Mining folded proteomes in the era of accurate structure prediction
Open accessPublished version of a preprint
Abstract
Protein structure fundamentally underpins the function and processes of numerous biological systems. Fold recognition algorithms offer a sensitive and robust tool to detect structural, and thereby functional, similarities between distantly related homologs. In the era of accurate structure prediction owing to advances in machine learning techniques and a wealth of experimentally determined structures, previously curated sequence databases have become a rich source of biological information. Here, we use bioinformatic fold recognition algorithms to scan the entire AlphaFold structure database to identify novel protein family members, infer function and group predicted protein structures. As an example of the utility of this approach, we identify novel, previously unknown members of various pore-forming protein families, including MACPFs, GSDMs and aerolysin-like proteins.
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
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · cited 1× · see citing works
“Today, in the era of accurate protein structure prediction [5,6], it is possible to build a reasonably accurate library comprising representative structures of all proteins in a proteome [7–9] (Fig 1A, 1B and 1C).”
BackgroundFast and accurate protein structure search with Foldseek
van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2022) · doi:10.1101/2022.02.07.479398 · Cited by 2,598 · see citing works
Fast and accurate protein structure search with Foldseek
van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01773-0 · Cited by 2,596 · see citing works
Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens
Behan, F. M., Iorio, F., Picco, G., Gonçalves, E., Beaver, C. M., Migliardi, G., Santos, R., Rao, Y., Sassi, F., Pinnelli, M., et al. (2019) · Nature · doi:10.1038/s41586-019-1103-9 · Cited by 1,338 · see citing works
Genetic and functional drivers of diffuse large B cell lymphoma
Reddy, A., Zhang, J., Davis, N. S., Moffitt, A. B., Love, C. L., Waldrop, A., Leppa, S., Pasanen, A., Meriranta, L., Karjalainen-Lindsberg, M.-L., et al. (2017) · Cell · doi:10.1016/j.cell.2017.09.027 · Cited by 1,104 · see citing works
Single-sequence protein structure prediction using language models from deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Rochereau, C., Church, G. M., Sorger, P. K., & AlQuraishi, M. (2021) · doi:10.1101/2021.08.02.454840 · Cited by 455 · cited 1× · see citing works
“Currently, this is dependent on MSAs for the detection of evolutionary covariance, however new single-sequence structure prediction methods are emerging that do not rely on sequence alignment [36].”
BackgroundSingle-sequence protein structure prediction using a language model and deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Floristean, C., Kharkar, A., Roy, K., Rochereau, C., Ahdritz, G., Zhang, J., Church, G. M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01432-w · Cited by 455 · see citing works
ECOD: an evolutionary classification of protein domains
Cheng, H., Schaeffer, R. D., Liao, Y., Kinch, L. N., Pei, J., Shi, S., Kim, B.-H., & Grishin, N. V. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003926 · Cited by 426 · cited 1× · see citing works
“Similarly, the AlphaFold database would benefit from application of other structure-based classification methods (such as adaptations to classification schemes of SCOPe [32] or ECOD [33]).”
MethodThe structural coverage of the human proteome before and after AlphaFold
Porta-Pardo, E., Ruiz-Serra, V., Valentini, S., & Valencia, A. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009818 · Cited by 131 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Novel machine learning approaches revolutionize protein knowledge
Bordin, N., Dallago, C., Heinzinger, M., Kim, S., Littmann, M., Rauer, C., Steinegger, M., Rost, B., & Orengo, C. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2022.11.001 · Cited by 72 · see citing works
The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database
Tang, Q.-Y., Ren, W., Wang, J., & Kaneko, K. (2022) · Molecular Biology and Evolution · doi:10.1093/molbev/msac197 · Cited by 31 · see citing works
- Authors
- Bayly-Jones, C., & Whisstock, J. C.
- Affiliations
- Monash University (AU)
- Corresponding author
- Whisstock, James C.
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2022-03-25
- Volume
- 18
- Issue
- 3
- Pages
- e1009930
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC8986115
- OpenAlex
- W4221071868
- Cited by
- 16
- References
- 10
- Citations by year
- 2026: 2 · 2024: 4 · 2023: 4 · 2022: 6
- FWCI
- 1.14
- Type
- journal article
- Language
- en
- Update notices
- new_version (2022-04-06)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Australian Research Council
- Australian Research Data Commons
- National Health and Medical Research Council of Australia
- 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 fold recognition, AlphaFold, protein structure prediction, pore-forming proteins, protein function prediction, structural similarity
- MeSH terms
- Machine Learning, Algorithms, Computational Biology, Proteome, Databases, Protein
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
