Protein ScienceBiophysicsPeer-reviewed journal article
Human Cells for Human Proteins: Isotope Labeling in Mammalian Cells for Functional NMR Studies of Disease-Relevant Proteins
Philip RößleriD, M. RuckstuhliD, Arnelle Löbbert, Timo T. StühlingeriD, Lucia Rosanna FranchiniiD, Ching‐Ju TsaiiD, Roman J. LichteneckeriD, Binesh Shrestha, Simon RüdisseriD, Robert KonratiD, Gebhard F. X. SchertleriD, Alvar D. GossertiD
Abstract
In biological and biomedical research the focus progressively moves towards difficult human proteins, which often can only be expressed in higher eukaryotic cells. Nuclear magnetic resonance (NMR) could contribute significantly to the understanding of important proteins as it is one of the most information-rich methods: it allows studying structure, function and dynamics of biomolecules and, importantly, their interactions with natural ligands or drugs. However, to exploit the full potential of NMR, proteins must be isotope labeled. Although expression protocols in e.g. HEK293 cells are often established, isotope labeling is difficult and very expensive. To resolve this disparity, we have developed a comprehensive suite of protocols for isotope labeling in HEK293 cells. We demonstrate uniform 15N and 13C labeling, as well as specific labeling, with special focus on methyl bearing amino acids, including the popular ILV 13C-methyl labeling pattern. Labeling is achieved with a simple laboratory setup and affordable labeling media. These are based on either labeled amino acids, their precursors or amino extracts from microorganisms, like yeast, algae or bacteria. This enables NMR studies of important, but difficult to produce proteins, like receptors. We therefore expect that these new methods make many highly important proteins accessible to NMR studies and allow exploiting the high information content of this method for accelerating biological and pharmaceutical research.
- organism: human
- assay: proteomics
- research focus: protein folding & dynamics
- approach: experimental assay
- Citations
- 11
- FWCI
- 6.39
- Field percentile
- top 3%
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