Abstract An essential requirement for the use of fluorescent dyes in biomedicine, molecular biology, biochemistry, biophysics and optical imaging is their (covalent) attachment to biomolecules. There is, however, no systematic and automated approach for the selection of suitable labeling sites in macromolecules, which is particular problematic for proteins. Here, we present a general and quantitative strategy to identify optimal residues for protein labeling using a naïve Bayes classifier. Based on a literature search and bioinformatics analysis of &gt;100 proteins with ∼400 successfully labeled residues, we identified four parameters, which we combined into a labeling score to rank residues for their suitability as a label-site. The utility of our approach for the systematic selection of single residues and residue pairs for FRET experiments is supported by data from the literature and by new experiments on different proteins. To make the method available to a large community of researchers, we developed a python package called “labelizer”, that performs an analysis of a pdb-structure (or structural models), label score calculation, and FRET assay scoring. We further provide a webserver ( https://labelizer.bio.lmu.de/ ) to conveniently apply our approach and to build up a central open-access database of (non-)successfully labeled protein residues to continuously improve and refine the labelizer approach.

Labelizer: systematic selection of protein residues for covalent fluorophore labeling