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Intrinsically disordered regions as facilitators of the transcription factor target search

Abstract

Transcription factors (TFs) contribute to organismal development and function by regulating gene expression. Despite decades of research, the factors determining the specificity and speed at which eukaryotic TFs detect their target binding sites remain poorly understood. Recent studies have pointed to intrinsically disordered regions (IDRs) within TFs as key regulators of the process by which TFs find their target sites on DNA (the TF target search). However, IDRs are challenging to study because they can confer specificity despite low sequence complexity and can be functionally conserved despite rapid sequence divergence. Nevertheless, emerging computational and experimental approaches are beginning to elucidate the sequence–function relationship within the IDRs of TFs. Additional insights are informing potential mechanisms underlying the IDR-directed search for the DNA targets of TFs, including incorporation into biomolecular condensates, facilitating TF co-localization, and the hypothesis that IDRs recognize and directly interact with specific genomic regions.

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Fig. 1: Characteristics of TF IDRs across life.
Fig. 2: Models of the determinants governing transcription factor binding specificity.
Fig. 3: Models of the dynamics of the transcription factor target search process.
Fig. 4: Challenges with computational sequence analysis of IDRs.
Fig. 5: Experimental approaches for identifying functional TF IDR grammars.
Fig. 6: Possible mechanisms underlying IDR-based TF target specificity.

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References

  1. Jacob, F. & Monod, J. Genetic regulatory mechanisms in the synthesis of proteins. J. Mol. Biol. 3, 318–356 (1961).

    Article  CAS  PubMed  Google Scholar 

  2. Gann, A. Jacob and Monod: from operons to evodevo. Curr. Biol. 20, R718–R723 (2010).

    Article  CAS  PubMed  Google Scholar 

  3. Beckwith, J. The operon as paradigm: normal science and the beginning of biological complexity. J. Mol. Biol. 409, 7–13 (2011).

    Article  CAS  PubMed  Google Scholar 

  4. Struhl, K. Fundamentally different logic of gene regulation in eukaryotes and prokaryotes. Cell 98, 1–4 (1999).

    Article  CAS  PubMed  Google Scholar 

  5. Ferrie, J. J., Karr, J. P., Tjian, R. & Darzacq, X. “Structure”–function relationships in eukaryotic transcription factors: the role of intrinsically disordered regions in gene regulation. Mol. Cell 82, 3970–3984 (2022).

    Article  CAS  PubMed  Google Scholar 

  6. Holehouse, A. S. & Kragelund, B. B. The molecular basis for cellular function of intrinsically disordered protein regions. Nat. Rev. Mol. Cell Biol. 25, 187–211 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  7. van der Lee, R. et al. Classification of intrinsically disordered regions and proteins. Chem. Rev. 114, 6589–6631 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Skupien-Rabian, B. et al. Proteomic and bioinformatic analysis of a nuclear intrinsically disordered proteome. J. Proteom. 130, 76–84 (2016).

    Article  CAS  Google Scholar 

  9. Wang, C., Uversky, V. N. & Kurgan, L. Disordered nucleiome: abundance of intrinsic disorder in the DNA- and RNA-binding proteins in 1121 species from Eukaryota, Bacteria and Archaea. Proteomics 16, 1486–1498 (2016).

    Article  CAS  PubMed  Google Scholar 

  10. Peng, Z., Mizianty, M. J. & Kurgan, L. Genome-scale prediction of proteins with long intrinsically disordered regions. Proteins 82, 145–158 (2014).

    Article  CAS  PubMed  Google Scholar 

  11. Minezaki, Y., Homma, K., Kinjo, A. R. & Nishikawa, K. Human transcription factors contain a high fraction of intrinsically disordered regions essential for transcriptional regulation. J. Mol. Biol. 359, 1137–1149 (2006).

    Article  CAS  PubMed  Google Scholar 

  12. Ward, J. J., Sodhi, J. S., McGuffin, L. J., Buxton, B. F. & Jones, D. T. Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J. Mol. Biol. 337, 635–645 (2004).

