Evidence map›Paper›PMID 31407380›Full record

ArticleProteins2019

Evaluation of template-based modeling in CASP13.

Tristan I Croll, Massimo D Sammito, Andriy Kryshtafovych, Randy J Read

Abstract read
In one paragraph

Article in Proteins, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing 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

41 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Three-Dimensional Modeling ofAntibodies (Basel, Switzerland) · 2025
    Article
  5. Slice'N'Dice: maximizing the value of predicted models for structural biologists.Acta crystallographica. Section D, Structural biology · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Illuminating the "Twilight Zone": Advances in Difficult Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
  10. Putting AlphaFold models to work with phenix.process_predicted_model and ISOLDE.Acta crystallographica. Section D, Structural biology · 2022
    Article
  11. Review
  12. Article
  13. Implications of AlphaFold2 for crystallographic phasing by molecular replacement.Acta crystallographica. Section D, Structural biology · 2022
    Review
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Current Approaches in Supersecondary Structures Investigation.International journal of molecular sciences · 2021
    Review
  20. Deep Learning-Based Advances in Protein Structure Prediction.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

4 authors.

Tristan I CrollDepartment of Haematology, University of Cambridge, Cambridge Institute for Medical Research, Cambridge, UK.ORCID 0000-0002-3514-8377
Massimo D SammitoDepartment of Haematology, University of Cambridge, Cambridge Institute for Medical Research, Cambridge, UK.ORCID 0000-0002-8346-9247
Andriy KryshtafovychGenome Center, University of California, Davis, California.ORCID 0000-0001-5066-7178
Randy J ReadDepartment of Haematology, University of Cambridge, Cambridge Institute for Medical Research, Cambridge, UK.ORCID 0000-0001-8273-0047

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
Marie Curie 790122NIGMS NIH HHS R01 GM100482Wellcome Trust 209407/Z/17/Z
6 · The paper itself

Abstract

Performance in the template-based modeling (TBM) category of CASP13 is assessed here, using a variety of metrics. Performance of the predictor groups that participated is ranked using the primary ranking score that was developed by the assessors for CASP12. This reveals that the best results are obtained by groups that include contact predictions or inter-residue distance predictions derived from deep multiple sequence alignments. In cases where there is a good homolog in the wwPDB (TBM-easy category), the best results are obtained by modifying a template. However, for cases with poorer homologs (TBM-hard), very good results can be obtained without using an explicit template, by deep learning algorithms trained on the wwPDB. Alternative metrics are introduced, to allow testing of aspects of structural models that are not addressed by traditional CASP metrics. These include comparisons to the main-chain and side-chain torsion angles of the target, and the utility of models for solving crystal structures by the molecular replacement method. The alternative metrics are poorly correlated with the traditional metrics, and it is proposed that modeling has reached a sufficient level of maturity that the best models should be expected to satisfy this wider range of criteria.

Indexed as

Protein ConformationProtein FoldingAlgorithmsComputational BiologyComputer SimulationDatabases, ProteinModels, MolecularProteinsSequence Analysis, ProteinProteinsCASPmolecular replacementstructure predictiontemplate-based modeling

Identifiers

PMID31407380
PMCPMC6851432

What OpenQuestion holds

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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.