Progress toward automated methyl assignments for methyl-TROSY applications
Open accessAbstract
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.
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Methyl side-chain NMR assignments can be obtained using only methyl-methyl NOEs from 3D NMR experiments and atomic coordinates of a structure or AF2 model as input with software such as MAUS, MAGIC, and methylFLYA.
“Here, NMR assignments for methyl side-chain groups can be obtained using only methyl-methyl NOEs obtained from 3D NMR experiments and the atomic coordinates of a structure (or AF2 predicted structure) as input with software such as MAUS, MAGIC, and methylFLYA91,93,94.”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [94]
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- Authors
- Clay, M. C., Saleh, T., Kamatham, S., Rossi, P., & Kalodimos, C. G.
- Affiliations
- St. Jude Children's Research Hospital (US)
- Corresponding author
- Rossi, Paolo
- Kalodimos, Charalampos G.
- Venue
- Structure
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 30
- Issue
- 1
- Pages
- 69-79.e2
- ISSN
- 0969-2126 (ISSN-L)
- PMC
- PMC8741727
- OpenAlex
- W4200568296
- Cited by
- 9
- References
- 54
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 2 · 2023: 3 · 2022: 1
- FWCI
- 0.55
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 371 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Structure (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- American Lebanese Syrian Associated Charities
- National Institutes of Health (R35 GM122462)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced NMR Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Spectroscopy › Molecular spectroscopy and chirality
- Keywords
- nuclear Overhauser effect, protein-protein interaction, supramolecular self-assembly
- MeSH terms
- Algorithms, Amino Acids, Humans, Methylation, Models, Molecular, Protein Binding, Proteins, Nuclear Magnetic Resonance, Biomolecular
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