Evidence map›Paper›PMID 26779991›Full record

ArticleProtein science : a publication of the Protein Society2016

On the reliability of peptide nonplanarity seen in ultra-high resolution crystal structures.

Andrew E Brereton, P Andrew Karplus

Abstract read
In one paragraph

Article in Protein science : a publication of the Protein Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Stereochemistry and Validation of Macromolecular Structures.Methods in molecular biology (Clifton, N.J.) · 2017
    Review
  5. How planar are planar peptide bonds?Protein science : a publication of the Protein Society · 2016
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Andrew E BreretonDepartment of Biochemistry and Biophysics, Oregon State University, Corvallis, 97331, Oregon.
P Andrew KarplusDepartment of Biochemistry and Biophysics, Oregon State University, Corvallis, 97331, Oregon.

Funding

Improving Modeling by Learning from Details of High Accuracy Protein StructuresR01GM083136 · NIGMS · OREGON STATE UNIVERSITY · PI KARPLUS, PAUL ANDREW · 2008 to 2015
$1.7M
NIGMS NIH HHS R01 GM083136
6 · The paper itself

Abstract

Ultra-high resolution protein crystal structures have been considered as relatively reliable sources for defining details of protein geometry, such as the extent to which the peptide unit deviates from planarity. Chellapa and Rose (Proteins 2015; 83:1687) recently called this into question, reporting that for a dozen representative protein structures determined at ∼ 1 Å resolution, the diffraction data could be equally well fit with models restrained to have highly planar peptides, i.e. having a standard deviation of the ω torsion angles of only ∼ 1° instead of the typically observed value of ∼ 6°. Here, we document both conceptual and practical shortcomings of that study and show that the more tightly restrained models are demonstrably incorrect and do not fit the diffraction data equally well. We emphasize the importance of inspecting electron density maps when investigating the agreement between a model and its experimental data. Overall, this report reinforces that modern standard refinement protocols have been well-conceived and that ultra-high resolution protein crystal structures, when evaluated carefully and used with an awareness of their levels of coordinate uncertainty, are powerful sources of information for providing reliable information about the details of protein geometry.

Indexed as

Crystallography, X-RayModels, MolecularPeptidesProtein ConformationReproducibility of ResultsPeptidesatomic resolutionmodel validationpeptide nonplanarityphenix refinementprotein crystallographyprotein geometry

Identifiers

PMID26779991
PMCPMC4941219

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Registered trials

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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.