Evidence map›Paper›PMID 34595772›Full record

ArticleProteins2021

Assessing the accuracy of contact and distance predictions in CASP14.

Victoria Ruiz-Serra, Camila Pontes, Edoardo Milanetti, Andriy Kryshtafovych, Rosalba Lepore, Alfonso Valencia

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 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Using deep-learning predictions of inter-residue distances for model validation.Acta crystallographica. Section D, Structural biology · 2022
    Article
  7. Article
  8. 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

6 authors.

Victoria Ruiz-SerraBarcelona Supercomputing Center (BSC), Barcelona, Spain.ORCID 0000-0003-3991-0514
Camila PontesBarcelona Supercomputing Center (BSC), Barcelona, Spain.ORCID 0000-0002-8726-3641
Edoardo MilanettiDepartment of Physics, Sapienza Università di Roma, Rome, Italy.ORCID 0000-0002-3046-5170
Andriy KryshtafovychGenome Center, University of California, Davis, California, USA.ORCID 0000-0001-5066-7178
Rosalba LeporeBarcelona Supercomputing Center (BSC), Barcelona, Spain.ORCID 0000-0002-9481-2557
Alfonso ValenciaBarcelona Supercomputing Center (BSC), Barcelona, Spain.ORCID 0000-0002-8937-6789

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
NIGMS NIH HHS R01 GM100482
6 · The paper itself

Abstract

We present the results of the assessment of the intramolecular residue-residue contact and distance predictions from groups participating in the 14th round of the CASP experiment. The performance of contact prediction methods was evaluated with the measures used in previous CASPs, while distance predictions were assessed based on a new protocol, which considers individual distance pairs as well as the whole predicted distance matrix, using a graph-based framework. The results of the evaluation indicate that predictions by the tFold framework, TripletRes and DeepPotential were the most accurate in both categories. With regards to progress in method performance, the results of the assessment in contact prediction did not reveal any discernible difference when compared to CASP13. Arguably, this could be due to CASP14 FM targets being more challenging than ever before.

Indexed as

Amino Acid SequenceModels, MolecularProtein ConformationProteinsSoftwareComputational BiologyReproducibility of ResultsSequence Analysis, ProteinProteinsbenchmarkinCASP14community-wide experimentnumerical evaluation measuresprediction of residue-residue contact and distance

Identifiers

PMID34595772
PMCPMC8616839

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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.