Gebhardt Christian, Bawidamann Pascal, Spring Anna-Katharina, Schenk Robin, Schütze Konstantin, Moya Muñoz Gabriel G, Wendler Nicolas D, Griffith Douglas A, Lipfert Jan, Cordes Thorben
Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Großhadernerstr. 2-4, Planegg-Martinsried, Germany.
Biophysical Chemistry, Department of Chemistry and Chemical Biology, Technische Universität Dortmund, Dortmund, Germany.
Nat Commun. 2025 May 4;16(1):4147. doi: 10.1038/s41467-025-58602-y.
Attaching fluorescent dyes to biomolecules is essential for assays in biology, biochemistry, biophysics, biomedicine and imaging. A systematic approach for the selection of suitable labeling sites in macromolecules, particularly proteins, is missing. We present a quantitative strategy to identify such protein residues using a naïve Bayes classifier. Analysis of >100 proteins with ~400 successfully labeled residues allows to identify four parameters, which can rank residues via a single metric (the label score). The approach is tested and benchmarked by inspection of literature data and experiments on the expression level, degree of labelling, and success in FRET assays of different bacterial substrate binding proteins. With the paper, we provide a python package and webserver ( https://labelizer.bio.lmu.de/ ), that performs an analysis of a pdb-structure (or model), label score calculation, and FRET assay scoring. The approach can facilitate to build up a central open-access database to continuously refine the label-site selection in proteins.
将荧光染料附着于生物分子对于生物学、生物化学、生物物理学、生物医学及成像领域的检测至关重要。目前缺少一种针对大分子尤其是蛋白质中合适标记位点选择的系统方法。我们提出一种使用朴素贝叶斯分类器识别此类蛋白质残基的定量策略。对100多种蛋白质中约400个成功标记的残基进行分析，从而确定四个参数，这些参数可通过单一指标（标记分数）对残基进行排序。通过查阅文献数据以及对不同细菌底物结合蛋白的表达水平、标记程度和荧光共振能量转移检测成功率进行实验，对该方法进行了测试和基准评估。随本文一同提供了一个Python软件包和网络服务器（https://labelizer.bio.lmu.de/），可对pdb结构（或模型）进行分析、计算标记分数并对荧光共振能量转移检测进行评分。该方法有助于建立一个中央开放获取数据库，以不断完善蛋白质中标记位点的选择。
