The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Pervasive, conserved secondary structure in highly charged protein regions
Open accessPreprint — published as 10.1371/journal.pcbi.1011565
Abstract
Understanding how protein sequences confer function remains a defining challenge in molecular biology. Two approaches have yielded enormous insight yet are often pursued separately: structure-based, where sequence-encoded structures mediate function, and disorder-based, where sequences dictate physicochemical and dynamical properties which determine function in the absence of stable structure. Here we study highly charged protein regions (>40% charged residues), which are routinely presumed to be disordered. Using recent advances in structure prediction and experimental structures, we show that roughly 40% of these regions form well-structured helices. Features often used to predict disorder—high charge density, low hydrophobicity, low sequence complexity, and evolutionarily varying length—are also compatible with solvated, variable-length helices. We show that a simple composition classifier predicts the existence of structure far better than well-established heuristics based on charge and hydropathy. We show that helical structure is more prevalent than previously appreciated in highly charged regions of diverse proteomes and characterize the conservation of highly charged regions. Our results underscore the importance of integrating, rather than choosing between, structure- and disorder-based approaches.
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
AlphaFold and Implications for Intrinsically Disordered Proteins
Ruff, K. M., & Pappu, R. V. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167208 · Cited by 635 · cited 3×
“The first is to impute disorder to residues with a low AlphaFold confidence score [37]; the second is to ascribe disorder to regions with high-confidence coil (e.”
Influential
Flagged first, then by citations
Research works printed but not linked: 49
- 2012, Structural biology, Versatility from protein disorder. Science, 337, 1460
- 10.1002/1097-0134(20001115)41:3<415::AID-PROT130>3.0.CO;2-7 · DOI
- 10.1073/pnas.1304749110 · DOI
- 10.1016/j.febslet.2004.09.036 · DOI
- 10.1002/1097-0134(20010101)42:1<38::AID-PROT50>3.0.CO;2-3 · DOI
- 10.1016/0097-8485(94)85023-2 · DOI
- 10.1016/j.molcel.2016.05.042 · DOI
- 10.1186/1471-2105-15-S9-S4 · DOI
- 2015, A theoretical method to compute sequence dependent configurational properties in charged polymers and proteins, J Chem Phys, 143, 085101, 10.1063/1.4929391 · DOI
- 10.1073/pnas.0911107107 · DOI
- 10.7554/eLife.46883 · DOI
- 10.1073/pnas.1516277113 · DOI
- 2021, Charge Segregation in the Intrinsically Disordered Region Governs VRN1 and DNA Liquid-like Phase Separation Robustness, J Mol Biol, 433, 167269, 10.1016/j.jmb.2021.167269 · DOI
- 10.1016/0097-8485(93)85006-X · DOI
- 10.1016/j.cell.2015.10.040 · DOI
- 10.1016/j.cell.2012.04.017 · DOI · Paper page
- 10.1016/j.cell.2013.10.033 · DOI
- 10.1038/nrm.2017.7 · DOI
- 10.1016/j.molcel.2015.01.013 · DOI · Paper page
- 10.1073/pnas.1504822112 · DOI · Paper page
- 10.1016/j.cell.2015.09.015 · DOI
- 2022, Charged sequence motifs increase the propensity towards liquid-liquid phase separation, FEBS Lett, 596, 1013, 10.1002/1873-3468.14294 · DOI
- 10.1016/j.jmb.2018.06.014 · DOI
- 10.1016/j.cell.2018.06.006 · DOI
- 2020, Disentangling the complexity of low complexity proteins, Brief Bioinform, 21, 458, 10.1093/bib/bbz007 · DOI
- 10.2139/ssrn.4002760 · DOI
- 10.1038/nsmb.1429 · DOI
- 10.1385/CBB:35:3:275 · DOI
- 10.1016/j.cell.2013.09.023 · DOI
- 10.1073/pnas.0806256105 · DOI
- 2018, Helical Polyampholyte Sequences Have Unique Thermodynamic Properties, J Phys Chem B, 122, 11784, 10.1021/acs.jpcb.8b08344 · DOI
- 2018, Waggawagga-CLI: A command-line tool for predicting stable single α-helices (SAH-domains), and the SAH-domain distribution across eukaryotes, PLoS One, 13, e0191924, 10.1371/journal.pone.0191924 · DOI
- Fossat MJ , Posey AE , Pappu RV. Uncovering the Contributions of Charge Regulation to the Stability of Single Alpha Helices. Chemphyschem. 2023; e202200746. · DOI
- 10.3389/fphy.2019.00010 · DOI
- 10.1101/2022.01.06.475253 · DOI
- 2021, Quantifying charge state heterogeneity for proteins with multiple ionizable residues, Biophys J, 120, 5438, 10.1016/j.bpj.2021.11.2886 · DOI
- 10.1038/nchembio.1739 · DOI
- 2021, A structural inventory of native ribosomal ABCE1-43S pre-initiation complexes, EMBO J, 40, e105179, 10.15252/embj.2020105179 · DOI
- 10.1016/j.bpj.2015.08.015 · DOI
- 10.1038/s41467-021-24773-7 · DOI
- 10.1038/s41592-022-01488-1 · DOI · Paper page
- 10.1016/S0006-3495(98)77529-0 · DOI
- 10.1093/database/baz022 · DOI
- 10.1002/prot.10150 · DOI
- CAID Predictors, DisProt Curators, Tosatto SCE, 2021, Critical assessment of protein intrinsic disorder prediction, Nat Methods, 18, 472, 10.1038/s41592-021-01117-3 · DOI · Paper page
- 10.1016/j.sbi.2018.09.008 · DOI
- 10.1073/pnas.1712061115 · DOI
- 2022, Quantifying shifts in natural selection on codon usage between protein regions: a population genetics approach, BMC Genomics, 23, 408, 10.1186/s12864-022-08635-0 · DOI
- 10.1186/1471-2105-5-113 · DOI
Metadata
- Authors
- Triandafillou, C. G., Pan, R. W., Dinner, A. R., & Drummond, D. A.
- Affiliations
- University of Pennsylvania (US)
- California Institute of Technology (US)
- University of Chicago (US)
- Corresponding author
- Drummond, D. Allan
- Venue
- PLoS Computational Biology
- Publisher
- openRxiv
- Published
- 2023-02-15
- Volume
- 19
- Issue
- 10
- Pages
- e1011565-e1011565
- ISSN
- 1553-734X (ISSN-L)
- PMC
- PMC10602382
- OpenAlex
- W4387665886
- Cited by
- 4
- References
- 2
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 1
- FWCI
- 0.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- Knowledge@UChicago (University of Chicago) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- University of Chicago (cc-by)
- Funders
- Damon Runyon Cancer Research Foundation (DRG-2465-22)
- University of Chicago
- National Institutes of Health
- Directorate for Biological Sciences
- U.S. Army (W911NF-14-1-0411)
- National Institute of General Medical Sciences (R35GM136381, GM144278, R01GM127406, GM127406, R35GM144278)
- Biological and Physical Sciences Division
- Army Research Office (W911NF-14-1-0411, W911NF-14-1, W911NF)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- intrinsically disordered regions, helical conformation, protein secondary structure, sequence-based structure prediction, charged residues, hydropathy, sequence complexity, protein evolution
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