Literature DB >> 34914892

Progress toward automated methyl assignments for methyl-TROSY applications.

Mary C Clay1, Tamjeed Saleh1, Samuel Kamatham1, Paolo Rossi2, Charalampos G Kalodimos3.   

Abstract

Methyl-TROSY spectroscopy has extended the reach of solution-state NMR to supra-molecular machineries over 100 kDa in size. Methyl groups are ideal probes for studying structure, dynamics, and protein-protein interactions in quasi-physiological conditions with atomic resolution. Successful implementation of the methodology requires accurate methyl chemical shift assignment, and the task still poses a significant challenge in the field. In this work, we outline the current state of technology for methyl labeling, data collection, data analysis, and nuclear Overhauser effect (NOE)-based automated methyl assignment approaches. We present MAGIC-Act and MAGIC-View, two Python extensions developed as part of the popular NMRFAM-Sparky package, and MAGIC-Net a standalone structure-based network analysis program. MAGIC-Act conducts statistically driven amino acid typing, Leu/Val pairing guided by 3D HMBC-HMQC, and NOESY cross-peak symmetry checking. MAGIC-Net provides model-based NOE statistics to aid in selection of a methyl labeling scheme. The programs provide a versatile, semi-automated framework for rapid methyl assignment.
Copyright © 2021 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  MAGIC Algorithm; NMR; highly deuterated large proteins; methyl assignment; methyl-TROSY

Mesh:

Substances:

Year:  2021        PMID: 34914892      PMCID: PMC8741727          DOI: 10.1016/j.str.2021.11.009

Source DB:  PubMed          Journal:  Structure        ISSN: 0969-2126            Impact factor:   5.006


  53 in total

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Authors:  Yoan R Monneau; Yojiro Ishida; Paolo Rossi; Tomohide Saio; Shiou-Ru Tzeng; Masayori Inouye; Charalampos G Kalodimos
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8.  MAP-XSII: an improved program for the automatic assignment of methyl resonances in large proteins.

Authors:  Yingqi Xu; Stephen Matthews
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Journal:  Nat Commun       Date:  2019-10-29       Impact factor: 14.919

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