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...

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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
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Abstract 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.
AbstractList 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.
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.
Author Vogel, Hans J
MacCallum, Justin L
Gaalswyk, Kari
Liu, Zhihong
AuthorAffiliation 2 Department of Biological Sciences, University of Calgary, Calgary , AB , Canada
1 Department of Chemistry, University of Calgary, Calgary , AB , Canada
AuthorAffiliation_xml – name: 1 Department of Chemistry, University of Calgary, Calgary , AB , Canada
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  fullname: Liu, Zhihong
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  givenname: Hans J
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  givenname: Justin L
  surname: MacCallum
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  organization: Department of Chemistry, University of Calgary, Calgary, AB, Canada
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Keywords protein structure
NMR
paramagnetic relaxation enhancement
modeling
integrative structural biology
calmodulin
Language English
License Copyright © 2021 Gaalswyk, Liu, Vogel and MacCallum.
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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
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Snippet Paramagnetic nuclear magnetic resonance (NMR) methods have emerged as powerful tools for structure determination of large, sparsely protonated proteins....
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SubjectTerms calmodulin
integrative structural biology
modeling
Molecular Biosciences
NMR
paramagnetic relaxation enhancement
protein structure
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Title An Integrative Approach to Determine 3D Protein Structures Using Sparse Paramagnetic NMR Data and Physical Modeling
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