Abstract
Selective methyl labeling is an extremely powerful approach to study the structure, dynamics and function of biomolecules by NMR. Despite spectacular progress in the field, such studies remain rather limited in number. One of the main obstacles remains the assignment of the methyl resonances, which is labor intensive and error prone. Typically, NOESY crosspeak patterns are manually correlated to the available crystal structure or an in silico template model of the protein. Here, we propose methyl assignment by graphing inference construct, an exhaustive search algorithm with no peak network definition requirement. In order to overcome the combinatorial problem, the exhaustive search is performed locally, i.e. for a small number of methyls connected through-space according to experimental 3D methyl NOESY data. The local network approach drastically reduces the search space. Only the best local assignments are combined to provide the final output. Assignments that match the data with comparable scores are made available to the user for cross-validation by additional experiments such as methyl-amide NOEs. Several NMR datasets for proteins in the 25–50 kDa range were used during development and for performance evaluation against the manually assigned data. We show that the algorithm is robust, reliable and greatly speeds up the methyl assignment task.
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Acknowledgements
We thank Dr. Youlin Xia, Dr. Jinfa Ying and Nikolaus Sgourakis for help with 4D data implementation and processing. Financial support by the National Institute of Health Grants AI094623 and GM122462 to C.G.K..
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Monneau, Y.R., Rossi, P., Bhaumik, A. et al. Automatic methyl assignment in large proteins by the MAGIC algorithm. J Biomol NMR 69, 215–227 (2017). https://doi.org/10.1007/s10858-017-0149-y
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DOI: https://doi.org/10.1007/s10858-017-0149-y