Protein structure determination by combining sparse NMR data with evolutionary couplings

By Yuefeng Tang, Yuanpeng Janet Huang, Thomas A. Hopf, Christopher Sander, Debora Marks, Gaetano T. Montelione. 2015

75 citations recorded by the source corpus.

Accurate determination of protein structure by NMR spectroscopy is challenging for larger proteins, for which experimental data are often incomplete and ambiguous. Evolutionary sequence information together with advances in maximum entropy statistical methods provide a rich complementary source of structural constraints. We have developed a hybrid approach (evolutionary coupling–NMR spectroscopy; EC-NMR) combining sparse NMR data with evolutionary residue-residue couplings and demonstrate accurate structure determination for several proteins 6−41 kDa in size.

Subfields: Molecular Biology

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Citations in the corpus

Integrative protein modeling in RosettaNMR from sparse paramagnetic restraints (2019, methodology): “Evolutionary coupling-NMR, developed by Tang et al. (Tang et al., 2015), incorporates evolutionary contacts during and after NMR data interpretation and NOE assignment.”
CS-ROSETTA (2018, methodology): “We found that the lowest energy models generated using Abrelax and RASREC exhibit C α RMSDs of 2.4 and 1.6 Å from the native structure (PDB ID 2N47; Tang et al., 2015), respectively (Fig.”
Structure of the Bacterial Cytoskeleton Protein Bactofilin by NMR Chemical Shifts and Sequence Variation. (2016, methodology): “The idea is similar to a very recently described approach that also relies on NMR-derived distance information (44).”
Chemical shift-based methods in NMR structure determination (2018, methodology): “For systems of larger size and dynamic complexity, automated methods benefit from advances in selective isotope labeling schemes [244], the use of probes with favorable relaxation properties, such as methyl groups [245], and utilization of sparse restraints [94,104,128].”
The AUDANA algorithm for automated protein 3D structure determination from NMR NOE data (2016, methodology): “Yet another bioinformatics approach combines sparse NMR data on a protein with distance restraints derived from evolutionary residue–residue couplings (Tang et al. 2015).”
Origins of coevolution between residues distant in protein 3D structures (2017, background): “There has been considerable recent success in using direct couplings to infer residue–residue contacts for protein structure prediction (6–9), protein–protein complex prediction (10–12), identification of specific interaction partners among two sets of paralogous proteins (13), resolution of ambigui”