Critical assessment of methods of protein structure prediction (CASP)—Round XIII
Open accessAbstract
CASP (critical assessment of structure prediction) assesses the state of the art in modeling protein structure from amino acid sequence. The most recent experiment (CASP13 held in 2018) saw dramatic progress in structure modeling without use of structural templates (historically “ab initio” modeling). Progress was driven by the successful application of deep learning techniques to predict inter‐residue distances. In turn, these results drove dramatic improvements in three‐dimensional structure accuracy: With the proviso that there are an adequate number of sequences known for the protein family, the new methods essentially solve the long‐standing problem of predicting the fold topology of monomeric proteins. Further, the number of sequences required in the alignment has fallen substantially. There is also substantial improvement in the accuracy of template‐based models. Other areas—model refinement, accuracy estimation, and the structure of protein assemblies—have again yielded interesting results. CASP13 placed increased emphasis on the use of sparse data together with modeling and chemical crosslinking, SAXS, and NMR all yielded more mature results. This paper summarizes the key outcomes of CASP13. The special issue of PROTEINS contains papers describing the CASP13 assessments in each modeling category and contributions from the participants.
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AlphaFold2 demonstrated considerable improvement over previous methods in terms of providing routinely high accuracy.
“This system used a completely different model from our CASP13 entry31, and demonstrated a considerable improvement over previous methods in terms of providing routinely high accuracy29,30.”
From Highly accurate protein structure prediction for the human proteome (2021) · cites this paper as [29]
The PROGRESS IN CASP13 section reports a dramatic improvement in model accuracy over earlier CASP rounds, and TEMPLATE FREE MODELING says DeepMind’s A7D group had the most accurate results overall. Those passages concern CASP13 methods, not the different system in the citing sentence; they do not establish that AlphaFold2 routinely achieved high accuracy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe CASP assessment is carried out biennially using recently solved structures that have not been deposited in the PDB or publicly disclosed, making it a blind test, and it has long served as the gold-standard assessment for structure prediction accuracy.
“The CASP assessment is carried out biennially using recently solved structures that have not been deposited in the PDB or publicly disclosed so that it is a blind test for the participating methods, and has long served as the gold-standard assessment for the accuracy of structure prediction25,26.”
From Highly accurate protein structure prediction with AlphaFold (2021) · cites this paper as [26]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredIn recent years, there has been major progress in utilizing residue co-evolution information for predicting structures of individual proteins, based on rapid growth of experimental data and new ideas, combined with deep learning.
“In recent years, there has been major progress in utilizing the residue co-evolution information for predicting structures of individual proteins, based on the rapid growth of experimental data on proteins and new ideas on how to use it, combined with spectacular advances in computer science (deep learning) [30–33].”
From Challenges in protein docking (2020) · cites this paper as [31]
Such servers are common in prediction of individual protein structures.
“Such servers are common in prediction of individual protein structures [31].”
From Challenges in protein docking (2020) · cites this paper as [31]
In CASP13 (2018), with the introduction of effective deep learning methods, the trend line rose to above 60 on the GDT_TS scale, even for the most difficult targets, a major advance from the previous CASP.
“In CASP13 (2018)18, with the introduction of effective deep learning methods, the trend line rose to above 60 on the GDT_TS scale, even for the most difficult targets, a major advance from the previous CASP.”
From Critical assessment of methods of protein structure prediction (CASP)—Round <scp>XIV</scp> (2021) · cites this paper as [18]
One reason refinement methods have apparently stalled is that modeling methods increasingly incorporate a refinement component, so further improvement with similar methods has become more difficult.
“One reason, as noted after CASP1318, is that modeling methods increasingly incorporate a refinement component, so that further improvement with similar methods has become more difficult.”
From Critical assessment of methods of protein structure prediction (CASP)—Round <scp>XIV</scp> (2021) · cites this paper as [18]
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- Authors
- Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J.
- Affiliations
- University of California, Davis (US)
- SIB Swiss Institute of Bioinformatics (CH)
- University of London (GB)
- Institute for Bioscience and Biotechnology Research (US)
- Corresponding author
- Moult, John
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2019-10-23
- Volume
- 87
- Issue
- 12
- Pages
- 1011-1020
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- PMC
- PMC6927249
- OpenAlex
- W3199799076
- Cited by
- 506
- References
- 42
- Citations by year
- 2026: 33 · 2025: 36 · 2024: 45 · 2023: 79 · 2022: 88 · 2021: 155 · 2020: 66 · 2019: 4
- FWCI
- 25.90
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- BIROn (Birkbeck, University of London) (acceptedVersion)
- PubMed Central (acceptedVersion)
- edoc (University of Basel) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM100482)
- 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 structure prediction, CASP13, ab initio structure prediction, deep learning, template-based modeling, model refinement, accuracy estimation, protein assembly, chemical crosslinking, small-angle X-ray scattering, nuclear magnetic resonance
- MeSH terms
- Amino Acid Sequence, Humans, Models, Molecular, Protein Conformation, Proteins, X-Ray Diffraction, Computational Biology, Scattering, Small Angle
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 ›
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