Labelizer: systematic selection of protein residues for covalent fluorophore labeling
Abstract 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 id...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58602-y |
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| author | Christian Gebhardt Pascal Bawidamann Anna-Katharina Spring Robin Schenk Konstantin Schütze Gabriel G. Moya Muñoz Nicolas D. Wendler Douglas A. Griffith Jan Lipfert Thorben Cordes |
| author_facet | Christian Gebhardt Pascal Bawidamann Anna-Katharina Spring Robin Schenk Konstantin Schütze Gabriel G. Moya Muñoz Nicolas D. Wendler Douglas A. Griffith Jan Lipfert Thorben Cordes |
| author_sort | Christian Gebhardt |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-bde863d6963f460ab45e7674827a457e |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-bde863d6963f460ab45e7674827a457e2025-08-20T02:10:50ZengNature PortfolioNature Communications2041-17232025-05-0116111610.1038/s41467-025-58602-yLabelizer: systematic selection of protein residues for covalent fluorophore labelingChristian Gebhardt0Pascal Bawidamann1Anna-Katharina Spring2Robin Schenk3Konstantin Schütze4Gabriel G. Moya Muñoz5Nicolas D. Wendler6Douglas A. Griffith7Jan Lipfert8Thorben Cordes9Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Großhadernerstr. 2-4Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Großhadernerstr. 2-4Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Großhadernerstr. 2-4Klinikum rechts der Isar, Technische Universität München, Klinik und Poliklinik für Innere Medizin IIPhysical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Großhadernerstr. 2-4Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Großhadernerstr. 2-4Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Großhadernerstr. 2-4Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Großhadernerstr. 2-4Department of Physics and Center for NanoScience, Ludwig-Maximilians-Universität München, Amalienstr. 54Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Großhadernerstr. 2-4Abstract 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.https://doi.org/10.1038/s41467-025-58602-y |
| spellingShingle | Christian Gebhardt Pascal Bawidamann Anna-Katharina Spring Robin Schenk Konstantin Schütze Gabriel G. Moya Muñoz Nicolas D. Wendler Douglas A. Griffith Jan Lipfert Thorben Cordes Labelizer: systematic selection of protein residues for covalent fluorophore labeling Nature Communications |
| title | Labelizer: systematic selection of protein residues for covalent fluorophore labeling |
| title_full | Labelizer: systematic selection of protein residues for covalent fluorophore labeling |
| title_fullStr | Labelizer: systematic selection of protein residues for covalent fluorophore labeling |
| title_full_unstemmed | Labelizer: systematic selection of protein residues for covalent fluorophore labeling |
| title_short | Labelizer: systematic selection of protein residues for covalent fluorophore labeling |
| title_sort | labelizer systematic selection of protein residues for covalent fluorophore labeling |
| url | https://doi.org/10.1038/s41467-025-58602-y |
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