The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Intrinsically Disordered Proteins and Their “Mysterious” (Meta)Physics
Open accessAbstract
Recognition of the natural abundance and functional importance of intrinsically disordered proteins (IDPs) and hybrid proteins containing ordered and intrinsically disordered protein regions (IDPRs) is changing protein science. IDPs and IDPRs; i.e., functional proteins and protein regions without unique structures, are commonly found in all organisms, where they have crucial roles in various biological processes. Disorder-based functionality complements functions of ordered proteins and domains. However, by virtue of their existence, IDPs/IDPRs, which are characterized by the remarkable conformational flexibility and structural plasticity, break multiple rules elaborated over the years to explain structure, folding, and functionality of well-folded proteins with unique structures. Despite the general believe, which dominated in protein science for more than a century, that unique biological functions of proteins require unique 3D-structures, structure-less IDPs/IDPRs are functional, being able to perform impossible tricks and to be engaged in biological activities, which are improbable for ordered proteins. With their exceptional spatio-temporal heterogeneity and high conformational flexibility, IDPs/IDPRs represent complex systems that act at the edge of chaos and are specifically tunable by various means. In this article, some of the wanders of intrinsic disorder are discussed as illustrations of their ‘mysterious’ (meta)physics.
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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
A draft map of the human proteome
Kim, M.-S., Pinto, S. M., Getnet, D., Nirujogi, R. S., Manda, S. S., Chaerkady, R., Madugundu, A. K., Kelkar, D. S., Isserlin, R., Jain, S., et al. (2014) · Nature · doi:10.1038/nature13302 · Cited by 2,198
Kato, M., Han, T. W., Xie, S., Shi, K., Du, X., Wu, L. C., Mirzaei, H., Goldsmith, E. J., Longgood, J., Pei, J., et al. (2012) · Cell · doi:10.1016/j.cell.2012.04.017 · Cited by 2,012
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 · cited 2×
“Consequently, the list of experimentally validated IDPs was extended first to 157 proteins [41] and later to more than 200 proteins containing at least one IDPR of 30 consecutive residues or longer [7, 29, 30].”
BackgroundProteoform: a single term describing protein complexity
The Consortium for Top Down Proteomics, Smith, L. M., & Kelleher, N. L. (2013) · Nature Methods · doi:10.1038/nmeth.2369 · Cited by 1,528 · cited 2×
“Therefore, intrinsic disorder represents a cornerstone of the proteoform concept [94] introduced to explain an important observation that the complexity of a biological system is primarily determined not by the genome size, but by its proteome size [95].”
BackgroundEvidence for a Partially Folded Intermediate in α-Synuclein Fibril Formation
Uversky, V. N., Li, J., & Fink, A. L. (2001) · Journal of Biological Chemistry · doi:10.1074/jbc.m010907200 · Cited by 1,096
Self-citationShowing your ID: intrinsic disorder as an ID for recognition, regulation and cell signaling
Uversky, V. N., Oldfield, C. J., & Dunker, A. K. (2005) · Journal of Molecular Recognition · doi:10.1002/jmr.747 · Cited by 847
Self-citationExtreme disorder in an ultrahigh-affinity protein complex
Borgia, A., Borgia, M. B., Bugge, K., Kissling, V. M., Heidarsson, P. O., Fernandes, C. B., Sottini, A., Soranno, A., Buholzer, K. J., Nettels, D., et al. (2018) · Nature · doi:10.1038/nature25762 · Cited by 653
Folding funnels, binding funnels, and protein function
Tsai, C., Kumar, S., Ma, B., & Nussinov, R. (1999) · Protein Science · doi:10.1110/ps.8.6.1181 · Cited by 648
Folding funnels and binding mechanisms
Ma, B., Kumar, S., Tsai, C.-J., & Nussinov, R. (1999) · Protein Engineering, Design and Selection · doi:10.1093/protein/12.9.713 · Cited by 594
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Utilizing graph machine learning within drug discovery and development
Gaudelet, T., Day, B., Jamasb, A. R., Soman, J., Regep, C., Liu, G., Hayter, J. B. R., Vickers, R., Roberts, C., Tang, J., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab159 · Cited by 313
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
Networks of Networks: An Essay on Multi-Level Biological Organization
Uversky, V. N., & Giuliani, A. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.706260 · Cited by 28
Self-citationProtein intrinsically disordered regions have a non-random, modular architecture
McConnell, B. S., & Parker, M. W. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad732 · Cited by 17
The pathogen-encoded signalling receptor Tir exploits host-like intrinsic disorder for infection
Vieira, M. F. M., Hernandez, G., Zhong, Q., Arbesú, M., Veloso, T., Gomes, T., Martins, M. L., Monteiro, H., Frazão, C., Frankel, G., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-05856-9 · Cited by 14
Role of Intrinsically Disordered Regions in Acceleration of Protein–Protein Association
Misiura, M. M., & Kolomeisky, A. B. (2020) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.9b08793 · Cited by 10
Peng, J., Svetec, N., & Zhao, L. (2022) · Molecular Biology and Evolution · doi:10.1093/molbev/msab350 · Cited by 10
Description of conformational ensembles of disordered proteins by residue-local probabilities
Bastida, A., Zúñiga, J., Miguel, B., & Soler, M. A. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp05970c · Cited by 3
Montaño‐Silva, P., Callejas‐Negrete, O. A., Pereira‐Santana, A., & Verdín, J. (2025) · The FEBS Journal · doi:10.1111/febs.70020 · Cited by 3
Understanding the Energy Landscape of Intrinsically Disordered Protein Ensembles
Viegas, R. G., Martins, I. B. S., & Leite, V. B. P. (2024) · doi:10.1101/2024.01.04.574178
Metadata
- Authors
- Uversky, V. N.
- Venue
- Frontiers in Physics
- Publisher
- Frontiers Media SA
- Published
- 2019-02-07
- Volume
- 7
- ISSN
- 2296-424X (ISSN-L)
- OpenAlex
- W2912126500
- Cited by
- 532
- References
- 13
- FWCI
- 20.34
- Type
- journal article
- Language
- en
- Open access
- gold
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