Cited by 177Protein Structure and DynamicsMachine Learning in BioinformaticsEnzyme Structure and Function

Assessment of contact predictions in CASP12: Co‐evolution and deep learning coming of age

Open access
Schaarschmidt, Joerg, Monastyrskyy, Bohdan, Kryshtafovych, Andriy, Bonvin, Alexandre M.J.J.·2018·Proteins: Structure, Function, and Bioinformatics

Abstract

Following up on the encouraging results of residue‐residue contact prediction in the CASP11 experiment, we present the analysis of predictions submitted for CASP12. The submissions include predictions of 34 groups for 38 domains classified as free modeling targets which are not accessible to homology‐based modeling due to a lack of structural templates. CASP11 saw a rise of coevolution‐based methods outperforming other approaches. The improvement of these methods coupled to machine learning and sequence database growth are most likely the main driver for a significant improvement in average precision from 27% in CASP11 to 47% in CASP12. In more than half of the targets, especially those with many homologous sequences accessible, precisions above 90% were achieved with the best predictors reaching a precision of 100% in some cases. We furthermore tested the impact of using these contacts as restraints in ab initio modeling of 14 single‐domain free modeling targets using Rosetta. Adding contacts to the Rosetta calculations resulted in improvements of up to 26% in GDT_TS within the top five structures.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/prot.25407

Cites

71 of 71 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Recent CASPs have seen a dramatic reduction in the dependence of model accuracy on sequence alignment depth.

    “On the first of these, recent CASPs have seen a dramatic reduction in the dependence of model accuracy on sequence alignment depth, a key ingredient in the classical contact prediction methods that preceded deep learning33 and also a usual input into deep learning networks.”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Schaarschmidt, J., Monastyrskyy, B., Kryshtafovych, A., & Bonvin, A. M. J. J.
Affiliations
  • Faculty of Science ‐ Chemistry Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University Utrecht The Netherlands
  • University of California, Davis (US)
  • Utrecht University (NL)
Corresponding author
Bonvin, Alexandre M.J.J.
Venue
Proteins: Structure, Function, and Bioinformatics
Publisher
Wiley
Published
2017-11-07
Volume
86
Issue
S1
Pages
51-66
ISSN
  • 0887-3585 (ISSN-L)
  • 1097-0134
OpenAlex
W2765742208
Cited by
177
References
71
Citations by year
2025: 2 · 2024: 6 · 2023: 11 · 2022: 20 · 2021: 35 · 2020: 34 · 2019: 50 · 2018: 15 · 2017: 3
FWCI
11.39
Type
journal article
Language
en
Open access
Locations
  • Proteins Structure Function and Bioinformatics (publishedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
  • eScholarship (California Digital Library) (submittedVersion, cc-by-nc)
Funders
National Institute of General Medical Sciences (GM100482)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
Keywords
residue-residue contact prediction, caspase-12, coevolution, deep learning, ab initio protein structure prediction, Rosetta, GDT_TS
MeSH terms
Machine Learning, Algorithms, Humans, Models, Molecular, Protein Conformation, Proteins, Software, Protein Folding, Crystallography, X-Ray, Computational Biology, Databases, Protein, Protein Interaction Domains and Motifs

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review