The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Net charge per residue modulates conformational ensembles of intrinsically disordered proteins
Open accessAbstract
Intrinsically disordered proteins (IDPs) adopt heterogeneous ensembles of conformations under physiological conditions. Understanding the relationship between amino acid sequence and conformational ensembles of IDPs can help clarify the role of disorder in physiological function. Recent studies revealed that polar IDPs favor collapsed ensembles in water despite the absence of hydrophobic groups—a result that holds for polypeptide backbones as well. By studying highly charged polypeptides, a different archetype of IDPs, we assess how charge content modulates the intrinsic preference of polypeptide backbones for collapsed structures. We characterized conformational ensembles for a set of protamines in aqueous milieus using molecular simulations and fluorescence measurements. Protamines are arginine-rich IDPs involved in the condensation of chromatin during spermatogenesis. Simulations based on the ABSINTH implicit solvation model predict the existence of a globule-to-coil transition, with net charge per residue serving as the discriminating order parameter. The transition is supported by quantitative agreement between simulation and experiment. Local conformational preferences partially explain the observed trends of polymeric properties. Our results lead to the proposal of a schematic protein phase diagram that should enable prediction of polymeric attributes for IDP conformational ensembles using easily calculated physicochemical properties of amino acid sequences. Although sequence composition allows the prediction of polymeric properties, interresidue contact preferences of protamines with similar polymeric attributes suggest that certain details of conformational ensembles depend on the sequence. This provides a plausible mechanism for specificity in the functions of IDPs.
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
Intrinsically unstructured proteins and their functions
Dyson, H. J., & Wright, P. E. (2005) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm1589 · Cited by 3,871
Intrinsic Disorder and Protein Function
Dunker, A. K., Brown, C. J., Lawson, J. D., Iakoucheva, L. M., & Obradović, Z. (2002) · Biochemistry · doi:10.1021/bi012159+ · Cited by 1,731
Intrinsic disorder as a mechanism to optimize allosteric coupling in proteins
Hilser, V. J., & Thompson, E. B. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0700329104 · Cited by 406
Disorder and Sequence Repeats in Hub Proteins and Their Implications for Network Evolution
Dosztányi, Z., Chen, J., Dunker, A. K., Simon, I., & Tompa, P. (2006) · Journal of Proteome Research · doi:10.1021/pr060171o · Cited by 341
Cited by
Alberti, S., Gladfelter, A., & Mittag, T. (2019) · Cell · doi:10.1016/j.cell.2018.12.035 · Cited by 2,856
Intrinsically Disordered Proteins and Intrinsically Disordered Protein Regions
Oldfield, C. J., & Dunker, A. K. (2014) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-072711-164947 · Cited by 1,057
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
Self-citationTemperature-dependent solvation modulates the dimensions of disordered proteins
Wuttke, R., Hofmann, H., Nettels, D., Borgia, M. B., Mittal, J., Best, R. B., & Schuler, B. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1313006111 · Cited by 192
Direct generation of protein conformational ensembles via machine learning
Janson, G., Valdes-Garcia, G., Heo, L., & Feig, M. (2023) · Nature Communications · doi:10.1038/s41467-023-36443-x · Cited by 192
AlphaFold2: A Role for Disordered Protein/Region Prediction?
Wilson, C. J., Choy, W.-Y., & Karttunen, M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23094591 · Cited by 173
Allosteric Modulators of Steroid Hormone Receptors: Structural Dynamics and Gene Regulation
Kumar, R., & McEwan, I. J. (2012) · Endocrine Reviews · doi:10.1210/er.2011-1033 · Cited by 135
Describing sequence–ensemble relationships for intrinsically disordered proteins
Mao, A. H., Lyle, N., & Pappu, R. V. (2013) · Biochemical Journal · doi:10.1042/bj20121346 · Cited by 135
Self-citationConformational buffering underlies functional selection in intrinsically disordered protein regions
González-Foutel, N. S., Glavina, J., Borcherds, W. M., Safranchik, M., Barrera-Vilarmau, S., Sagar, A., Estaña, A., Barozet, A., Garrone, N. A., Fernandez-Ballester, G., et al. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00811-w · Cited by 118
Self-citationBinding without folding – the biomolecular function of disordered polyelectrolyte complexes
Schuler, B., Borgia, A., Borgia, M. B., Heidarsson, P. O., Holmstrom, E. D., Nettels, D., & Sottini, A. (2020) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2019.12.006 · Cited by 91
A quantitative measure for protein conformational heterogeneity
Lyle, N., Das, R. K., & Pappu, R. V. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4812791 · Cited by 85
Self-citationHow multisite phosphorylation impacts the conformations of intrinsically disordered proteins
Jin, F., & Gräter, F. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1008939 · Cited by 77
Sequence-to-conformation relationships of disordered regions tethered to folded domains of proteins
Mittal, A., Holehouse, A. S., Cohan, M. C., & Pappu, R. V. (2018) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.05.012 · Cited by 72
Self-citationQuantifying the topography of the intrinsic energy landscape of flexible biomolecular recognition
Chu, X., Gan, L., Wang, E., & Wang, J. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1220699110 · Cited by 62
Warnet, X. L., Bakke Krog, H., Sevillano‐Quispe, O. G., Poulsen, H., & Kjaergaard, M. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.14842 · Cited by 50
Waszkiewicz, R., Michaś, A., Białobrzewski, M. K., Klepka, B. P., Cieplak-Rotowska, M. K., Staszałek, Z., Cichocki, B., Lisicki, M., Szymczak, P., & Niedzwiecka, A. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.4c00312 · Cited by 16
Metadata
- Authors
- Mao, A. H., Crick, S. L., Vitalis, A., Chicoine, C. L., & Pappu, R. V.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2010-04-19
- Volume
- 107
- Issue
- 18
- Pages
- 8183-8188
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2043308634
- Cited by
- 564
- References
- 4
- FWCI
- 11.34
- Type
- journal article
- Language
- en
- Open access
- green
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