    Article  CAS  PubMed  Google Scholar 

  13. Liu, J. et al. Intrinsic disorder in transcription factors. Biochemistry 45, 6873–6888 (2006).

    Article  CAS  PubMed  Google Scholar 

  14. Ishida, T. & Kinoshita, K. PrDOS: prediction of disordered protein regions from amino acid sequence. Nucleic Acids Res. 35, W460–W464 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wright, P. E. & Dyson, H. J. Intrinsically unstructured proteins: re-assessing the protein structure–function paradigm. J. Mol. Biol. 293, 321–331 (1999).

    Article  CAS  PubMed  Google Scholar 

  16. Uversky, V. N. Unusual biophysics of intrinsically disordered proteins. Biochim. Biophys. Acta Proteins Proteom. 1834, 932–951 (2013).

    Article  CAS  Google Scholar 

  17. Varadi, M. et al. pE-DB: a database of structural ensembles of intrinsically disordered and of unfolded proteins. Nucleic Acids Res. 42, D326–D335 (2014).

    Article  CAS  PubMed  Google Scholar 

  18. Fuxreiter, M., Simon, I., Friedrich, P. & Tompa, P. Preformed structural elements feature in partner recognition by intrinsically unstructured proteins. J. Mol. Biol. 338, 1015–1026 (2004).

    Article  CAS  PubMed  Google Scholar 

  19. Arai, M., Sugase, K., Dyson, H. J. & Wright, P. E. Conformational propensities of intrinsically disordered proteins influence the mechanism of binding and folding. Proc. Natl Acad. Sci. USA 112, 9614–9619 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hammes, G. G., Chang, Y.-C. & Oas, T. G. Conformational selection or induced fit: a flux description of reaction mechanism. Proc. Natl Acad. Sci. USA 106, 13737–13741 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Borgia, A. et al. Extreme disorder in an ultrahigh-affinity protein complex. Nature 555, 61–66 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Schuler, B. et al. Binding without folding—the biomolecular function of disordered polyelectrolyte complexes. Curr. Opin. Struct. Biol. 60, 66–76 (2020).

    Article  CAS  PubMed  Google Scholar 

  23. Fuxreiter, M. Classifying the binding modes of disordered proteins. Int. J. Mol. Sci. 21, 8615 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Olsen, J. G., Teilum, K. & Kragelund, B. B. Behaviour of intrinsically disordered proteins in protein–protein complexes with an emphasis on fuzziness. Cell. Mol. Life Sci. 74, 3175–3183 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Clerc, I. et al. The diversity of molecular interactions involving intrinsically disordered proteins: a molecular modeling perspective. Comput. Struct. Biotechnol. J. 19, 3817–3828 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Miskei, M., Horvath, A., Vendruscolo, M. & Fuxreiter, M. Sequence-based prediction of fuzzy protein interactions. J. Mol. Biol. 432, 2289–2303 (2020).

    Article  CAS  PubMed  Google Scholar 

  27. Bjarnason, S. et al. DNA binding redistributes activation domain ensemble and accessibility in pioneer factor Sox2. Nat. Commun. 15, 1445 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kornberg, R. D. Mediator and the mechanism of transcriptional activation. Trends Biochem. Sci. 30, 235–239 (2005).

    Article  CAS  PubMed  Google Scholar 

  29. Hope, I. A. & Struhl, K. Functional dissection of a eukaryotic transcriptional activator protein, GCN4 of yeast. Cell 46, 885–894 (1986).

    Article  CAS  PubMed  Google Scholar 

  30. Hope, I. A., Mahadevan, S. & Struhl, K. Structural and functional characterization of the short acidic transcriptional activation region of yeast GCN4 protein. Nature 333, 635–640 (1988).

    Article  CAS  PubMed  Google Scholar 

  31. Staller, M. V. et al. A high-throughput mutational scan of an intrinsically disordered acidic transcriptional activation domain. Cell Syst. 6, 444–455.e446 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Sabari, B. R. et al. Coactivator condensation at super-enhancers links phase separation and gene control. Science 361, eaar3958 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Boija, A. et al. Transcription factors activate genes through the phase-separation capacity of their activation domains. Cell 175, 1842–1855.e1816 (2018).

