Structures of core eukaryotic protein complexes
Open accessPreprint — published as 10.1126/science.abm4805 · Paper page
Abstract
Protein-protein interactions play critical roles in biology, but despite decades of effort, the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions that have not yet been identified. Here, we take advantage of recent advances in proteome-wide amino acid coevolution analysis and deep-learning-based structure modeling to systematically identify and build accurate models of core eukaryotic protein complexes, as represented within the Saccharomyces cerevisiae proteome. We use a combination of RoseTTAFold and AlphaFold to screen through paired multiple sequence alignments for 8.3 million pairs of S. cerevisiae proteins and build models for strongly predicted protein assemblies with two to five components. Comparison to existing interaction and structural data suggests that these predictions are likely to be quite accurate. We provide structure models spanning almost all key processes in Eukaryotic cells for 104 protein assemblies which have not been previously identified, and 608 which have not been structurally characterized. One-sentence summary: We take advantage of recent advances in proteome-wide amino acid coevolution analysis and deep-learning-based structure modeling to systematically identify and build accurate models of core eukaryotic protein complexes.
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
Flagged first, then by citations
Cited by
Sorted by citations
AI-based structure prediction empowers integrative structural analysis of human nuclear pores
Mosalaganti, S., Obarska-Kosinska, A., Siggel, M., Taniguchi, R., Turoňová, B., Zimmerli, C. E., Buczak, K., Schmidt, F. H., Margiotta, E., Mackmull, M.-T., et al. (2022) · Science · doi:10.1126/science.abm9506 · Cited by 355 · see citing works
- Authors
- Humphreys, I. R., Pei, J., Baek, M., Krishnakumar, A., Anishchenko, I., Ovchinnikov, S., Zhang, J., Ness, T. J., Banjade, S., Bagde, S., Stancheva, V. G., Li, X.-H., Liu, K., Zheng, Z., Barrero, D. J., Roy, U., Fernández, I. S., Szakal, B., Branzei, D., Greene, E. C., Biggins, S., Keeney, S., Miller, E. A., Fromme, J. C., Hendrickson, T. L., Cong, Q., & Baker, D.
- Affiliations
- University of Washington (US)
- The University of Texas Southwestern Medical Center (US)
- Harvard University (US)
- Wayne State University (US)
- Cornell University (US)
- MRC Laboratory of Molecular Biology (GB)
- Memorial Sloan Kettering Cancer Center (US)
- Howard Hughes Medical Institute (US)
- Columbia University (US)
- St. Jude Children's Research Hospital (US)
- IFOM (IT)
- Corresponding author
- Humphreys, Ian R.
- Pei, Jimin
- Baek, Minkyung
- Krishnakumar, Aditya
- Cong, Qian
- Baker, David
- ORCID
- Humphreys, Ian R.
- Pei, Jimin
- Baek, Minkyung
- Krishnakumar, Aditya
- Anishchenko, Ivan
- Ovchinnikov, Sergey
- Zhang, Jing
- Ness, Travis J.
- Banjade, Sudeep
- Bagde, Saket
- Stancheva, Viktoriya G.
- Li, Xiao-Han
- Liu, Kaixian
- Zheng, Zhi
- Barrero, Daniel J.
- Roy, Upasana
- Fernández, Israel S.
- Branzei, Dana
- Greene, Eric C.
- Biggins, Sue
- Keeney, Scott
- Miller, Elizabeth A.
- Fromme, J. Christopher
- Hendrickson, Tamara L.
- Cong, Qian
- Baker, David
- Venue
- Science
- Publisher
- openRxiv
- Published
- 2021-09-30
- Volume
- 374
- Issue
- 6573
- Pages
- eabm4805-eabm4805
- ISSN
- 0036-8075 (ISSN-L)
- PMC
- PMC7612107
- OpenAlex
- W3212912034
- Cited by
- 541
- References
- 1
- Citations by year
- 2026: 58 · 2025: 82 · 2024: 124 · 2023: 117 · 2022: 146 · 2021: 12 · 2013: 1 · 2012: 1
- FWCI
- 31.39
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Discovery Research Portal (University of Dundee) (submittedVersion)
- Universitätsbibliographie, Universität Duisburg-Essen (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- Damon Runyon Cancer Research Foundation
- Washington Research Foundation
- Southwestern Medical Foundation
- Associazione Italiana per la Ricerca sul Cancro
- National Institutes of Health
- Medical Research Council (MRC_UP_1201/10)
- National Cancer Institute (R01CA221858, P30CA008748)
- National Institute of General Medical Sciences (R35GM136258, R35GM118026)
- National Institute of Allergy and Infectious Diseases (R21AI156595)
- National Institute of Neurological Disorders and Stroke (R35NS097333)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- protein-protein interaction, protein complexes, deep learning, coevolution, protein structure prediction, Saccharomyces cerevisiae
- MeSH terms
- Deep Learning, Acyltransferases, Computer Simulation, DNA Repair, Ligases, Membrane Proteins, Models, Molecular, Protein Conformation, Ribosomes, Saccharomyces cerevisiae, Protein Biosynthesis, Evolution, Molecular, Computational Biology, Chromosome Segregation, Proteome, Ubiquitin, Protein Interaction Mapping, Saccharomyces cerevisiae Proteins, Multiprotein Complexes, Homologous Recombination, Protein Interaction Maps
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