PreprintCited by 541Protein Structure and DynamicsMachine Learning in BioinformaticsBioinformatics and Genomic Networks

Structures of core eukaryotic protein complexes

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Humphreys, Ian R., Pei, Jimin, Baek, Minkyung, Krishnakumar, Aditya, Anishchenko, Ivan, Ovchinnikov, Sergey, Zhang, Jing, Ness, Travis J. +19 more·2021·Science

Preprint — 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.

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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
Venue
Science
Publisher
openRxiv
Published
2021-09-30
Volume
374
Issue
6573
Pages
eabm4805-eabm4805
ISSN
0036-8075 (ISSN-L)
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
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

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