Assessing the helical stability of polyXYs at the boundaries of intrinsically disordered regions with MD simulations
Open accessPublished version of a preprint
Abstract
Abstract Intrinsically disordered regions (IDRs) in proteins lack stable structure. By carrying many hydrophilic and charged residues, it prevents them from forming globular domains and contributes to their flexibility and accessibility. Naturally, regions with reduced amino acid composition (low complexity regions; LCRs) occur within IDRs. Disorder and low complexity in protein sequences are linked to various biological functions including phase separation, regulation, and molecular interactions, and mutations in these regions can contribute to several diseases including cancer. Understanding these biological properties requires examination of the structural properties of IDRs and the LCRs within, but their inherently dynamic nature requires specific approaches combining sequence analyses, structure predictions and molecular dynamics (MD) simulations. Here, we leverage our previous work where we identified that some types of LCRs combining two residues (polyXY) are frequent within IDRs and confer them with propensity to form helical conformation. We identify a significant accumulation of such polyXYs at the ends of IDRs following alpha-helices that start outside the IDR and may extend through the polyXY into the IDR, particularly from the N-terminal end of the IDR. MD simulations support the dynamic nature of such helical conformations. Our results suggest a mechanism by which evolutionary emergence of LCRs at IDR ends could provide proteins with flexible regions for fold-upon-binding.
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
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.
Cites
UCSF Chimera—A visualization system for exploratory research and analysis
Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20084 · Cited by 45,260 · see citing works
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 · see citing works
All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins
MacKerell, A. D., Bashford, D., Bellott, M., Dunbrack, R. L., Evanseck, J. D., Field, M. J., Fischer, S., Gao, J., Guo, H., Ha, S., et al. (1998) · The Journal of Physical Chemistry B · doi:10.1021/jp973084f · Cited by 14,270 · see citing works
Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1061 · Cited by 7,850 · see citing works
Biopython: freely available python tools for computational molecular biology and bioinformatics
Cock, P. J. A., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., et al. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp163 · Cited by 6,080 · see citing works
Liquid phase condensation in cell physiology and disease
Shin, Y., & Brangwynne, C. P. (2017) · Science · doi:10.1126/science.aaf4382 · Cited by 4,184 · see citing works
Tian, C., Kasavajhala, K., Belfon, K. A. A., Raguette, L. E., Huang, H., Migues, A. N., Bickel, J. D., Wang, Y., Pincay, J., Wu, Q., et al. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00591 · Cited by 2,556 · see citing works
Intrinsically disordered proteins in cellular signalling and regulation
Wright, P. E., & Dyson, H. J. (2014) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3920 · Cited by 2,371 · see citing works
Classification of Intrinsically Disordered Regions and Proteins
van der Lee, R., Buljan, M., Lang, B., Weatheritt, R. J., Daughdrill, G. W., Dunker, A. K., Fuxreiter, M., Gough, J., Gsponer, J., Jones, D. T., et al. (2014) · Chemical Reviews · doi:10.1021/cr400525m · Cited by 2,155 · see citing works
Varadi, M., Bertoni, D., Magana, P., Paramval, U., Pidruchna, I., Radhakrishnan, M., Tsenkov, M., Nair, S., Mirdita, M., Yeo, J., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad1011 · Cited by 2,052 · see citing works
Intrinsically Disordered Proteins in Human Diseases: Introducing the D 2 Concept
Uversky, V. N., Oldfield, C. J., & Dunker, A. K. (2008) · Annual Review of Biophysics · doi:10.1146/annurev.biophys.37.032807.125924 · Cited by 1,406 · see citing works
Developing a molecular dynamics force field for both folded and disordered protein states
Robustelli, P., Piana, S., & Shaw, D. E. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1800690115 · Cited by 1,047 · see citing works
Das, R. K., & Pappu, R. V. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1304749110 · Cited by 1,008 · see citing works
A series of PDB-related databanks for everyday needs
Touw, W. G., Baakman, C. A. B., Black, J., Beek, T. A. H. T., Krieger, E., Joosten, R. P., & Vriend, G. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1028 · Cited by 936 · see citing works
Emerging functions of the VCP/p97 AAA-ATPase in the ubiquitin system
Meyer, H. H., Bug, M., & Bremer, S. (2012) · Nature Cell Biology · doi:10.1038/ncb2407 · Cited by 803 · see citing works
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 · see citing works
Unusual biophysics of intrinsically disordered proteins.
