Evidence map›Paper›PMID 28940798›Full record

ArticleProteins2018

Protein structure prediction using Rosetta in CASP12.

Sergey Ovchinnikov, Hahnbeom Park, David E Kim, Frank DiMaio, David Baker

Abstract read
In one paragraph

Article in Proteins, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing 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

50 citing papers in PubMed.

  1. Review
  2. Article
  3. Antifungal Potential ofJournal of fungi (Basel, Switzerland) · 2026
    Article
  4. Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026
    Review
  5. ΦX174 bacteriophage viability predicted by protein biophysical modeling.bioRxiv : the preprint server for biology · 2025
    Article
  6. Article
  7. Article
  8. In Silico Identification of Novel Compounds as Anthelmintics AgainstAnimals : an open access journal from MDPI · 2025
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. DATA-DRIVEN LEARNING OF GEOMETRIC SCATTERING MODULES FOR GNNS.IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing · 2021
    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.

Sergey OvchinnikovDepartment of Biochemistry, University of Washington, Seattle, Washington.ORCID 0000-0003-2774-2744
Hahnbeom ParkDepartment of Biochemistry, University of Washington, Seattle, Washington.
David E KimInstitute for Protein Design, University of Washington, Seattle, Washington.
Frank DiMaioDepartment of Biochemistry, University of Washington, Seattle, Washington.ORCID 0000-0002-7524-8938
David BakerDepartment of Biochemistry, University of Washington, Seattle, Washington.

Funding

TRAINING IN MOLECULAR AND CELLULAR BIOLOGYT32GM007270 · NIGMS · UNIVERSITY OF WASHINGTON · PI RAIBLE, DAVID W · 1985 to 2020
$21.0M
High resolution structure modeling with RosettaR01GM092802 · NIGMS · UNIVERSITY OF WASHINGTON · PI BAKER, DAVID · 2010 to 2017
$2.3M
Howard Hughes Medical InstituteNIGMS NIH HHS R01 GM092802NIGMS NIH HHS T32 GM007270
6 · The paper itself

Abstract

We describe several notable aspects of our structure predictions using Rosetta in CASP12 in the free modeling (FM) and refinement (TR) categories. First, we had previously generated (and published) models for most large protein families lacking experimentally determined structures using Rosetta guided by co-evolution based contact predictions, and for several targets these models proved better starting points for comparative modeling than any known crystal structure-our model database thus starts to fulfill one of the goals of the original protein structure initiative. Second, while our "human" group simply submitted ROBETTA models for most targets, for six targets expert intervention improved predictions considerably; the largest improvement was for T0886 where we correctly parsed two discontinuous domains guided by predicted contact maps to accurately identify a structural homolog of the same fold. Third, Rosetta all atom refinement followed by MD simulations led to consistent but small improvements when starting models were close to the native structure, and larger but less consistent improvements when starting models were further away.

Indexed as

Models, MolecularProtein ConformationProtein FoldingAlgorithmsComputational BiologyCrystallography, X-RayHumansProteinsSequence Analysis, ProteinProteinsab initio predictionco-evolutionprotein structure predictionrefinementRosetta

Identifiers

PMID28940798
PMCPMC6802495

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Registered trials

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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.