    Article  CAS  PubMed  Google Scholar 

  34. Tompa, P., Schad, E., Tantos, A. & Kalmar, L. Intrinsically disordered proteins: emerging interaction specialists. Curr. Opin. Struct. Biol. 35, 49–59 (2015).

    Article  CAS  PubMed  Google Scholar 

  35. Wang, J. et al. A molecular grammar governing the driving forces for phase separation of prion-like RNA binding proteins. Cell 174, 688–699.e616 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Bugge, K. et al. Interactions by disorder—a matter of context. Front. Mol. Biosci. 7, 110 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Amin, A. N., Lin, Y.-H., Das, S. & Chan, H. S. Analytical theory for sequence-specific binary fuzzy complexes of charged intrinsically disordered proteins. J. Phys. Chem. B 124, 6709–6720 (2020).

    Article  CAS  PubMed  Google Scholar 

  38. Choi, J.-M., Holehouse, A. S. & Pappu, R. V. Physical principles underlying the complex biology of intracellular phase transitions. Annu. Rev. Biophys. 49, 107–133 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Zarin, T. et al. Proteome-wide signatures of function in highly diverged intrinsically disordered regions. eLife 8, e46883 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Riback, J. A. et al. Stress-triggered phase separation is an adaptive, evolutionarily tuned response. Cell 168, 1028–1040.e1019 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 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).

    Article  CAS  PubMed  Google Scholar 

  42. Langstein-Skora, I. et al. Sequence- and chemical specificity define the functional landscape of intrinsically disordered regions. Preprint at bioRxiv https://doi.org/10.1101/2022.02.10.480018 (2022).

  43. Baughman, H. E. R. et al. An intrinsically disordered transcription activation domain increases the DNA binding affinity and reduces the specificity of NFκB p50/RelA. J. Biol. Chem. 298, 102349 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Chong, S. & Mir, M. Towards decoding the sequence-based grammar governing the functions of intrinsically disordered protein regions. J. Mol. Biol. 433, 166724 (2021).

    Article  CAS  PubMed  Google Scholar 

  45. Holehouse, A. S. & Kragelund, B. B. The molecular basis for cellular function of intrinsically disordered protein regions. Nat. Rev. Mol. Cell Biol. 25, 187–211 (2024).

    Article  CAS  PubMed  Google Scholar 

  46. Hong, S., Choi, S., Kim, R. & Koh, J. Mechanisms of macromolecular interactions mediated by protein intrinsic disorder. Mol. Cell 43, 899–908 (2020).

    Article  CAS  Google Scholar 

  47. Wunderlich, Z. & Mirny, L. A. Different gene regulation strategies revealed by analysis of binding motifs. Trends Genet. 25, 434–440 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Siggers, T., Reddy, J., Barron, B. & Bulyk, M. L. Diversification of transcription factor paralogs via noncanonical modularity in C2H2 zinc finger DNA binding. Mol. Cell 55, 640–648 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Jana, T., Brodsky, S. & Barkai, N. Speed-specificity trade-offs in the transcription factors search for their genomic binding sites. Trends Genet. 37, 421–432 (2021).

    Article  CAS  PubMed  Google Scholar 

  50. Wang, J. et al. Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome Res. 22, 1798–1812 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Reece, R. J. & Ptashne, M. Determinants of binding-site specificity among yeast C6 zinc cluster proteins. Science 261, 909–911 (1993).

    Article  CAS  PubMed  Google Scholar 

  52. Erijman, A. et al. A high-throughput screen for transcription activation domains reveals their sequence features and permits prediction by deep learning. Mol. Cell 78, 890–902.e896 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 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.e335 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Sanborn, A. L. et al. Simple biochemical features underlie transcriptional activation domain diversity and dynamic, fuzzy binding to Mediator. eLife 10, e68068 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Ravarani, C. N. et al. High-throughput discovery of functional disordered regions: investigation of transactivation domains. Mol. Syst. Biol. 14, e8190 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Hummel, N. F. C., Markel, K., Stefani, J., Staller, M. V. & Shih, P. M. Systematic identification of transcriptional activation domains from non-transcription factor proteins in plants and yeast. Cell Syst. 15, 662– 672.e4 (2024).

