Cited by 79Protein Structure and DynamicsEnzyme Structure and FunctionMachine Learning in Bioinformatics

A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments

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Abriata, Luciano A., Tamò, Giorgio E., Dal Peraro, Matteo·2019·Proteins: Structure, Function, and Bioinformatics

Abstract

We present our assessment of tertiary structure predictions for hard targets in Critical Assessment of Structure Prediction round 13 (CASP13). The analysis includes (a) assignment and discussion of best models through scores‐aided visual inspection of models for each evaluation unit (EU); (b) ranking of predictors resulting from this evaluation and from global scores; and (c) evaluation of progress, state of the art, and current limitations of protein structure prediction. We witness a sizable improvement in tertiary structure prediction building on the progress observed from CASP11 to CASP12, with (a) top models reaching backbone RMSD <3 å for several EUs of size <150 residues, contributed by many groups; (b) at least one model that roughly captures global topology for all EUs, probably unprecedented in this track of CASP; and (c) even quite good models for full, unsplit targets. Better structure predictions are brought about mainly by improved residue‐residue contact predictions, and since this CASP also by distance predictions, achieved through state‐of‐the‐art machine learning methods which also progressed to work with slightly shallower alignments compared to CASP12. As we reach a new realm of tertiary structure prediction quality, new directions are proposed and explored for future CASPs: (a) dropping splitting into EUs, (b) rethinking difficulty metrics probably in terms of contact and distance predictions, (c) assessing also side chains for models of high backbone accuracy, and (d) assessing residue‐wise and possibly residue‐residue quality estimates.

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Crossref · 10.1002/prot.25787

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  • Existing protein structure prediction methods fall far short of atomic or experimental accuracy, especially when no homologous structure is available, limiting their utility for many biological applications.

    “Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available.”

    From Highly accurate protein structure prediction with AlphaFold (2021) · cites this paper as [13]

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References 21 · Cited by 79

Authors
Abriata, L. A., Tamò, G. E., & Dal Peraro, M.
Affiliations
  • SIB Swiss Institute of Bioinformatics (CH)
  • School of Life Sciences Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland
Corresponding author
Abriata, Luciano A.
Venue
Proteins: Structure, Function, and Bioinformatics
Publisher
Wiley
Published
2019-08-07
Volume
87
Issue
12
Pages
1100-1112
ISSN
  • 0887-3585 (ISSN-L)
  • 1097-0134
OpenAlex
W2962831078
Cited by
79
References
21
Citations by year
2026: 2 · 2025: 2 · 2024: 3 · 2023: 7 · 2022: 5 · 2021: 33 · 2020: 20 · 2019: 7
FWCI
5.38
Type
journal article
Language
en
Open access
Locations
  • Proteins Structure Function and Bioinformatics (publishedVersion)
  • PubMed (publishedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
Funders
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
protein tertiary structure prediction, CASP13, assessment units, residue-residue contact prediction, distance prediction, machine learning predictors
MeSH terms
Algorithms, Models, Molecular, Protein Conformation, Proteins, Software, Protein Folding, Computational Biology, Sequence Analysis, Protein, Databases, Protein

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