The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Conformational Landscapes of a Class I Ribonucleotide Reductase Complex during Turnover Reveal Intrinsic Dynamics and Asymmetry
Open accessPreprint — published as 10.1038/s41467-025-57735-4
Abstract
Understanding the structural dynamics associated with enzymatic catalysis has been a long-standing goal of biochemistry. With the advent of modern cryo-electron microscopy (cryo-EM), it has become conceivable to redefine a protein’s structure as the continuum of all conformations and their distributions. However, capturing and interpreting this information remains challenging. Here, we use classification and deep-learning-based analyses to characterize the conformational heterogeneity of a class I ribonucleotide reductase (RNR) during turnover. By converting the resulting information into physically interpretable 2D conformational landscapes, we demonstrate that RNR continuously samples a wide range of motions while maintaining surprising asymmetry to regulate the two halves of its turnover cycle. Remarkably, we directly observe the appearance of highly transient conformations needed for catalysis, as well as the interaction of RNR with its endogenous reductant thioredoxin also contributing to the asymmetry and dynamics of the enzyme complex. Overall, this work highlights the role of conformational dynamics in regulating key steps in enzyme mechanisms.
Overview
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
Verification
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.
Citations
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
Flagged first, then by citations
Research works printed but not linked: 69
- 10.1146/annurev-biochem-013118-111843 · DOI
- Burnim, A. A. , Spence, M. A. , Xu, D. , Jackson, C. J. & Ando, N . Comprehensive phylogenetic analysis of the ribonucleotide reductase family reveals an ancestral clade. Elife 11, (2022). · DOI
- 10.1002/pro.4483 · DOI
- 10.1126/science.271.5248.477 · DOI
- Ruskoski, T. B. & Boal, A. K . The periodic table of ribonucleotide reductases. J. Biol. Chem. 297, 101137 (2021). · DOI
- 10.1146/annurev-biochem-061408-095817 · DOI
- 10.1021/acs.biochem.0c00001 · DOI
- 10.1126/science.aba6794 · DOI
- 10.1021/ja207455k · DOI
- 10.1074/jbc.M117.783092 · DOI
- 10.1021/jacs.5b09189 · DOI
- 10.1021/ja076459b · DOI
- 10.1021/bi010651n · DOI
- 10.1021/ja054991y · DOI
- 10.1016/j.jmb.2006.03.035 · DOI
- 10.1021/ja0685607 · DOI
- 10.1021/ja057776q · DOI
- 10.1016/j.ymeth.2016.02.007 · DOI
- 10.1038/s41592-020-01049-4 · DOI
- Punjani, A. & Fleet, D. J . 3D variability analysis: Resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM. J. Struct. Biol. 213, 107702 (2021). · DOI
- 10.1038/s41592-021-01220-5 · DOI
- 10.1093/micmic/ozad067.518 · DOI
- 10.1038/s41592-023-02031-6 · DOI
- 10.1101/2024.05.30.596729 · DOI · Paper page
- Jeon, M. , et al. CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM. arXiv [cs.CV] (2024).
- 10.1038/s41594-023-01078-5 · DOI
- Wang, J. et al. Catalytic trajectory of a dimeric nonribosomal peptide synthetase subunit with an inserted epimerase domain. Nat. Commun. 13, 592 (2022). · DOI
- 10.1038/s41594-023-01105-5 · DOI
- 10.1038/s41467-023-43145-x · DOI
- 10.1038/s41586-022-05366-w · DOI
- 10.1021/bi401056e · DOI
- 10.1021/bi8012559 · DOI
- 10.1038/s41587-020-00801-7 · DOI
- 10.1021/bi201925t · DOI
- 10.1021/bi00135a009 · DOI
- 10.1016/S0969-2126(97)00259-1 · DOI
- 10.1021/bi034374r · DOI
- 10.1021/ja201640n · DOI
- 10.1021/ja076043y · DOI
- 2021, Detection of catalytically linked conformational changes in wild-type class Ia ribonucleotide reductase using reaction-induced FTIR spectroscopy, J. Phys. Chem. B, 125, 8362, 10.1021/acs.jpcb.1c03038 · DOI
- 10.1038/s41467-019-10568-4 · DOI
- 10.1101/2021.10.04.463034 · DOI · Paper page
- 10.1021/ar4000407 · DOI
- 10.1074/jbc.M809338200 · DOI · Paper page
- 10.1074/jbc.M112.413427 · DOI
- 10.1016/S0021-9258(18)97742-2 · DOI
- 10.1021/bi00155a029 · DOI
- 2023, Ensemble Reweighting Using Cryo-EM Particle Images, J. Phys. Chem. B, 127, 5410, 10.1021/acs.jpcb.3c01087 · DOI
- 10.1073/pnas.1800356115 · DOI
- 10.1038/s41467-022-29332-2 · DOI
- 10.1038/nmeth.4169 · DOI · Paper page
- 10.1042/BCJ20210708 · DOI
- 10.1038/nmeth.4193 · DOI
- 10.1038/s41592-019-0575-8 · DOI
- 10.1038/s41592-020-00990-8 · DOI
- 10.1107/S2059798319011471/HTTPS://GOLDBOOK.IUPAC.ORG/E02002.HTML · DOI
- 10.1107/S0907444910007493 · DOI · Paper page
- 10.1002/PRO.4792 · DOI
- Sanchez-Garcia, R. et al. DeepEMhancer: a deep learning solution for cryo-EM volume post-processing. Commun Biol 4, 874 (2021). · DOI
- 10.1038/s41596-022-00763-x · DOI
- Scott, D. W. Multivariate Density Estimation: Theory, Practice, and Visualization. (John Wiley & Sons, 2015). · DOI · Paper page
- 10.1021/acs.jcim.3c01153 · DOI
- 10.1021/ACS.JCTC.9B00591/SUPPL_FILE/CT9B00591_SI_002.ZIP · DOI
- 10.1021/jz501780a · DOI
- 10.1021/acs.jcim.0c01390 · DOI
- 10.1021/acs.jctc.0c00194 · DOI
- 10.1021/acs.jcim.5b00674 · DOI
- Barca, G. M. J. et al. Recent developments in the general atomic and molecular electronic structure system. J. Chem. Phys. 152, 154102 (2020).
- 10.1021/jacs.0c04325 · DOI
Metadata
- Authors
- Xu, D., Thomas, W. C., Burnim, A. A., & Ando, N.
- Affiliations
- Cornell University (US)
- Princeton University (US)
- Corresponding author
- Ando, Nozomi
- Venue
- Nature Communications
- Publisher
- openRxiv
- Published
- 2024-06-17
- Volume
- 16
- Issue
- 1
- Pages
- 2458-2458
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC11903788
- OpenAlex
- W4408367378
- Cited by
- 10
- References
- 1
- Citations by year
- 2026: 6 · 2025: 3 · 2024: 1
- FWCI
- 2.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (1942668)
- U.S. Department of Health and Human Services (U24 GM129539)
- Simons Foundation (SF 349247)
- National Institutes of Health
- National Institute of General Medical Sciences (U24GM129539)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Advanced Electron Microscopy Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- class I ribonucleotide reductase, conformational dynamics, conformational landscape, enzyme turnover, thioredoxin, enzyme mechanism, protein complex dynamics, deep learning classification
- MeSH terms
- Models, Molecular, Protein Conformation, Ribonucleotide Reductases, Thioredoxins, Cryoelectron Microscopy
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