Ligand-induced structural transitions combined with paramagnetic ions facilitate unambiguous NMR assignments of methyl groups in large proteins.
Lars MühlbergTuncay AlarcinThorben MaassRobert CreutznacherRichard KüchlerAlvaro MallagarayPublished in: Journal of biomolecular NMR (2022)
NMR spectroscopy allows the study of biomolecules in close-to-native conditions. Structural information can be inferred from the NMR spectra when an assignment is available. Protein assignment is usually a time-consuming task, being specially challenging in the case of large, supramolecular systems. Here, we present an extension of existing state-of-the-art strategies for methyl group assignment that partially overcomes signal overlapping and other difficulties associated to isolated methyl groups. Our approach exploits the ability of proteins to populate two or more conformational states, allowing for unique NOE restraints in each protein conformer. The method is compatible with automated assignment algorithms, granting assignments beyond the limits of a single protein state. The approach also benefits from long-range structural restraints obtained from metal-induced pseudocontact shifts (PCS) and paramagnetic relaxation enhancements (PREs). We illustrate the method with the complete assignment of the 199 methyl groups of a MIL proS V proS AT methyl-labeled sample of the UDP-glucose pyrophosphorylase enzyme from Leishmania major (LmUGP). Protozoan parasites of the genus Leishmania causes Leishmaniasis, a neglected disease affecting over 12 million people worldwide. LmUGP is responsible for the de novo biosynthesis of uridine diphosphate-glucose, a precursor in the biosynthesis of the dense surface glycocalyx involved in parasite survival and infectivity. NMR experiments with LmUGP and related enzymes have the potential to unravel new insights in the host resistance mechanisms used by Leishmania major. Our efforts will help in the development of selective and efficient drugs against Leishmania.
Keyphrases
- magnetic resonance
- high resolution
- machine learning
- diabetic rats
- protein protein
- deep learning
- solid state
- drug induced
- amino acid
- binding protein
- single molecule
- high throughput
- blood pressure
- quantum dots
- blood glucose
- healthcare
- quality improvement
- molecular dynamics simulations
- small molecule
- adipose tissue
- insulin resistance
- single cell
- human health
- liquid chromatography
- simultaneous determination