Accuracy of AlphaFold models: Comparison with short N O contacts in atomic resolution protein crystal structures
Open accessPublished version of a preprint
Abstract
Artificial intelligence (AI) has revolutionized structural biology by predicting protein 3D structures with near-experimental accuracy. Here, short backbone N-O distances in high-resolution crystal structures were compared to those in three-dimensional models based on AI AlphaFold/ColabFold, specifically considering their estimated standard errors. Experimental and computationally modeled distances very often differ significantly, showing that these models' precision is inadequate to reproduce experimental results at high resolution. T-tests and normal probability plots showed that these computational methods predict atomic position standard errors 3.5-6 times bigger than experimental errors. SYNOPSIS: Positional standard errors in AI-based protein 3D models are 3.5-6 times larger than in atomic resolution crystal 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.
Integrity signals
Registry
Cites
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.
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
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works
Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants
Yin, R., Feng, B. Y., Varshney, A., & Pierce, B. G. (2021) · doi:10.1101/2021.10.23.465575 · Cited by 335 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Utilizing protein structure graph embeddings to predict the pathogenicity of missense variants
Danner, M., Begemann, M., Elbracht, M., Kurth, I., & Krause, J. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf097 · Cited by 2 · see citing works
- Authors
- Carugo, O.
- Affiliations
- University of Vienna (AT)
- Corresponding author
- Carugo, Oliviero
- ORCID
- Carugo, Oliviero
- Venue
- Computational Biology and Chemistry
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 110
- Pages
- 108069
- ISSN
- 1476-9271 (ISSN-L)
- OpenAlex
- W4393930591
- Cited by
- 6
- References
- 3
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 1
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computational Biology and Chemistry (publishedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Ministero dell’Istruzione, dell’Università e della Ricerca
- Università degli Studi di Pavia
- 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
- AlphaFold, ColabFold, protein structure prediction, model accuracy
- MeSH terms
- Artificial Intelligence, Models, Molecular, Protein Conformation, Proteins, Crystallography, X-Ray
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
