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Conformational ensembles of the human intrinsically disordered proteome

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Tesei, Giulio, Trolle, Anna Ida, Jonsson, Nicolas, Betz, Johannes, Knudsen, Frederik E., Pesce, Francesco, Johansson, Kristoffer E., Lindorff-Larsen, Kresten·2024·Nature
DOI 10.1038/s41586-023-07004-5
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  1. Holehouse, A. S. & Kragelund, B. B. The molecular basis for cellular function of intrinsically disordered protein regions. Nat. Rev. Mol. Cell Biol. https://doi.org/10.1038/s41580-023-00673-0 (2023). · DOI · Paper page
  2. Ghafouri, H. et al. PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins. Nucleic Acids Res. 52, D536–D544 (2024). · DOI
  3. Tesei, G., Schulze, T. K., Crehuet, R. & Lindorff-Larsen, K. Accurate model of liquid–liquid phase behavior of intrinsically disordered proteins from optimization of single-chain properties. Proc. Natl Acad. Sci. USA 118, e2111696118 (2021). · DOI · Paper page
  4. Tesei, G. & Lindorff-Larsen, K. Improved predictions of phase behaviour of intrinsically disordered proteins by tuning the interaction range. Open Res. Europe 2, 94 (2023). · DOI
  5. Alderson, T. R., Pritišanac, I., Kolarić, D., Moses, A. M. & Forman-Kay, J. D. Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2. Proc. Natl Acad. Sci. USA 120, e2304302120 (2023). · DOI · Paper page
  6. Piovesan, D., Monzon, A. M. & Tosatto, S. C. E. Intrinsic protein disorder and conditional folding in AlphaFoldDB. Protein Sci. 31, e4466 (2022). · DOI · Paper page
  7. Thomasen, F. E. & Lindorff-Larsen, K. Conformational ensembles of intrinsically disordered proteins and flexible multidomain proteins. Biochem. Soc. Trans. 50, 541–554 (2022). · DOI
  8. Das, R. K., Huang, Y., Phillips, A. H., Kriwacki, R. W. & Pappu, R. V. Cryptic sequence features within the disordered protein p27Kip1 regulate cell cycle signaling. Proc. Natl Acad. Sci. USA 113, 5616–5621 (2016). · DOI
  9. Zheng, W. et al. Inferring properties of disordered chains from FRET transfer efficiencies. J. Chem. Phys. 148, 123329 (2018). · DOI
  10. Sherry, K. P., Das, R. K., Pappu, R. V. & Barrick, D. Control of transcriptional activity by design of charge patterning in the intrinsically disordered ram region of the notch receptor. Proc. Natl Acad. Sci. USA 114, E9243–E9252 (2017). · DOI
  11. Riback, J. A. et al. Stress-triggered phase separation is an adaptive, evolutionarily tuned response. Cell 168, 1028–1040 (2017). · DOI · Paper page
  12. Bremer, A. et al. Deciphering how naturally occurring sequence features impact the phase behaviours of disordered prion-like domains. Nat. Chem. 14, 196–207 (2022). · DOI
  13. Ibrahim, A. Y. et al. Intrinsically disordered regions that drive phase separation form a robustly distinct protein class. J. Biol. Chem. 299, 102801 (2023). · DOI
  14. Teilum, K., Olsen, J. G. & Kragelund, B. B. Globular and disordered—the non-identical twins in protein-protein interactions. Front. Mol. Biosci. 2, 40 (2015). · DOI
  15. Staller, M. V. et al. Directed mutational scanning reveals a balance between acidic and hydrophobic residues in strong human activation domains. Cell Syst. 13, 334–345 (2022).
