Evidence map›Paper›PMID 33999209›Full record

ArticleNucleic acids research2021

DeepRefiner: high-accuracy protein structure refinement by deep network calibration.

Md Hossain Shuvo, Muhammad Gulfam, Debswapna Bhattacharya

Abstract read
In one paragraph

Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Understanding Pseudophosphatase Function Through Biochemical Interactions.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  13. Long-Distance Movement ofPlants (Basel, Switzerland) · 2023
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Pseudophosphatases as Regulators of MAPK Signaling.International journal of molecular sciences · 2021
    Review
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.

Md Hossain ShuvoDepartment of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, USA.
Muhammad GulfamDepartment of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, USA.
Debswapna BhattacharyaDepartment of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, USA.ORCID 0000-0002-9630-0141

Funding

GPU-accelerated high-performance computing to supercharge foundational deep learning method development for scalable and accurate prediction of protein structuresR35GM138146 · NIGMS · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI Debswapna Bhattacharya · 2020 to 2026
$2.5M
NIGMS NIH HHS R35 GM138146
6 · The paper itself

Abstract

The DeepRefiner webserver, freely available at http://watson.cse.eng.auburn.edu/DeepRefiner/, is an interactive and fully configurable online system for high-accuracy protein structure refinement. Fuelled by deep learning, DeepRefiner offers the ability to leverage cutting-edge deep neural network architectures which can be calibrated for on-demand selection of adventurous or conservative refinement modes targeted at degree or consistency of refinement. The method has been extensively tested in the Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiments under the group name 'Bhattacharya-Server' and was officially ranked as the No. 2 refinement server in CASP13 (second only to 'Seok-server' and outperforming all other refinement servers) and No. 2 refinement server in CASP14 (second only to 'FEIG-S' and outperforming all other refinement servers including 'Seok-server'). The DeepRefiner web interface offers a number of convenient features, including (i) fully customizable refinement job submission and validation; (ii) automated job status update, tracking, and notifications; (ii) interactive and interpretable web-based results retrieval with quantitative and visual analysis and (iv) extensive help information on job submission and results interpretation via web-based tutorial and help tooltips.

Indexed as

Protein ConformationSoftwareDeep LearningModels, Molecular

Identifiers

PMID33999209
PMCPMC8262753

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.