Evidence map›Paper›PMID 28833585›Full record

ArticleProteins2018

Protein structure model refinement in CASP12 using short and long molecular dynamics simulations in implicit solvent.

Genki Terashi, Daisuke Kihara

Abstract read
In one paragraph

Article in Proteins, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
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

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
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  3. Article
  4. Article
  5. Article
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  7. Article
  8. Methods for the Refinement of Protein Structure 3D Models.International journal of molecular sciences · 2019
    Review
  9. 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

2 authors.

Genki TerashiDepartment of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907.
Daisuke KiharaDepartment of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907.ORCID 0000-0003-4091-6614

Funding

Structural Modeling of Multifarious Protein ComplexesR01GM123055 · NIGMS · PURDUE UNIVERSITY · PI KIHARA, DAISUKE · 2017 to 2020
$1.2M
NIGMS NIH HHS R01 GM123055
6 · The paper itself

Abstract

Protein structure prediction has matured over years, particularly those which use structure templates for building a model. It can build a model with correct overall conformation in cases where appropriate templates are available. Models with the correct topology can be practically useful for limited purposes that need residue-level accuracy, but further improvement of the models can allow the models to be used in tasks that need detailed structures, such as molecular replacement in X-ray crystallography or structure-based drug screening. Thus, model refinement is an important final step in protein structure prediction to bridge predictions to real-life applications. Model refinement is one of the categories in recent rounds of critical assessment of techniques in protein structure prediction (CASP) and has recently been drawing more attention due to its realized importance. Here we report our group's performance in the refinement category in CASP12. Our method is based on inexpensive short molecular dynamics (MD) simulations in implicit solvent. Our performance in CASP12 was among the top, which was consistent with the previous round, CASP11. Our method with short MD runs achieved comparable performance with other methods that used longer simulations. Detailed analyses found that improvements typically occurred in entire regions of a structure rather than only in flexible loop regions. The remaining challenge in the structure refinement includes large conformational refinement which involves substantial motions of secondary structure elements or domains.

Indexed as

Models, MolecularMolecular Dynamics SimulationProtein ConformationAlgorithmsComputational BiologyCrystallography, X-RayHumansProteinsSequence Analysis, ProteinSolventsProteinsSolventsCASPcomputational methodmolecular dynamicsprotein structure predictionprotein structure refinementstructure modelingtemplate-based modeling

Identifiers

PMID28833585
PMCPMC5820213

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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.