  16. Sabari, B. R., Dall’Agnese, A. & Young, R. A. Biomolecular condensates in the nucleus. Trends Biochem. Sci. 45, 961–977 (2020). · DOI
  17. Greig, J. A. et al. Arginine-enriched mixed-charge domains provide cohesion for nuclear speckle condensation. Mol. Cell 77, 1237–1250 (2020). · DOI
  18. Chang, F. T. M. et al. PML bodies provide an important platform for the maintenance of telomeric chromatin integrity in embryonic stem cells. Nucleic Acids Res. 41, 4447–4458 (2013). · DOI
  19. Lyons, H. et al. Functional partitioning of transcriptional regulators by patterned charge blocks. Cell 186, 327–345 (2023). · DOI
  20. Rostam, N. et al. CD-CODE: crowdsourcing condensate database and encyclopedia. Nat. Methods 20, 673–676 (2023). · DOI
  21. Nott, T. J. et al. Phase transition of a disordered nuage protein generates environmentally responsive membraneless organelles. Mol. Cell 57, 936–947 (2015). · DOI · Paper page
  22. Pak, C. W. et al. Sequence determinants of intracellular phase separation by complex coacervation of a disordered protein. Mol. Cell 63, 72–85 (2016). · DOI
  23. Das, R. K. & Pappu, R. V. Conformations of intrinsically disordered proteins are influenced by linear sequence distributions of oppositely charged residues. Proc. Natl Acad. Sci. USA 110, 13392–13397 (2013). · DOI
  24. Sawle, L. & Ghosh, K. A theoretical method to compute sequence dependent configurational properties in charged polymers and proteins. J. Chem. Phys. 143, 085101 (2015). · DOI · Paper page
  25. Zarin, T. et al. Proteome-wide signatures of function in highly diverged intrinsically disordered regions. eLife 8, e46883 (2019). · DOI
  26. Zheng, W., Dignon, G., Brown, M., Kim, Y. C. & Mittal, J. Hydropathy patterning complements charge patterning to describe conformational preferences of disordered proteins. J. Phys. Chem. Lett. 11, 3408–3415 (2020). · DOI
  27. Huihui, J. & Ghosh, K. Intrachain interaction topology can identify functionally similar intrinsically disordered proteins. Biophys. J. 120, 1860–1868 (2021). · DOI
  28. Yamazaki, H., Takagi, M., Kosako, H., Hirano, T. & Yoshimura, S. H. Cell cycle-specific phase separation regulated by protein charge blockiness. Nat. Cell Biol. 24, 625–632 (2022). · DOI
  29. Mao, A. H., Crick, S. L., Vitalis, A., Chicoine, C. L. & Pappu, R. V. Net charge per residue modulates conformational ensembles of intrinsically disordered proteins. Proc. Natl Acad. Sci. USA 107, 8183–8188 (2010). · DOI · Paper page
  30. Lin, Y.-H. & Chan, H. S. Phase separation and single-chain compactness of charged disordered proteins are strongly correlated. Biophys. J. 112, 2043–2046 (2017). · DOI
  31. Cohan, M. C., Shinn, M. K., Lalmansingh, J. M. & Pappu, R. V. Uncovering non-random binary patterns within sequences of intrinsically disordered proteins. J. Mol. Biol. 434, 167373 (2022). · DOI
  32. DelRosso, N. et al. Large-scale mapping and mutagenesis of human transcriptional effector domains. Nature 616, 365–372 (2023). · DOI · Paper page
  33. Johansson, K. E., Mashahreh, B., Hartmann-Petersen, R., Ravid, T. & Lindorff-Larsen, K. Prediction of quality-control degradation signals in yeast proteins. J. Mol. Biol. 435, 167915 (2023). · DOI · Paper page
  34. Ruff, K. M. in Intrinsically Disordered Proteins (eds Kragelund, B. B. & Skriver, K.) Ch. 18, 347–389 (Springer, 2020). · DOI
  35. Lotthammer, J. M. et al. Direct prediction of intrinsically disordered protein conformational properties from sequences. Nat. Methods https://doi.org/10.1038/s41592-023-02159-5 (2024). · DOI
  36. Wang, J. et al. A molecular grammar governing the driving forces for phase separation of prion-like RNA binding proteins. Cell 174, 688–699 (2018). · DOI · Paper page
  37. Fisher, R. S. & Elbaum-Garfinkle, S. Tunable multiphase dynamics of arginine and lysine liquid condensates. Nat. Commun. 11, 4628 (2020). · DOI
