins. We demonstrate the versatility of RRP to enable a systematic prioritization of the most relevant interaction partners from high content data, highlighted by the analysis of cancer relevant protein interactions for oncoproteins Pin1 and PME-1. We validated the importance of selected interactions by demonstration of PTOV1 and CSKN2B as novel regulators of Pin1 target c-Jun phosphorylation and reveal previously unknown interacting proteins that may mediate PME-1 effects via PP2A-inhibition. The RRP framework is modular and can be modified to answer versatile research problems depending on the nature of the biological question under study. Based on comparison of RRP to other existing filtering tools, the presented data indicate that RRP offers added value especially for the analysis of interacting proteins for which there is no sufficient prior knowledge available. Finally, we encourage the use of RRP in combination with either SAINT or CRAPome computational tools for selecting the candidate interactors that fulfill the both important requirements, functional relevance, and high confidence interaction detection.
高内涵蛋白质相互作用筛选彻底改变了我们对蛋白质复合物组装的理解。然而，高内涵蛋白质相互作用数据转化过程中的一个主要挑战是识别那些与特定生物学问题功能相关的相互作用。为应对这一挑战，我们开发了一个相关性排序平台（RRP），它由模块化的功能和生物信息学过滤器组成，用于在相互作用组蛋白中提供相关性排名。我们展示了RRP的多功能性，能够从高内涵数据中系统地优先选择最相关的相互作用伙伴，对癌蛋白Pin1和PME - 1的癌症相关蛋白质相互作用分析突出了这一点。我们通过证明PTOV1和CSKN2B作为Pin1靶标c - Jun磷酸化的新型调节因子，验证了所选相互作用的重要性，并揭示了以前未知的相互作用蛋白，它们可能通过抑制PP2A介导PME - 1的作用。RRP框架是模块化的，可以根据所研究生物学问题的性质进行修改，以回答各种研究问题。基于RRP与其他现有过滤工具的比较，所呈现的数据表明RRP特别为分析缺乏足够先验知识的相互作用蛋白提供了附加价值。最后，我们鼓励将RRP与SAINT或CRAPome计算工具结合使用，以选择满足功能相关性和高置信度相互作用检测这两个重要要求的候选相互作用蛋白。
