Petrov Petar B, Awoniyi Luqman O, Šuštar Vid, Balci M Özge, Mattila Pieta K
MediCity Research Laboratories, Institute of Biomedicine, University of Turku, 20014 Turku, Finland.
Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.
Int J Mol Sci. 2022 Mar 20;23(6):3351. doi: 10.3390/ijms23063351.
Protein-protein interactions govern cellular processes via complex regulatory networks, which are still far from being understood. Thus, identifying and understanding connections between proteins can significantly facilitate our comprehension of the mechanistic principles of protein functions. Coevolution between proteins is a sign of functional communication and, as such, provides a powerful approach to search for novel direct or indirect molecular partners. However, an evolutionary analysis of large arrays of proteins in silico is a highly time-consuming effort that has limited the usage of this method for protein pairs or small protein groups. Here, we developed AutoCoEv, a user-friendly, open source, computational pipeline for the search of coevolution between a large number of proteins. By driving 15 individual programs, culminating in CAPS2 as the software for detecting coevolution, AutoCoEv achieves a seamless automation and parallelization of the workflow. Importantly, we provide a patch to the CAPS2 source code to strengthen its statistical output, allowing for multiple comparison corrections and an enhanced analysis of the results. We apply the pipeline to inspect coevolution among 324 proteins identified to be located at the vicinity of the lipid rafts of B lymphocytes. We successfully detected multiple coevolutionary relations between the proteins, predicting many novel partners and previously unidentified clusters of functionally related molecules. We conclude that AutoCoEv, can be used to predict functional interactions from large datasets in a time- and cost-efficient manner.
蛋白质-蛋白质相互作用通过复杂的调控网络来控制细胞过程，而这些网络仍远未被完全理解。因此，识别和理解蛋白质之间的联系能够显著促进我们对蛋白质功能机制原理的理解。蛋白质之间的共同进化是功能交流的一种标志，因此提供了一种强大的方法来寻找新的直接或间接分子伴侣。然而，在计算机上对大量蛋白质进行进化分析是一项极其耗时的工作，这限制了该方法在蛋白质对或小蛋白质组中的应用。在此，我们开发了AutoCoEv，这是一种用户友好、开源的计算流程，用于搜索大量蛋白质之间的共同进化。通过驱动15个独立程序，最终以CAPS2作为检测共同进化的软件，AutoCoEv实现了工作流程的无缝自动化和并行化。重要的是，我们为CAPS2源代码提供了一个补丁，以加强其统计输出，允许进行多重比较校正并增强对结果的分析。我们应用该流程来检查324种被确定位于B淋巴细胞脂筏附近的蛋白质之间的共同进化。我们成功检测到了这些蛋白质之间的多种共同进化关系，预测了许多新的伴侣以及先前未识别的功能相关分子簇。我们得出结论，AutoCoEv可用于以高效省时且经济的方式从大型数据集中预测功能相互作用。
