Evidence map›Paper›PMID 29132296›Full record

ArticleBMC bioinformatics2017

A fragment based method for modeling of protein segments into cryo-EM density maps.

Jochen Ismer, Alexander S Rose, Johanna K S Tiemann, Peter W Hildebrand

Abstract readValidation Study
In one paragraph

Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
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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

4 authors.

Jochen IsmerInstitute of Medical Physics and Biophysics, University Medicine Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Alexander S RoseInstitute of Medical Physics and Biophysics, University Medicine Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Johanna K S TiemannInstitute of Medical Physics and Biophysics, University Medicine Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Peter W HildebrandInstitute of Medical Physics and Biophysics, University Medicine Berlin, Charitéplatz 1, 10117, Berlin, Germany. peter.hildebrand@charite.de.

Funding

Deutsche Forschungsgemeinschaft BI 893/8Deutsche Forschungsgemeinschaft HI 1502/1-2Deutsche Forschungsgemeinschaft Sfb740/B6
6 · The paper itself

Abstract

backgroundSingle-particle analysis of electron cryo-microscopy (cryo-EM) is a key technology for elucidation of macromolecular structures. Recent technical advances in hardware and software developments significantly enhanced the resolution of cryo-EM density maps and broadened the applicability and the circle of users. To facilitate modeling of macromolecules into cryo-EM density maps, fast and easy to use methods for modeling are now demanded.

resultsHere we investigated and benchmarked the suitability of a classical and well established fragment-based approach for modeling of segments into cryo-EM density maps (termed FragFit). FragFit uses a hierarchical strategy to select fragments from a pre-calculated set of billions of fragments derived from structures deposited in the Protein Data Bank, based on sequence similarly, fit of stem atoms and fit to a cryo-EM density map. The user only has to specify the sequence of the segment and the number of the N- and C-terminal stem-residues in the protein. Using a representative data set of protein structures, we show that protein segments can be accurately modeled into cryo-EM density maps of different resolution by FragFit. Prediction quality depends on segment length, the type of secondary structure of the segment and local quality of the map.

conclusionFast and automated calculation of FragFit renders it applicable for implementation of interactive web-applications e.g. to model missing segments, flexible protein parts or hinge-regions into cryo-EM density maps.

Indexed as

Cryoelectron MicroscopyDatabases, ProteinModels, MolecularProteinsProtein Structure, SecondarySoftwareProteinsCryo-EMFlexible fittingFragment based modeling

Identifiers

PMID29132296
PMCPMC5683378

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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.