High Accuracy Protein Structures from Minimal Sparse Paramagnetic Solid‐State NMR Restraints

Pérez, Alberto; Gaalswyk, Kari; Jaroniec, Christopher P.; MacCallum, Justin L.

doi:10.1002/ange.201811895

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…The physics-based prior,

, specifies which structures are more likely a priori and determines the distribution of structures in the absence of data. In the present study the physics-based prior is given by the Amber ff14SB force field ( Maier et al, 2015 ) with a grid-based torsion potential ( Perez et al, 2015 ) and the OBC generalized-Born implicit solvent model ( Onufriev et al, 2004 ). The likelihood function,

, captures the compatibility between the data and some structure

.…”

Section: Computational Approachmentioning

confidence: 99%

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

Gaalswyk

Liu

Vogel

et al. 2021

Front. Mol. Biosci.

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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.

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“…The physics-based prior,

, captures the compatibility between the data and some structure

.…”

Section: Computational Approachmentioning

confidence: 99%

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

Gaalswyk

Liu

Vogel

et al. 2021

Front. Mol. Biosci.

Self Cite

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show abstract

“…25 MELD has already shown promising results in solving biomolecular structures using different sources of data, such as sparsely labeled NMR samples 26,27 or solid-state NMR paramagnetic relaxation enhancement data. 28 We tested CryoFold 2.0 on eight different systems using diverse starting models to highlight the benefits of the current approach.…”

Section: ■ Introductionmentioning

confidence: 99%

“…And third, this integration is compatible with MELD’s philosophy to combine other sources of data in regions where cryo-EM data is limited such as cross-linking mass spectroscopy . MELD has already shown promising results in solving biomolecular structures using different sources of data, such as sparsely labeled NMR samples , or solid-state NMR paramagnetic relaxation enhancement data . We tested CryoFold 2.0 on eight different systems using diverse starting models to highlight the benefits of the current approach.…”

Section: Introductionmentioning

confidence: 99%

CryoFold 2.0: Cryo-EM Structure Determination with MELD

et al. 2023

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Cryo-electron microscopy data are becoming more prevalent and accessible at higher resolution levels, leading to the development of new computational tools to determine the atomic structure of macromolecules. However, while existing tools adapted from X-ray crystallography are suitable for the highest-resolution maps, new tools are needed for lower-resolution levels and to account for map heterogeneity. In this article, we introduce CryoFold 2.0, an integrative physics-based approach that combines Bayesian inference and the ability to handle multiple data sources with the molecular dynamics flexible fitting (MDFF) approach to determine the structures of macromolecules by using cryo-EM data. CryoFold 2.0 is incorporated into the MELD (modeling employing limited data) plugin, resulting in a pipeline that is more computationally efficient and accurate than running MELD or MDFF alone. The approach requires fewer computational resources and shorter simulation times than the original CryoFold, and it minimizes manual intervention. We demonstrate the effectiveness of the approach on eight different systems, highlighting its various benefits.

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Dimer Organization of Membrane‐Associated NS5A of Hepatitis C Virus as Determined by Highly Sensitive ¹H‐Detected Solid‐State NMR

et al. 2021

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The Hepatitis Cv irus nonstructural protein 5A (NS5A) is amembrane-associated protein involved in multiple steps of the viral life cycle.D irect-acting antivirals (DAAs) targeting NS5A are ac ornerstone of antiviral therapy, but the mode-of-action of these drugs is poorly understood. This is due to the lack of information on the membrane-bound NS5A structure.Herein, we present the structural model of an NS5A AH-linker-D1 protein reconstituted as proteoliposomes.W e use highly sensitive proton-detected solid-state NMR methods suitable to study samples generated through synthetic biology approaches.S pectra analyses disclose that both the AH membrane anchor and the linker are highly flexible.P aramagnetic relaxation enhancements (PRE) reveal that the dimer organization in lipids requires an ew type of NS5A selfinteraction not reflected in previous crystal structures.I n conclusion, we providethe first characterization of NS5A AHlinker-D1 in al ipidic environment shedding light onto the mode-of-action of clinically used NS5A inhibitors.

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High Accuracy Protein Structures from Minimal Sparse Paramagnetic Solid‐State NMR Restraints

Cited by 4 publications

References 37 publications

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

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

CryoFold 2.0: Cryo-EM Structure Determination with MELD

Dimer Organization of Membrane‐Associated NS5A of Hepatitis C Virus as Determined by Highly Sensitive ¹H‐Detected Solid‐State NMR

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High Accuracy Protein Structures from Minimal Sparse Paramagnetic Solid‐State NMR Restraints

Cited by 4 publications

References 37 publications

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

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

CryoFold 2.0: Cryo-EM Structure Determination with MELD

Dimer Organization of Membrane‐Associated NS5A of Hepatitis C Virus as Determined by Highly Sensitive 1H‐Detected Solid‐State NMR

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Dimer Organization of Membrane‐Associated NS5A of Hepatitis C Virus as Determined by Highly Sensitive ¹H‐Detected Solid‐State NMR