Evidence map›Paper›PMID 31373574›Full record

ArticleActa crystallographica. Section D, Structural biology2019

Sequence assignment for low-resolution modelling of protein crystal structures.

Grzegorz Chojnowski, Joana Pereira, Victor S Lamzin

Abstract read
In one paragraph

Article in Acta crystallographica. Section D, Structural biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Deep learning applications in protein crystallography.Acta crystallographica. Section A, Foundations and advances · 2024
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. IUCrJ · 2022
    Article
  11. CryoEM analysis of small plant biocatalysts at sub-2 Å resolution.Acta crystallographica. Section D, Structural biology · 2022
    Article
  12. Article
  13. Article
  14. The accuracy of protein models automatically built into cryo-EM maps with ARP/wARP.Acta crystallographica. Section D, Structural biology · 2021
    Article
  15. Article
  16. 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

3 authors.

Grzegorz ChojnowskiEuropean Molecular Biology Laboratory, c/o DESY, Notkestrasse 85, 22607 Hamburg, Germany.
Joana PereiraEuropean Molecular Biology Laboratory, c/o DESY, Notkestrasse 85, 22607 Hamburg, Germany.ORCID 0000-0002-5588-6588
Victor S LamzinEuropean Molecular Biology Laboratory, c/o DESY, Notkestrasse 85, 22607 Hamburg, Germany.

Funding

Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie 05K14YEAHorizon 2020 Framework Programme EINFRA-2015-1-675858
6 · The paper itself

Abstract

The performance of automated model building in crystal structure determination usually decreases with the resolution of the experimental data, and may result in fragmented models and incorrect side-chain assignment. Presented here are new methods for machine-learning-based docking of main-chain fragments to the sequence and for their sequence-independent connection using a dedicated library of protein fragments. The combined use of these new methods noticeably increases sequence coverage and reduces fragmentation of the protein models automatically built with ARP/wARP.

Indexed as

Machine LearningAlgorithmsAmino Acid SequenceCrystallography, X-RayDatabases, ProteinDatasets as TopicMolecular Docking SimulationProtein ConformationProteinsSoftwareProteinsARP/wARPloop buildinglow-resolution modellingmacromolecular crystallographymodel buildingsequence assignment

Identifiers

PMID31373574
PMCPMC6677015

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.