  38. Schmidt, H. B., Barreau, A. & Rohatgi, R. Phase separation-deficient TDP43 remains functional in splicing. Nat. Commun. 10, 4890 (2019). · DOI
  39. Sørensen, C. S. & Kjaergaard, M. Effective concentrations enforced by intrinsically disordered linkers are governed by polymer physics. Proc. Natl Acad. Sci. USA 116, 23124–23131 (2019). · DOI
  40. Hantschel, O. et al. A myristoyl/phosphotyrosine switch regulates c-Abl. Cell 112, 845–857 (2003). · DOI · Paper page
  41. Harmon, T. S., Holehouse, A. S., Rosen, M. K. & Pappu, R. V. Intrinsically disordered linkers determine the interplay between phase separation and gelation in multivalent proteins. eLife 6, e30294 (2017). · DOI · Paper page
  42. Asselin, L. et al. Mutations in the KIF21B kinesin gene cause neurodevelopmental disorders through imbalanced canonical motor activity. Nat. Commun. 11, 2441 (2020). · DOI
  43. Ahmed, S. B. M. & Prigent, S. A. Insights into the Shc family of adaptor proteins. J. Mol. Signal. 12, 2 (2017). · DOI
  44. Manetti, F. LIM kinases are attractive targets with many macromolecular partners and only a few small molecule regulators. Med. Res. Rev. 32, 968–998 (2011). · DOI
  45. Mégarbané, H. et al. An autosomal-recessive form of cutis laxa is due to homozygous elastin mutations, and the phenotype may be modified by a heterozygous fibulin 5 polymorphism. J. Invest. Dermatol. 129, 1650–1655 (2009). · DOI
  46. Langenhove, T. V. et al. Genetic contribution of FUS to frontotemporal lobar degeneration. Neurology 74, 366–371 (2010). · DOI
  47. Liu, Q. et al. Whole-exome sequencing identifies a missense mutation in hnRNPA1 in a family with flail arm ALS. Neurology 87, 1763–1769 (2016). · DOI
  48. Mensah, M. A. et al. Aberrant phase separation and nucleolar dysfunction in rare genetic diseases. Nature 614, 564–571 (2023).
  49. Banani, S. F. et al. Genetic variation associated with condensate dysregulation in disease. Dev. Cell 57, 1776–1788 (2022). · DOI
  50. Richardson, R. A. K., Navarro, H. T., Amaral, L. A. N. & Stoeger, T. Meta-research: understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results. eLife 12, RP93429 (2023). · DOI · Paper page
  51. Janson, G., Valdes-Garcia, G., Heo, L. & Feig, M. Direct generation of protein conformational ensembles via machine learning. Nat. Commun. 14, 774 (2023). · DOI · Paper page
  52. Le Mercier, P. et al. SwissBioPics—an interactive library of cell images for the visualization of subcellular location data. Database 2022, baac026 (2022). · DOI
  53. Hanson, J., Yang, Y., Paliwal, K. & Zhou, Y. Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks. Bioinformatics 33, 685–692 (2016). · DOI · Paper page
  54. Hekkelman, M. L., de Vries, I., Joosten, R. P. & Perrakis, A. AlphaFill: enriching AlphaFold models with ligands and cofactors. Nat. Methods 20, 205–213 (2022). · DOI · Paper page
  55. Anderson, J. A., Glaser, J. & Glotzer, S. C. HOOMD-blue: a Python package for high-performance molecular dynamics and hard particle Monte Carlo simulations. Comput. Mater. Sci. 173, 109363 (2020). · DOI · Paper page
  56. Flyvbjerg, H. & Petersen, H. G. Error estimates on averages of correlated data. J. Chem. Phys. 91, 461–466 (1989). · DOI · Paper page
  57. Borgia, A. et al. Consistent view of polypeptide chain expansion in chemical denaturants from multiple experimental methods. J. Am. Chem. Soc. 138, 11714–11726 (2016). · DOI
  58. Aronovitz, J. & Nelson, D. Universal features of polymer shapes. Journal de Physique 47, 1445–1456 (1986). · DOI
  59. Hensen, U., Gräter, F. & Henchman, R. H. Macromolecular entropy can be accurately computed from force. J. Chem. Theory Comput. 10, 4777–4781 (2014). · DOI
  60. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
  61. McGibbon, R. T. et al. MDTraj: a modern open library for the analysis of molecular dynamics trajectories. Biophys. J. 109, 1528 – 1532 (2015). · DOI · Paper page
