Evidence map›Paper›PMID 30092279›Full record

ArticleJournal of structural biology2018

Constructing atomic structural models into cryo-EM densities using molecular dynamics - Pros and cons.

Yuhang Wang, Mrinal Shekhar, Darren Thifault, Christopher J Williams, Ryan McGreevy, Jane Richardson, Abhishek Singharoy, Emad Tajkhorshid

Open access · greenAbstract read
In one paragraph

Article in Journal of structural biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Cryo-EM structures of a lipid-sensitive pentameric ligand-gated ion channel embedded in a phosphatidylcholine-only bilayer.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
  4. Article
  5. Comparing cryo-EM structures.Journal of structural biology · 2018
    Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Yuhang WangCenter for Biophysics and Quantitative Biology, College of Medicine, Department of Biochemistry, Beckman Institute for Advanced Science and Technology, and University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States.
Mrinal ShekharCenter for Biophysics and Quantitative Biology, College of Medicine, Department of Biochemistry, Beckman Institute for Advanced Science and Technology, and University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States.
Darren ThifaultSchool of Molecular Sciences, Center for Applied Structural Discovery, Arizona State University, Tempe, AZ 85287, United States.
Christopher J WilliamsDepartment of Biochemistry, Duke University, Durham, NC 27710, United States.
Ryan McGreevyCenter for Biophysics and Quantitative Biology, College of Medicine, Department of Biochemistry, Beckman Institute for Advanced Science and Technology, and University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States.
Jane RichardsonDepartment of Biochemistry, Duke University, Durham, NC 27710, United States.
Abhishek SingharoySchool of Molecular Sciences, Center for Applied Structural Discovery, Arizona State University, Tempe, AZ 85287, United States. Electronic address: asinghar@asu.edu.
Emad TajkhorshidNIH Center for Macromolecular Modeling and Bioinformatics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States.
University of Illinois Urbana-Champaign · USArizona State University · USDuke University · US

Funding

PROJECT 5 - DUKE - STRUCTURE VALIDATION AND IMPROVEMENT FOR PROTEINS AND N. ACIDSP01GM063210 · NIGMS · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI ADAMS, PAUL DAVID · 2001 to 2021
$33.5M
WHOLE CELL SIMULATIONP41GM104601 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI TAJKHORSHID, EMAD · 2012 to 2021
$19.0M
System-Level Simulation of a Bioenergetic MembraneR01GM067887 · NIGMS · UNIVERSITY OF ILLINOIS URBANA-CHAMPAIGN · PI TAJKHORSHID, EMAD · 2003 to 2018
$4.0M
MolProbity Validation & Corrections: for Crystallography, PDB & BiomedicineR01GM073919 · NIGMS · DUKE UNIVERSITY · PI RICHARDSON, DAVID CLAUDE · 2006 to 2018
$3.7M
Commodity Hardware Acceleration of Popular Modeling Software for Structural BioloR01GM098243 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI SCHULTEN, KLAUS · 2011 to 2014
$1.2M
NIGMS NIH HHS P01 GM063210NIGMS NIH HHS P41 GM104601NIGMS NIH HHS R01 GM067887NIGMS NIH HHS R01 GM073919NIGMS NIH HHS R01 GM098243
6 · The paper itself

Abstract

Accurate structure determination from electron density maps at 3-5 Å resolution necessitates a balance between extensive global and local sampling of atomistic models, yet with the stereochemical correctness of backbone and sidechain geometries. Molecular Dynamics Flexible Fitting (MDFF), particularly through a resolution-exchange scheme, ReMDFF, provides a robust way of achieving this balance for hybrid structure determination. Employing two high-resolution density maps, namely that of β-galactosidase at 3.2 Å and TRPV1 at 3.4 Å, we showcase the quality of ReMDFF-generated models, comparing them against ones submitted by independent research groups for the 2015-2016 Cryo-EM Model Challenge. This comparison offers a clear evaluation of ReMDFF's strengths and shortcomings, and those of data-guided real-space refinements in general. ReMDFF results scored highly on the various metric for judging the quality-of-fit and quality-of-model. However, some systematic discrepancies are also noted employing a Molprobity analysis, that are reproducible across multiple competition entries. A space of key refinement parameters is explored within ReMDFF to observe their impact within the final model. Choice of force field parameters and initial model seem to have the most significant impact on ReMDFF model-quality. To this end, very recently developed CHARMM36m force field parameters provide now more refined ReMDFF models than the ones originally submitted to the Cryo-EM challenge. Finally, a set of good-practices is prescribed for the community to benefit from the MDFF developments.

Indexed as

Cryoelectron MicroscopyMolecular Dynamics SimulationProtein ConformationCryo-EM data challengeForce fieldsHybrid modelingMolecular dynamics flexible fittingResolution-exchangeStructure determination

Identifiers

PMID30092279
PMCPMC6394829
OpenAlexW2886486369

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.