Hernandez Altair C, Ortiz Sebastian, Betancur Laura I, Dojčilović Radovan, Picco Andrea, Kaksonen Marko, Oliva Baldo, Gallego Oriol
Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005, Catalonia, Spain.
Department of Biochemistry, University of Geneva, 1205 Genève, Switzerland.
NAR Genom Bioinform. 2024 Mar 12;6(1):lqae027. doi: 10.1093/nargab/lqae027. eCollection 2024 Mar.
Structural knowledge of protein assemblies in their physiological environment is paramount to understand cellular functions at the molecular level. Protein interactions from Imaging Complexes after Translocation (PICT) is a live-cell imaging technique for the structural characterization of macromolecular assemblies in living cells. PICT relies on the measurement of the separation between labelled molecules using fluorescence microscopy and cell engineering. Unfortunately, the required computational tools to extract molecular distances involve a variety of sophisticated software programs that challenge reproducibility and limit their implementation to highly specialized researchers. Here we introduce PyF2F, a Python-based software that provides a workflow for measuring molecular distances from PICT data, with minimal user programming expertise. We used a published dataset to validate PyF2F's performance.
了解蛋白质组装体在其生理环境中的结构知识对于在分子水平上理解细胞功能至关重要。易位后成像复合物的蛋白质相互作用(PICT)是一种用于对活细胞中的大分子组装体进行结构表征的活细胞成像技术。PICT依赖于使用荧光显微镜和细胞工程来测量标记分子之间的距离。不幸的是,提取分子距离所需的计算工具涉及各种复杂的软件程序,这对可重复性提出了挑战,并将其应用限制在高度专业化的研究人员手中。在这里,我们介绍了PyF2F,这是一种基于Python的软件,它提供了一个工作流程,用于从PICT数据中测量分子距离,所需的用户编程专业知识最少。我们使用一个已发表的数据集来验证PyF2F的性能。