Variability of Protein Structure Models from Electron Microscopy

Monroe, Lyman; Terashi, Genki

doi:10.1016/j.str.2017.02.004

Cited by 13 publications

(13 citation statements)

References 41 publications

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“…Density maps that were determined at a resolution between 5.0 Å to 10.0 Å and have an associated atomic structure in PDB were selected. Then, to ensure that a map and its associated structure have sufficient structural agreement, the cross-correlation between the experimental map and the simulated map density at the resolution of the experimental map computed from the structure was examined 34 and only maps with a cross-correlation of over 0.65 were kept. Finally, we computed the sequence identity between underlined proteins in pairs of EM maps, and a map was removed from the dataset if its underlined protein has over 25% identity to a protein of another map in the dataset.…”

Section: Methodsmentioning

confidence: 99%

Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning

2019

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An increasing number of protein structures have been solved by cryo-electron microscopy (cryo-EM). Although structures determined at near-atomic resolution are now routinely reported, many density maps are still determined at an intermediate resolution, where extracting structure information is still a challenge. We have developed a computational method, Emap2sec, which identifies the secondary structures of proteins (α helices, β sheets, and other structures) in an EM map of 5 to 10 Å resolution. Emap2sec uses a 3D deep convolutional neural network to assign secondary structure to each grid point in an EM map. We tested Emap2sec on 6.0 and 10.0 Å resolution EM maps simulated from 34 structures, as well as on 43 maps determined experimentally at 5.0 to 9.5 Å resolution. Emap2sec was able to clearly identify the secondary structures in many maps tested, and showed substantially better performance than existing methods.

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Section: Methodsmentioning

confidence: 99%

Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning

2019

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“…The procedure to vary the weight factor between data and model has been investigated earlier by Monroe et al (2017) in the context of MDFF and Rosetta, where they found that most models could be "refined further", although their dataset was mainly aimed at lower resolution cases down to 20Å. In addition, besides the difference in software platform used, they did not explicitly show how the weight factor relates to the cross correlation or how to decide on the optimal weight factor at high resolution cryo-EM modeling.…”

Section: Re-refining Cryo-em Structures With Phenix / Opls3ementioning

confidence: 99%

Macromolecular refinement of X-ray and cryo-electron microscopy structures with Phenix / OPLS3e for improved structure and ligand quality

Zundert

Borrelli

Moriarty

et al. 2020

Preprint

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Accurate macromolecular structure refinement is of paramount importance in structure based drug discovery as it provides a gateway to using ligand binding free energy calculations and ligand docking techniques. When dealing with high-resolution data, a simple restraint model may be preferred when the data is able to guide atom parameters to an unambiguous location. However, at lower resolution, the additional information contained in a complex force field may aid in refinement by avoiding implausible structures permitted by the simpler restraints. With the advent of the resolution revolution in cryo-electron microscopy, low resolution refinement is common, and likewise increases the need for a reliable force field. Here we report on the incorporation of the OPLS3e force field with the VSGB2.1 solvation model in the popular refinement package Phenix. The implementation is versatile and can be used in both reciprocal and real space refinement, alleviating the need for manually creating accurate ligand restraint dictionaries in the form of CIF files. Our results show significantly improved structure quality at lower resolution for X-ray refinement with reduced ligand strain, while showing only a slight increase in Rfree. For real space refinement of cryo-EM based structures, we find comparable quality structures, goodness-of-fit and reduced ligand strain. In addition, we explicitly show how structure quality is related with the map-model cross correlation as a function of data weight, and how it can be an insightful tool for detecting both over- and underfitting, especially coupled with ligand energies. Further, we have compiled a user-friendly start-to-end script for refining structures with Phenix/OPLS3e, which is available starting in the Schrodinger 2020-3 distribution.

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“…Out of 44 fragments in the data set, the model by MAINMAST had a lower or identical RMSD with RosettaES models for 16 cases, and better or worse but within an RMSD margin of 0.5 or 1.0 Å to RosettaES models for 26 and 32 cases, respectively. Considering a recent study that investigated variability of structure models from EM maps 29 , a difference within 1 Å is not meaningful for maps determined at a resolution of 3 Å. Thus, overall the performance of MAINMAST was comparable to RosettaES.…”

Section: Resultsmentioning

confidence: 85%

De novo main-chain modeling for EM maps using MAINMAST

2018

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An increasing number of protein structures are determined by cryo-electron microscopy (cryo-EM) at near atomic resolution. However, tracing the main-chains and building full-atom models from EM maps of ~4–5 Å is still not trivial and remains a time-consuming task. Here, we introduce a fully automated de novo structure modeling method, MAINMAST, which builds three-dimensional models of a protein from a near-atomic resolution EM map. The method directly traces the protein’s main-chain and identifies Cα positions as tree-graph structures in the EM map. MAINMAST performs significantly better than existing software in building global protein structure models on data sets of 40 simulated density maps at 5 Å resolution and 30 experimentally determined maps at 2.6–4.8 Å resolution. In another benchmark of building missing fragments in protein models for EM maps, MAINMAST builds fragments of 11–161 residues long with an average RMSD of 2.68 Å.

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Variability of Protein Structure Models from Electron Microscopy

Cited by 13 publications

References 41 publications

Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning

Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning

Macromolecular refinement of X-ray and cryo-electron microscopy structures with Phenix / OPLS3e for improved structure and ligand quality

De novo main-chain modeling for EM maps using MAINMAST

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