Magically speed up your methyl assignment!!!

What is it about?

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 meth- yls 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 together to provide the final output. Assignments that match the data with comparable scores are made avail- able to the user for cross-validation by additional experi- ments such as methyl-amide NOEs. Several NMR datasets for proteins in the 25–50 kDa range were used during devel- opment and for performance evaluation against the manually assigned data. We show that the algorithm is robust, reliable and and greatly speeds up the methyl assignment task.

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Why is it important?

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.

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