  62. Holehouse, A. S., Das, R. K., Ahad, J. N., Richardson, M. O. & Pappu, R. V. CIDER: resources to analyze sequence-ensemble relationships of intrinsically disordered proteins. Biophys. J. 112, 16–21 (2017). · DOI
  63. Montepietra, D. et al. FRETpredict: a Python package for FRET efficiency predictions using rotamer libraries. Preprint at bioRxiv https://doi.org/10.1101/2023.01.27.525885 (2023). · DOI
  64. Rotkiewicz, P. & Skolnick, J. Fast procedure for reconstruction of full-atom protein models from reduced representations. J. Comput. Chem. 29, 1460–1465 (2008). · DOI
  65. Fuertes, G. et al. Decoupling of size and shape fluctuations in heteropolymeric sequences reconciles discrepancies in SAXS vs. FRET measurements. Proc. Natl Acad. Sci. USA 114, E6342–E6351 (2017). · DOI
  66. Gomes, G.-N. W. et al. Conformational ensembles of an intrinsically disordered protein consistent with NMR, SAXS, and single-molecule FRET. J. Am. Chem. Soc. 142, 15697–15710 (2020). · DOI
  67. Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000). · DOI · Paper page
  68. The Gene Ontology Consortium. The Gene Ontology knowledgebase in 2023. Genetics 224, iyad031 (2023). · DOI · Paper page
  69. Hagberg, A. A., Schult, D. A. & Swart, P. J. Exploring network structure, dynamics, and function using networkx. In Proc. 7th Python in Science Conference (eds Varoquaux, G., Vaught, T. & Millman, J.) 11–15 (2008). · DOI · Paper page
  70. Brunner, E. & Munzel, U. The nonparametric Behrens–Fisher problem: asymptotic theory and a small-sample approximation. Biom. J. 42, 17–25 (2000). · DOI
  71. Thulin, M. Modern Statistics with R (Eos Chasma Press, 2021).
  72. Noguchi, K., Konietschke, F., Marmolejo-Ramos, F. & Pauly, M. Permutation tests are robust and powerful at 0.5% and 5% significance levels. Behav. Res. Meth. 53, 2712–2724 (2021). · DOI
  73. Mashahreh, B. et al. Conserved degronome features governing quality control associated proteolysis. Nat. Commun. 13, 7588 (2022). · DOI
  74. Koren, I. et al. The eukaryotic proteome is shaped by E3 ubiquitin ligases targeting C-terminal degrons. Cell 173, 1622–1635 (2018). · DOI · Paper page
  75. Chang, Y.-W., Hsieh, C.-J., Chang, K.-W., Ringgaard, M. & Lin, C.-J. Training and testing low-degree polynomial data mappings via linearSVM. J. Mach. Learn. Res. 11, 1471–1490 (2010).
  76. Schölkopf, B., Smola, A., Williamson, R. & Bartlett, P. New support vector algorithms. Neural Comput. 12, 1207–1245 (2000). · DOI
  77. Breiman, L. Random forests. Machine Learning 45, 5–32 (2001). · DOI · Paper page
  78. Chao, T.-H., Rekhi, S., Mittal, J. & Tabor, D. P. Data-driven models for predicting intrinsically disordered protein polymer physics directly from composition or sequence. Mol. Syst. Des. Eng. 8, 1146–1155 (2023). · DOI · Paper page
  79. Dignon, G. L., Zheng, W., Kim, Y. C., Best, R. B. & Mittal, J. Sequence determinants of protein phase behavior from a coarse-grained model. PLOS Comput. Biol. 14, e1005941 (2018). · DOI · Paper page
  80. Lu, A. X. et al. Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning. PLOS Comput. Biol. 18, e1010238 (2022). · DOI
  81. Altenhoff, A. M. et al. OMA orthology in 2021: website overhaul, conserved isoforms, ancestral gene order and more. Nucleic Acids Res. 49, D373–D379 (2020). · DOI · Paper page
  82. Katoh, K. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002). · DOI · Paper page

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Metadata

Authors
Tesei, G., Trolle, A. I., Jonsson, N., Betz, J., Knudsen, F. E., Pesce, F., Johansson, K. E., & Lindorff-Larsen, K.
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2024-01-31
Volume
626
Issue
8000
Pages
897-904
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-023-07004-5
OpenAlex
W4391383720
Cited by
308
References
16
FWCI
35.20
Type
journal article
Language
en
Open access
green
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