Identification of permissive amber suppression sites for efficient non-canonical amino acid incorporation in mammalian cells

Bartoschek, Michael D.; Ugur, Enes; Nguyen, Tuan-Anh; Rodschinka, Geraldine; Wierer, Michael; Lang, Kathrin und Bultmann, Sebastian (2021): Identification of permissive amber suppression sites for efficient non-canonical amino acid incorporation in mammalian cells. In: Nucleic Acids Research, Bd. 49, Nr. 11, e62

Abstract

The genetic code of mammalian cells can be expanded to allow the incorporation of non-canonical amino acids (ncAAs) by suppressing in-frame amber stop codons (UAG) with an orthogonal pyrrolysyl-tRNA synthetase (PyIRS)/tRNA(CUP)(Pyl )(PylT) pair. However, the feasibility of this approach is substantially hampered by unpredictable variations in incorporation efficiencies at different stop codon positions within target proteins. Here, we apply a proteomics-based approach to quantify ncAA incorporation rates at hundreds of endogenous amber stop codons in mammalian cells. With these data, we compute iPASS (Identification of Permissive Amber Sites for Suppression;available at www.bultmannlab.eu/tools/iPASS), a linear regression model to predict relative ncAA incorporation efficiencies depending on the surrounding sequence context. To verify iPASS, we develop a dual-fluorescence reporter for high-throughput flow-cytometry analysis that reproducibly yields context-specific ncAA incorporation efficiencies. We show that nucleotides up- and downstream of UAG synergistically influence ncAA incorporation efficiency independent of cell line and ncAA identity. Additionally, we demonstrate iPASS-guided optimization of ncAA incorporation rates by synonymous exchange of codons flanking the amber stop codon. This combination of in silico analysis followed by validation in living mammalian cells substantially simplifies identification as well as adaptation of sites within a target protein to confer high ncAA incorporation rates. [GRAPHICS] .

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Biologie
• Themengebiete: 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften; Biologie
• ISSN: 0305-1048
• Sprache: Englisch