Title |
Determination of ligand binding modes in weak protein–ligand complexes using sparse NMR data
|
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Published in |
Journal of Biomolecular NMR, October 2016
|
DOI | 10.1007/s10858-016-0067-4 |
Pubmed ID | |
Authors |
Biswaranjan Mohanty, Martin L. Williams, Bradley C. Doak, Mansha Vazirani, Olga Ilyichova, Geqing Wang, Wolfgang Bermel, Jamie S. Simpson, David K. Chalmers, Glenn F. King, Mehdi Mobli, Martin J. Scanlon |
Abstract |
We describe a general approach to determine the binding pose of small molecules in weakly bound protein-ligand complexes by deriving distance constraints between the ligand and methyl groups from all methyl-containing residues of the protein. We demonstrate that using a single sample, which can be prepared without the use of expensive precursors, it is possible to generate high-resolution data rapidly and obtain the resonance assignments of Ile, Leu, Val, Ala and Thr methyl groups using triple resonance scalar correlation data. The same sample may be used to obtain Met (ε)CH3 assignments using NOESY-based methods, although the superior sensitivity of NOESY using [U-(13)C,(15)N]-labeled protein makes the use of this second sample more efficient. We describe a structural model for a weakly binding ligand bound to its target protein, DsbA, derived from intermolecular methyl-to-ligand nuclear Overhauser enhancements, and demonstrate that the ability to assign all methyl resonances in the spectrum is essential to derive an accurate model of the structure. Once the methyl assignments have been obtained, this approach provides a rapid means to generate structural models for weakly bound protein-ligand complexes. Such weak complexes are often found at the beginning of programs of fragment based drug design and can be challenging to characterize using X-ray crystallography. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 45 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 15 | 33% |
Student > Bachelor | 6 | 13% |
Student > Ph. D. Student | 5 | 11% |
Professor > Associate Professor | 4 | 9% |
Professor | 2 | 4% |
Other | 5 | 11% |
Unknown | 8 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Chemistry | 17 | 38% |
Biochemistry, Genetics and Molecular Biology | 12 | 27% |
Pharmacology, Toxicology and Pharmaceutical Science | 3 | 7% |
Agricultural and Biological Sciences | 3 | 7% |
Engineering | 1 | 2% |
Other | 0 | 0% |
Unknown | 9 | 20% |