Evidence map›Paper›PMID 34240477›Full record

ArticleProteins2021

Topology evaluation of models for difficult targets in the 14th round of the critical assessment of protein structure prediction (CASP14).

Lisa N Kinch, Jimin Pei, Andriy Kryshtafovych, R Dustin Schaeffer, Nick V Grishin

Abstract read
In one paragraph

Article in Proteins, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

  1. Article
  2. Family-level specialization in protein domain insertion architectures.Protein science : a publication of the Protein Society · 2026
    Article
  3. Predicting Single-Stranded DNA Oligonucleotides 3D Structures: An Open Issue.Computational and structural biotechnology journal · 2026
    Article
  4. Article
  5. Review
  6. Article
  7. Assessment of nucleic acid structure prediction in CASP16.bioRxiv : the preprint server for biology · 2025
    Article
  8. The ubiquitin E3 ligase BFAR promotes degradation of PNPLA3.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  9. Article
  10. Article
  11. Article
  12. Assessment of three-dimensional RNA structure prediction in CASP15.bioRxiv : the preprint server for biology · 2023
    Article
  13. Article
  14. Classification of domains in predicted structures of the human proteome.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  15. DPAM: A domain parser for AlphaFold models.Protein science : a publication of the Protein Society · 2023
    Article
  16. Article
  17. Review
  18. Article
  19. Co-component signal transduction systems: Fast-evolving virulence regulation cassettes discovered in enteric bacteria.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  20. 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

5 authors.

Lisa N KinchHoward Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, Texas, USA.ORCID 0000-0003-3041-2615
Jimin PeiHoward Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Andriy KryshtafovychGenome Center, University of California, Davis, California, USA.ORCID 0000-0001-5066-7178
R Dustin SchaefferDepartment of Biophysics and Biochemistry, University of Texas Southwestern Medical Center, Dallas, Texas, USA.ORCID 0000-0001-6502-1425
Nick V GrishinHoward Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

Funding

Prospective analysis to determine model accuracy performance and boundaries in the post-AlphaFold2 environmentR01GM100482 · NIGMS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI FIDELIS, KRZYSZTOF A · 2012 to 2025
$11.1M
Computational analysis of proteinsR35GM127390 · NIGMS · UT SOUTHWESTERN MEDICAL CENTER · PI GRISHIN, NICK V. · 2018 to 2022
$1.5M
Howard Hughes Medical InstituteNIGMS NIH HHS R01 GM100482NIGMS NIH HHS R35 GM127390
6 · The paper itself

Abstract

This report describes the tertiary structure prediction assessment of difficult modeling targets in the 14th round of the Critical Assessment of Structure Prediction (CASP14). We implemented an official ranking scheme that used the same scores as the previous CASP topology-based assessment, but combined these scores with one that emphasized physically realistic models. The top performing AlphaFold2 group outperformed the rest of the prediction community on all but two of the difficult targets considered in this assessment. They provided high quality models for most of the targets (86% over GDT_TS 70), including larger targets above 150 residues, and they correctly predicted the topology of almost all the rest. AlphaFold2 performance was followed by two manual Baker methods, a Feig method that refined Zhang-server models, two notable automated Zhang server methods (QUARK and Zhang-server), and a Zhang manual group. Despite the remarkable progress in protein structure prediction of difficult targets, both the prediction community and AlphaFold2, to a lesser extent, faced challenges with flexible regions and obligate oligomeric assemblies. The official ranking of top-performing methods was supported by performance generated PCA and heatmap clusters that gave insight into target difficulties and the most successful state-of-the-art structure prediction methodologies.

Indexed as

Models, MolecularProtein ConformationProtein FoldingSoftwareComputational BiologyDatabases, ProteinProteinsSequence Analysis, ProteinProteinsCASP14free modelinghomology modelingmachine learningprotein structure predictionstructural bioinformaticstopology structure modeling evaluation

Identifiers

PMID34240477
PMCPMC8616777

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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.