    Article  CAS  PubMed  Google Scholar 

  57. DelRosso et al. Large-scale mapping and mutagenesis of human transcriptional effector domains. Nature 616, 365–372 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Tycko, J. et al. High-throughput discovery and characterization of human transcriptional effectors. Cell 183, 2020–2035.e2016 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Brodsky, S. et al. Intrinsically disordered regions direct transcription factor in vivo binding specificity. Mol. Cell 79, 459–471.e454 (2020). This study systematically perturbed and truncated the IDRs of Msn2 and Yap1 and then performed genome-wide mapping in vivo, which demonstrated that these IDRs are crucial for target specifcity.

    Article  CAS  PubMed  Google Scholar 

  60. Kumar, D. K. et al. Complementary strategies for directing in vivo transcription factor binding through DNA binding domains and intrinsically disordered regions. Mol. Cell 83, 1462–1473.e1465 (2023).

    Article  CAS  PubMed  Google Scholar 

  61. Brodsky, S., Jana, T. & Barkai, N. Order through disorder: the role of intrinsically disordered regions in transcription factor binding specificity. Curr. Opin. Struct. Biol. 71, 110–115 (2021).

    Article  CAS  PubMed  Google Scholar 

  62. Staller, M. V. Transcription factors perform a 2-step search of the nucleus. Genetics 222, iyac111 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Jonas, F. et al. The molecular grammar of protein disorder guiding genome-binding locations. Nucleic Acids Res. 51, 4831–4844 (2023). By creating and profiling more than a hundred IDR variants, this study revealed that the IDR-based target specificity of Msn2 was conferred through a grammar of hydrophobic residues dispersed in a hydrophilic context.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Hurieva, B. et al. Disordered sequences of transcription factors regulate genomic binding by integrating diverse sequence grammars and interaction types. Nucleic Acids Res. 52, 8763–8777 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Lang, T. J. et al. Massively parallel binding assay (MPBA) reveals limited transcription factor binding cooperativity, challenging models of specificity. Nucleic Acids Res. 52, 12227–12243 (2024).

    Article  CAS  Google Scholar 

  66. He, F. et al. Interaction between p53 N terminus and core domain regulates specific and nonspecific DNA binding. Proc. Natl Acad. Sci. USA 116, 8859–8868 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Krois, A. S., Dyson, H. J. & Wright, P. E. Long-range regulation of p53 DNA binding by its intrinsically disordered N-terminal transactivation domain. Proc. Natl Acad. Sci. USA 115, E11302–E11310 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Tripathi, S. et al. Defining the condensate landscape of fusion oncoproteins. Nat. Commun. 14, 6008 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Shirnekhi, H. K., Chandra, B. & Kriwacki, R. W. Dissolving fusion oncoprotein condensates to reverse aberrant gene expression. Cancer Res. 83, 3324–3326 (2023).

    Article  CAS  PubMed  Google Scholar 

  70. Wang, Y. et al. Dissolution of oncofusion transcription factor condensates for cancer therapy. Nat. Chem. Biol. 19, 1223–1234 (2023).

    Article  CAS  PubMed  Google Scholar 

  71. Gangwal, K. et al. Microsatellites as EWS/FLI response elements in Ewing’s sarcoma. Proc. Natl Acad. Sci. USA 105, 10149–10154 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Grünewald, T. G. et al. Chimeric EWSR1-FLI1 regulates the Ewing sarcoma susceptibility gene EGR2 via a GGAA microsatellite. Nat. Genet. 47, 1073–1078 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Boulay, G. et al. Cancer-specific retargeting of BAF complexes by a prion-like domain. Cell 171, 163–178.e119 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Chong, S. et al. Imaging dynamic and selective low-complexity domain interactions that control gene transcription. Science 361, eaar2555 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Chong, S. et al. Tuning levels of low-complexity domain interactions to modulate endogenous oncogenic transcription. Mol. Cell 82, 2084–2097.e2085 (2022).

    Article  CAS  PubMed  Google Scholar 

  76. Boller, S. et al. Pioneering activity of the C-terminal domain of EBF1 shapes the chromatin landscape for B cell programming. Immunity 44, 527–541 (2016).

