Evidence map›Paper›PMID 22155667›Full record

ArticleJournal of structural biology2012

Evolutionary bidirectional expansion for the tracing of alpha helices in cryo-electron microscopy reconstructions.

Mirabela Rusu, Willy Wriggers

Abstract readValidation Study
In one paragraph

Article in Journal of structural biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

32 citing papers in PubMed.

  1. Article
  2. Article
  3. An Approach to Developing Benchmark Datasets for Protein Secondary Structure Segmentation from Cryo-EM Density Maps.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2023
    Article
  4. Article
  5. Article
  6. Article
  7. Combine Cryo-EM Density Map and Residue Contact for Protein Structure Prediction - A Case Study.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2020
    Article
  8. Using Curriculum Learning in Pattern Recognition of 3-dimensional Cryo-electron Microscopy Density Maps.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2020
    Article
  9. Article
  10. Review
  11. Exploratory Studies Detecting Secondary Structures in Medium Resolution 3D Cryo-EM Images Using Deep Convolutional Neural Networks.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2018
    Article
  12. A Pattern Recognition Tool for Medium-resolution Cryo-EM Density Maps and Low-resolution Cryo-ET Density maps.Bioinformatics research and applications : 14th International Symposium, ISBRA 2018, Beijing, China, June 8-11, 2018, Proceedings. ISBRA (Conference) (14th : 2018 : Beijing, China) · 2018
    Article
  13. Review
  14. Article
  15. Quantification of Twist from the Central Lines of β-Strands.Journal of computational biology : a journal of computational molecular cell biology · 2018
    Article
  16. Analysis of β-strand Twist from the 3-dimensional Image of a Protein.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2017
    Article
  17. Article
  18. Article
  19. Comparing an Atomic Model or Structure to a Corresponding Cryo-electron Microscopy Image at the Central Axis of a Helix.Journal of computational biology : a journal of computational molecular cell biology · 2017
    Article
  20. Deep Convolutional Neural Networks for Detecting Secondary Structures in Protein Density Maps from Cryo-Electron Microscopy.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2016
    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.

Mirabela RusuSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St., Houston, TX 77030, USA. mirabela.rusu@biomachina.org
Willy Wriggers

Funding

Multi-Resolution Docking Methods for Electron MicroscopyR01GM062968 · NIGMS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI WRIGGERS, WILLY R · 2001 to 2023
$4.8M
NIGMS NIH HHS R01 GM062968NIGMS NIH HHS R01GM62968
6 · The paper itself

Abstract

Cryo-electron microscopy (cryo-EM) enables the imaging of macromolecular complexes in near-native environments at resolutions that often permit the visualization of secondary structure elements. For example, alpha helices frequently show consistent patterns in volumetric maps, exhibiting rod-like structures of high density. Here, we introduce VolTrac (Volume Tracer) - a novel technique for the annotation of alpha-helical density in cryo-EM data sets. VolTrac combines a genetic algorithm and a bidirectional expansion with a tabu search strategy to trace helical regions. Our method takes advantage of the stochastic search by using a genetic algorithm to identify optimal placements for a short cylindrical template, avoiding exploration of already characterized tabu regions. These placements are then utilized as starting positions for the adaptive bidirectional expansion that characterizes the curvature and length of the helical region. The method reliably predicted helices with seven or more residues in experimental and simulated maps at intermediate (4-10Å) resolution. The observed success rates, ranging from 70.6% to 100%, depended on the map resolution and validation parameters. For successful predictions, the helical axes were located within 2Å from known helical axes of atomic structures.

Indexed as

AlgorithmsComputer SimulationModels, MolecularChaperonin 60Cryoelectron MicroscopyImaging, Three-DimensionalKinesinsPlant VirusesProteasome Endopeptidase ComplexProtein Structure, SecondaryChaperonin 60KinesinsProteasome Endopeptidase Complex

Identifiers

PMID22155667
PMCPMC3288247

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.