Uversky, V. (2013) · Biochimica et Biophysica Acta · doi:10.1016/j.bbapap.2012.12.008 · Cited by 536 · see citing works
Tandem repeats mediating genetic plasticity in health and disease
Hannan, A. J. (2018) · Nature Reviews Genetics · doi:10.1038/nrg.2017.115 · Cited by 446 · see citing works
Babu, M. M. (2016) · Biochemical Society Transactions · doi:10.1042/bst20160172 · Cited by 408 · see citing works
VCP/p97-Mediated Unfolding as a Principle in Protein Homeostasis and Signaling
van den Boom, J., & Meyer, H. H. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.10.028 · Cited by 408 · see citing works
Relating sequence encoded information to form and function of intrinsically disordered proteins
Das, R. K., Ruff, K. M., & Pappu, R. V. (2015) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2015.03.008 · Cited by 400 · see citing works
Context-dependent secondary structure formation of a designed protein sequence
Minor, D. L., & Kim, P. S. (1996) · Nature · doi:10.1038/380730a0 · Cited by 393 · see citing works
Enhanced sampling in molecular dynamics.
Yang, Y., Shao, Q., Zhang, J., Yang, L.-J., & Gao, Y. (2019) · Journal of Chemical Physics · doi:10.1063/1.5109531 · Cited by 362 · see citing works
Critical assessment of protein intrinsic disorder prediction
Necci, M., Piovesan, D., Predictors, C., Hoque, M. T., Walsh, I., Iqbal, S., Vendruscolo, M., Sormanni, P., Wang, C., Raimondi, D., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01117-3 · Cited by 343 · see citing works
Structural Basis of the Translational Elongation Cycle
Voorhees, R. M., & Ramakrishnan, V. (2013) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-113009-092313 · Cited by 289 · see citing works
Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2
Alderson, T. R., Pritišanac, I., Kolarić, Đ., Moses, A. M., & Forman-Kay, J. D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2304302120 · Cited by 240 · see citing works
Helical Propensity in an Intrinsically Disordered Protein Accelerates Ligand Binding
Iešmantavičius, V., Dogan, J., Jemth, P., Teilum, K., & Kjærgaard, M. (2014) · Angewandte Chemie International Edition · doi:10.1002/anie.201307712 · Cited by 162 · see citing works
MobiDB: 10 years of intrinsically disordered proteins
Piovesan, D., Conte, A. D., Clementel, D., Monzón, A. M., Bevilacqua, M., Aspromonte, M. C., Iserte, J., Orti, F., Marino‐Buslje, C., & Tosatto, S. C. E. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1065 · Cited by 158 · see citing works
Recent Force Field Strategies for Intrinsically Disordered Proteins
Mu, J., Liu, H., Zhang, J., Luo, R., & Chen, H. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01175 · Cited by 122 · see citing works
Disentangling the complexity of low complexity proteins
Mier, P., Paladin, L., Tamana, S., Petrosian, S., Hajdu-Soltész, B., Urbanek, A., Gruca, A., Plewczyński, D., Grynberg, M., Bernadó, P., et al. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz007 · Cited by 97 · see citing works
Self-citationConformational ensembles of intrinsically disordered proteins and flexible multidomain proteins
Thomasen, F. E., & Lindorff‐Larsen, K. (2022) · Biochemical Society Transactions · doi:10.1042/bst20210499 · Cited by 92 · see citing works
Yang, J., Gao, M., Xiong, J., Su, Z., & Huang, Y. (2019) · Protein Science · doi:10.1002/pro.3718 · Cited by 85 · see citing works
Intrinsically disordered protein‐specific force field CHARMM 36 IDPSFF
Liu, H., Song, D., Lu, H. J., Luo, R., & Chen, H. (2018) · Chemical Biology & Drug Design · doi:10.1111/cbdd.13342 · Cited by 77 · see citing works
Comparison and Evaluation of Force Fields for Intrinsically Disordered Proteins
Rahman, M. U., Rehman, A. U., Liu, H., & Chen, H. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00762 · Cited by 74 · see citing works
Nwanochie, E., & Uversky, V. N. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20174186 · Cited by 69 · see citing works
Amino acid homorepeats in proteins
Chavali, S., Singh, A., Santhanam, B., & Babu, M. (2020) · Nature Reviews Chemistry · doi:10.1038/s41570-020-0204-1 · Cited by 58 · see citing works
Liu, H., Song, D., Zhang, Y., Yang, S., Luo, R., & Chen, H. (2019) · Physical Chemistry Chemical Physics · doi:10.1039/c9cp03434j · Cited by 54 · see citing works