    Article  CAS  PubMed  Google Scholar 

  77. Zolotarev, N. et al. Regularly spaced tyrosines in EBF1 mediate BRG1 recruitment and formation of nuclear subdiffractive clusters. Genes. Dev. 38, 4–10 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Chen, Y. et al. Mechanisms governing target search and binding dynamics of hypoxia-inducible factors. eLife 11, e75064 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Burdach, J. et al. Regions outside the DNA-binding domain are critical for proper in vivo specificity of an archetypal zinc finger transcription factor. Nucleic Acids Res. 42, 276–289 (2014).

    Article  CAS  PubMed  Google Scholar 

  80. Garcia, D. A. et al. An intrinsically disordered region-mediated confinement state contributes to the dynamics and function of transcription factors. Mol. Cell 81, 1484–1498.e1486 (2021). Using single-molecule imaging, this study demonstrated the effect of IDRs on diffusion rates and residence times during the TF search process.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Lerner, J., Katznelson, A., Zhang, J. & Zaret, K. S. Different chromatin-scanning modes lead to targeting of compacted chromatin by pioneer factors FOXA1 and SOX2. Cell Rep. 42, 112748 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Naderi, J. et al. An activity-specificity trade-off encoded in human transcription factors. Nat. Cell Biol. 81, 1309–1321 (2024).

    Article  Google Scholar 

  83. Lambourne, L. et al. Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms. Preprint at bioRxiv https://doi.org/10.1101/2024.03.12.584681 (2024).

  84. Mukherjee, A. et al. A fine kinetic balance of interactions directs transcription factor hubs to genes. Preprint at bioRxiv https://doi.org/10.1101/2024.04.16.589811 (2024).

  85. Gorman, J. & Greene, E. C. Visualizing one-dimensional diffusion of proteins along DNA. Nat. Struct. Mol. Biol. 15, 768–774 (2008).

    Article  CAS  PubMed  Google Scholar 

  86. Suter, D. M. Transcription factors and DNA play hide and seek. Trends Cell Biol. 30, 491–500 (2020).

    Article  CAS  PubMed  Google Scholar 

  87. Mirny, L. et al. How a protein searches for its site on DNA: the mechanism of facilitated diffusion. J. Phys. A 42, 434013 (2009).

    Article  Google Scholar 

  88. Berg, O. G. & Ehrenberg, M. Association kinetics with coupled three- and one-dimensional diffusion: chain-length dependence of the association rate to specific DNA sites. Biophysical Chem. 15, 41–51 (1982).

    Article  CAS  Google Scholar 

  89. Berg, O. G., Winter, R. B. & von Hippel, P. H. Diffusion-driven mechanisms of protein translocation on nucleic acids. 1. Models and theory. Biochemistry 20, 6929–6948 (1981).

    Article  CAS  PubMed  Google Scholar 

  90. Winter, R. B., Berg, O. G. & Von Hippel, P. H. Diffusion-driven mechanisms of protein translocation on nucleic acids. 3. The Escherichia coli lac repressor-operator interaction: kinetic measurements and conclusions. Biochemistry 20, 6961–6977 (1981).

    Article  CAS  PubMed  Google Scholar 

  91. von Hippel, P. H. & Berg, O. G. Facilitated target location in biological systems. J. Biol. Chem. 264, 675–678 (1989).

    Article  Google Scholar 

  92. Liu, Z. & Tjian, R. Visualizing transcription factor dynamics in living cells. J. Cell Biol. 217, 1181–1191 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Elf, J. & Barkefors, I. Single-molecule kinetics in living cells. Annu. Rev. Biochem. 88, 635–659 (2019).

    Article  CAS  PubMed  Google Scholar 

  94. Lionnet, T. & Wu, C. Single-molecule tracking of transcription protein dynamics in living cells: seeing is believing, but what are we seeing? Curr. Opin. Genet. Dev. 67, 94–102 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Wang, Z. & Deng, W. Dynamic transcription regulation at the single-molecule level. Dev. Biol. 482, 67–81 (2022).

    Article  CAS  PubMed  Google Scholar 

  96. Elf, J., Li, G. W. & Xie, X. S. Probing transcription factor dynamics at the single-molecule level in a living cell. Science 316, 1191–1194 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Hammar, P. et al. The lac repressor displays facilitated diffusion in living cells. Science 336, 1595–1598 (2012).

