An Integrative Approach to Determine 3D Protein Structures Using Sparse Paramagnetic NMR Data and Physical Modeling

Paramagnetic nuclear magnetic resonance (NMR) methods have emerged as powerful tools for structure determination of large, sparsely protonated proteins. However traditional applications face several challenges, including a need for large datasets to offset the sparsity of restraints, the difficulty...

Full description

Saved in:
Bibliographic Details
Published inFrontiers in molecular biosciences Vol. 8; p. 676268
Main Authors Gaalswyk, Kari, Liu, Zhihong, Vogel, Hans J, MacCallum, Justin L
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 12.08.2021
Subjects
calmodulin
integrative structural biology
modeling
Molecular Biosciences
NMR
paramagnetic relaxation enhancement
protein structure
protein structure
NMR
paramagnetic relaxation enhancement
modeling
integrative structural biology
calmodulin
Online AccessGet full text

Cover

More Information
Summary:Paramagnetic nuclear magnetic resonance (NMR) methods have emerged as powerful tools for structure determination of large, sparsely protonated proteins. However traditional applications face several challenges, including a need for large datasets to offset the sparsity of restraints, the difficulty in accounting for the conformational heterogeneity of the spin-label, and noisy experimental data. Here we propose an integrative approach to structure determination combining sparse paramagnetic NMR with physical modelling to infer approximate protein structural ensembles. We use calmodulin in complex with the smooth muscle myosin light chain kinase peptide as a model system. Despite acquiring data from samples labeled only at the backbone amide positions, we are able to produce an ensemble with an average RMSD of ∼2.8 Å from a reference X-ray crystal structure. Our approach requires only backbone chemical shifts and measurements of the paramagnetic relaxation enhancement and residual dipolar couplings that can be obtained from sparsely labeled samples.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Alexandre M. J. J. Bonvin, Utrecht University, Netherlands
Reviewed by:Enrico Ravera, University of Florence, Italy
Edited by:Massimiliano Bonomi, Institut Pasteur, France
These authors have contributed equally to this work
This article was submitted to Biological Modeling and Simulation, a section of the journal Frontiers in Molecular Biosciences
ISSN:2296-889X
2296-889X
DOI:10.3389/fmolb.2021.676268