Automatic Assignment of Methyl-NMR Spectra of Supramolecular Machines Using Graph Theory

Pritišanac, I.; Degiacomi, M.T.; Alderson, T.R.; Carneiro, M.G.; Ab, E.; Siegal, Gregg; Baldwin, Andrew J.

Abstract

Methyl groups are powerful probes for the analysis of structure, dynamics and function of supramolecular assemblies, using both solution- and solid-state NMR. Widespread application of the methodology has been limited due to the challenges associated with assigning spectral resonances to specific locations within a biomolecule. Here, we present Methyl Assignment by Graph Matching (MAGMA), for the automatic assignment of methyl resonances. A graph matching protocol examines all possibilities for each resonance in order to determine an exact assignment that includes a complete description of any ambiguity. MAGMA gives 100% accuracy in confident assignments when tested against both synthetic data, and 9 cross-validated examples using both solution- and solid-state NMR data. We show that this remarkable accuracy enables a user to distinguish between alternative protein structures. In a drug discovery application on HSP90, we show the method can rapidly and efficiently distinguish between possible ligand binding modes. By providing an exact and robust solution to methyl resonance assignment, MAGMA can facilitate significantly accelerated studies of supramolecular machines using methyl-based NMR spectroscopy.

Citation

Pritišanac, I., Degiacomi, M., Alderson, T., Carneiro, M., Ab, E., Siegal, G., & Baldwin, A. J. (2017). Automatic Assignment of Methyl-NMR Spectra of Supramolecular Machines Using Graph Theory. Journal of the American Chemical Society, 139(28), 9523-9533. https://doi.org/10.1021/jacs.6b11358

• Journal Article Type: Article
• Acceptance Date: Nov 1, 2016
• Online Publication Date: Jul 10, 2017
• Publication Date: 2017-07
• Deposit Date: Jul 26, 2017
• Journal: Journal of the American Chemical Society
• Print ISSN: 0002-7863
• Electronic ISSN: 1520-5126
• Publisher: American Chemical Society
• Peer Reviewed: Peer Reviewed
• Volume: 139
• Issue: 28
• Pages: 9523-9533
• DOI: https://doi.org/10.1021/jacs.6b11358
• Public URL: https://durham-repository.worktribe.com/output/1372481