Abriata, L. A., & Peraro, M. D. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.04.050 · Cited by 51 · see citing works
Crabtree, M., Borcherds, W. M., Poosapati, A., Shammas, S. L., Daughdrill, G. W., & Clarke, J. (2017) · Biochemistry · doi:10.1021/acs.biochem.7b00179 · Cited by 49 · see citing works
Liu, W., Chen, L., Yin, D., Yang, Z., Feng, J., Sun, Q., Lai, L., & Guo, X. (2023) · Nature Communications · doi:10.1038/s41467-023-41018-x · Cited by 37 · see citing works
A Perspective on the Prospective Use of AI in Protein Structure Prediction
Versini, R., Sritharan, S., Fas, B. A., Tubiana, T., Aimeur, S., Henri, J., Erard, M., Nüße, O., Andréani, J., Baaden, M., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01361 · Cited by 33 · see citing works
Evolutionary Study of Disorder in Protein Sequences
Kastano, K., Erdős, G., Mier, P., Alanis‐Lobato, G., Promponas, V. J., Dosztányi, Z., & Andrade-Navarro, M. A. (2020) · Biomolecules · doi:10.3390/biom10101413 · Cited by 30 · see citing works
Self-citationFolding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model
Sisk, T. R., & Robustelli, P. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2313360121 · Cited by 26 · see citing works
Hong, J.-Y., Wang, D., Xue, W., Yue, H.-W., Yang, H., Jiang, L., Wang, W., & Hu, H. (2019) · Scientific Reports · doi:10.1038/s41598-019-43926-9 · Cited by 20 · see citing works
Low Complexity Induces Structure in Protein Regions Predicted as Intrinsically Disordered
Gonçalves-Kulik, M., Mier, P., Kastano, K., Cortés, J., Bernadó, P., Schmid, F., & Andrade-Navarro, M. A. (2022) · Biomolecules · doi:10.3390/biom12081098 · Cited by 20 · see citing works
Self-citationZhu, K., Cai, Y., Si, X., Ye, Z., Gao, Y., Liu, C., Wang, R. Y., Ma, Z., Zhu, H., Zhang, L. N., et al. (2022) · Cell Death and Differentiation · doi:10.1038/s41418-022-01000-4 · Cited by 19 · see citing works
Bruley, A., Bitard‐Feildel, T., Callebaut, I., & Duprat, É. (2022) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26441 · Cited by 16 · see citing works
The sequence context in poly-alanine regions: structure, function and conservation
Mier, P., Elena‐Real, C. A., Cortés, J., Bernadó, P., & Andrade-Navarro, M. A. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac610 · Cited by 14 · see citing works
Self-citationPolyX2: Fast Detection of Homorepeats in Large Protein Datasets
Mier, P., & Andrade-Navarro, M. A. (2022) · Genes · doi:10.3390/genes13050758 · Cited by 14 · see citing works
Self-citationKhaled, M., Strodel, B., & Sayyed–Ahmad, A. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1143353 · Cited by 13 · see citing works
Basu, S., & Bahadur, R. P. (2019) · Journal of Structural Biology · doi:10.1016/j.jsb.2019.107428 · Cited by 12 · see citing works
One Step Closer to the Understanding of the Relationship IDR-LCR-Structure
Gonçalves-Kulik, M., Schmid, F., & Andrade-Navarro, M. A. (2023) · Genes · doi:10.3390/genes14091711 · Cited by 9 · see citing works
Self-citationMier, P., & Andrade-Navarro, M. A. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.09.011 · Cited by 6 · see citing works
Self-citationAnbo, H., Sakuma, K., Fukuchi, S., & Ota, M. (2023) · Biology · doi:10.3390/biology12020182 · Cited by 6 · see citing works
Flagged first, then by citations
- Authors
- Gonçalves-Kulik, M., Baptista, L. A., Schmid, F., & Andrade-Navarro, M. A.
- Affiliations
- Johannes Gutenberg University Mainz (DE)
- Max Planck Institute for Polymer Research (DE)
- Corresponding author
- Andrade-Navarro, Miguel A.
- Venue
- Computational and Structural Biotechnology Reports
- Published
- 2025-01
- Volume
- 2
- ISSN
- 2950-3639 (ISSN-L)
- OpenAlex
- W4410921172
- Cited by
- 0
- References
- 55
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computational and Structural Biotechnology Reports (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Gutenberg Open Science (cc-by)
- Funders
- Max-Planck-Gesellschaft
- Johannes Gutenberg University
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Keywords
- intrinsically disordered regions, low complexity regions, helical propensity, alpha-helix formation, protein flexibility, protein evolution, phase separation
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