Advances in the molecular dynamics flexible fitting method for cryo-EM modeling

McGreevy, Ryan; Teo, Ivan; Singharoy, Abhishek; Schulten, Klaus

doi:10.1016/j.ymeth.2016.01.009

Cited by 87 publications

(106 citation statements)

References 101 publications

(134 reference statements)

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“…Coot (Emsley and Cowtan, 2004) was used for model building. Initial rounds of refinement used xMDFF, with input files prepared in VMD (Humphrey et al, 1996) using the MDFF-GUI (McGreevy et al, 2016) with default molecular dynamics parameters (McGreevy et al, 2014), improving R work /R free from 0.50/0.54 to 0.41/0.45. The model was further refined using PHENIX with additional local refinements using xMDFF.…”

Section: Methodsmentioning

confidence: 99%

Crystal Structure and Conformational Change Mechanism of a Bacterial Nramp-Family Divalent Metal Transporter

et al. 2016

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Summary The widely-conserved natural resistance associated macrophage protein (Nramp) family of divalent metal transporters enables manganese import in bacteria and dietary iron uptake in mammals. We determined the crystal structure of the Deinococcus radiodurans Nramp homolog (DraNramp) in an inward-facing apo state, including the complete transmembrane (TM) segment 1a—absent from a previous Nramp structure. Mapping our cysteine accessibility scanning results onto this structure, we identified the metal permeation pathway in the alternate outward-open conformation. We investigated the functional impact of two natural anemia-causing glycine-to-arginine mutations, which impaired transition metal transport in both human Nramp2 and DraNramp. The TM4 G153R mutation perturbs the closing of the outward metal permeation pathway and alters the selectivity of the conserved metal-binding site. In contrast, the TM1a G45R mutation prevents conformational change by sterically blocking the essential movement of that helix, thus locking the transporter in an inward-facing state.

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Section: Methodsmentioning

confidence: 99%

Crystal Structure and Conformational Change Mechanism of a Bacterial Nramp-Family Divalent Metal Transporter

et al. 2016

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“…Each equilibrated structure was then refined via a density-guided MD simulation step (MDFF1) 69, 71 . The last frame of this trajectory was used as the input model for Rosetta density-guided local building and refinement (Rosettal) 58 .…”

Section: Methodsmentioning

confidence: 99%

“…As opposed to Rosetta, MD simulations use a purely physics-based approach, based on integrating the equations of motion in molecular mechanics force fields 70 . The molecular dynamics flexible fitting method (MDFF) uses MD simulations to refine protein structure guided by experimental cryo-EM density maps 69, 71 . MDFF has been successfully applied to various biological systems, including membrane proteins 71–73 .…”

Section: Introductionmentioning

confidence: 99%

“…The molecular dynamics flexible fitting method (MDFF) uses MD simulations to refine protein structure guided by experimental cryo-EM density maps 69, 71 . MDFF has been successfully applied to various biological systems, including membrane proteins 71–73 . The success of MDFF structure refinement depends on the resolutions of the available cryo-EM density maps.…”

Section: Introductionmentioning

confidence: 99%

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Iterative Molecular Dynamics–Rosetta Membrane Protein Structure Refinement Guided by Cryo-EM Densities

Leelananda

Lindert

2017

J. Chem. Theory Comput.

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Knowing atomistic details of proteins is essential not only for the understanding of protein function but also for the development of drugs. Experimental methods such as X-ray crystallography, NMR and cryo-EM are the preferred form of protein structure determination and have achieved great success over the last decades. Computational methods may be an alternative when experimental techniques fail. However, computational methods are severely limited when it comes to predicting larger macromolecule structures with little sequence similarity to known structures. The incorporation of experimental restraints in computational methods is becoming increasingly important to more reliably predict protein structure. One such experimental input used in structure prediction and refinement is cryo-EM densities. Recent advances in cryo-EM have arguably revolutionized the field of structural biology. Our previously developed cryo-EM guided Rosetta-MD protocol has shown great promise in the refinement of soluble protein structures. In this study, we extended cryo-EM density-guided iterative Rosetta-MD to membrane proteins. We also improved the methodology in general by picking models based on a combination of their score and fit-to-density during the Rosetta model selection. By doing so, we have been able to pick superior models than with the previous selection based on Rosetta score only and we have been able to further improve our previously refined models of soluble proteins. The method was tested with five membrane spanning protein structures. By applying density-guided Rosetta-MD iteratively we were able to refine the predicted structures of these membrane proteins to atomic resolutions. We also showed that the resolution of the density maps determines the improvement and quality of the refined models. By incorporating high resolution density maps (~ 4 Å) we were able to more significantly improve the quality of the models than when medium resolution maps (6.9 Å) were used. Beginning from an average starting structure RMSD to native of 4.66 Å, our protocol was able to refine the structures to bring the average refined structure RMSD to 1.66 Å when 4 Å density maps were used.

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“…More concretely, for 49 EM maps of a wide range of resolutions, we ran two structure fitting/refinement protocols, Molecular Dynamics Flexible Fitting (MDFF) (McGreevy et al, 2014; McGreevy et al, 2016) and Rosetta (Wang et al, 2015) starting from the deposited structure models and observed changes of cross correlation of the models to the EM maps and the energy of those structures. These two programs were chosen because they are among the most popular structure modeling and refinement tools used for protein structure determination of EM maps.…”

Section: Introductionmentioning

confidence: 99%

Variability of Protein Structure Models from Electron Microscopy

Monroe

Terashi

2017

Structure

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Summary An increasing number of biomolecular structures are solved by electron microscopy (EM). However, the quality of structure models determined from EM maps vary substantially. To understand to what extent structure models are supported by information embedded in EM maps, we used two computational structure refinement methods to examine how much structures can be refined using a dataset of 49 maps with accompanying structure models. The extent of structure modification as well as the disagreement between refinement models produced by the two computational methods scaled inversely with the global and the local map resolutions. A general quantitative estimation of deviations of structures for particular map resolutions are provided. Our results indicate that the observed discrepancy between the deposited map and the refined models is due to the lack of structural information present in EM maps and thus these annotations must be used with caution for further applications.

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Advances in the molecular dynamics flexible fitting method for cryo-EM modeling

Cited by 87 publications

References 101 publications

Crystal Structure and Conformational Change Mechanism of a Bacterial Nramp-Family Divalent Metal Transporter

Crystal Structure and Conformational Change Mechanism of a Bacterial Nramp-Family Divalent Metal Transporter

Iterative Molecular Dynamics–Rosetta Membrane Protein Structure Refinement Guided by Cryo-EM Densities

Variability of Protein Structure Models from Electron Microscopy

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