PyF2F: a robust and simplified fluorophore-to-fluorophore distance measurement tool for Protein interactions from Imaging Complexes after Translocation experiments.

IF 4 Q1 GENETICS & HEREDITY NAR Genomics and Bioinformatics Pub Date : 2024-03-12 eCollection Date: 2024-03-01 DOI:10.1093/nargab/lqae027
Altair C Hernandez, Sebastian Ortiz, Laura I Betancur, Radovan Dojčilović, Andrea Picco, Marko Kaksonen, Baldo Oliva, Oriol Gallego
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Abstract

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.

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PyF2F:一种稳健、简化的荧光团对荧光团距离测量工具,用于从转位实验后的成像复合物中测量蛋白质相互作用。
要从分子水平了解细胞功能,就必须了解蛋白质在生理环境中的结构。转位后复合物成像(PICT)中的蛋白质相互作用是一种活细胞成像技术,用于描述活细胞中大分子组装的结构特征。PICT 依靠荧光显微镜和细胞工程测量标记分子之间的分离。遗憾的是,提取分子间距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,并限制了高度专业化研究人员的实施。我们在此介绍 PyF2F,这是一款基于 Python 的软件,它提供了一个从 PICT 数据中测量分子距离的工作流程,用户只需具备最低限度的编程专业知识。我们使用一个已发表的数据集来验证 PyF2F 的性能。
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CiteScore
8.00
自引率
2.20%
发文量
95
审稿时长
15 weeks
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