    Article  CAS  PubMed  Google Scholar 

  98. Marklund, E. et al. DNA surface exploration and operator bypassing during target search. Nature 583, 858–861 (2020).

    Article  CAS  PubMed  Google Scholar 

  99. Hansen, A. S., Amitai, A., Cattoglio, C., Tjian, R. & Darzacq, X. Guided nuclear exploration increases CTCF target search efficiency. Nat. Chem. Biol. 16, 257–266 (2020). By combining single-molecule imaging and a mathematical model, this study showed that the interaction between an IDR and RNA accelerates the search process of the CTCF TF.

    Article  CAS  PubMed  Google Scholar 

  100. Tang, X. et al. Kinetic principles underlying pioneer function of GAGA transcription factor in live cells. Nat. Struct. Mol. Biol. 29, 665–676 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Mazzocca, M. et al. Chromatin organization drives the search mechanism of nuclear factors. Nat. Commun. 14, 6433 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Garcia, D. A. et al. Power-law behavior of transcription factor dynamics at the single-molecule level implies a continuum affinity model. Nucleic Acids Res. 49, 6605–6620 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Gera, T., Jonas, F., More, R. & Barkai, N. Evolution of binding preferences among whole-genome duplicated transcription factors. Elife 11, e73225 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Nguyen Ba, A. N. et al. Proteome-wide discovery of evolutionary conserved sequences in disordered regions. Sci. Signal. 5, rs1 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Brown, C. J., Johnson, A. K. & Daughdrill, G. W. Comparing models of evolution for ordered and disordered proteins. Mol. Biol. Evol. 27, 609–621 (2010).

    Article  CAS  PubMed  Google Scholar 

  106. Benz, C. et al. Proteome‐scale mapping of binding sites in the unstructured regions of the human proteome. Mol. Syst. Biol. 18, e10584 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Krystkowiak, I. & Davey, N. E. SLiMSearch: a framework for proteome-wide discovery and annotation of functional modules in intrinsically disordered regions. Nucleic Acids Res. 45, W464–w469 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Tompa, P., Davey, N. E., Gibson, T. J. & Babu, M. M. A million peptide motifs for the molecular biologist. Mol. Cell 55, 161–169 (2014).

    Article  CAS  PubMed  Google Scholar 

  109. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Lotthammer, J. M., Ginell, G. M., Griffith, D., Emenecker, R. J. & Holehouse, A. S.Direct prediction of intrinsically disordered protein conformational properties from sequence. Nat. Methods 21, 465–476 (2024). This study presents a deep-learning model to efficiently predict biophysical ensemble properties for an IDR from its seqeuence after being trained on molecular dynamics simulations.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Joseph, J. A. et al. Physics-driven coarse-grained model for biomolecular phase separation with near-quantitative accuracy. Nat. Comput. Sci. 1, 732–743 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Ginell, G. M., Emenecker, R. J., Lotthammer, J. M., Usher, E. T. & Holehouse, A. S. Direct prediction of intermolecular interactions driven by disordered regions. Preprint at bioRxiv https://doi.org/10.1101/2024.06.03.597104 (2024).

  115. King, M. R. et al. Macromolecular condensation organizes nucleolar sub-phases to set up a pH gradient. Cell 187, 1889–1906.e1824 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Tesei, G. et al. Conformational ensembles of the human intrinsically disordered proteome. Nature 626, 897–904 (2024). This study conducted a comprehensive computational analysis of biophysical properties across the human IDRome and related them to biological functions.

    Article  CAS  PubMed  Google Scholar 

  117. Kasahara, K., Terazawa, H., Takahashi, T. & Higo, J. Studies on molecular dynamics of intrinsically disordered proteins and their fuzzy complexes: a mini-review. Comput. Struct. Biotechnol. J. 17, 712–720 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. De La Cruz, N. et al. Disorder-mediated interactions target proteins to specific condensates. Mol. Cell 84, 3497–3512.e9 (2024).

    Article  CAS  PubMed  Google Scholar 

  119. Wang, X. et al. Dynamic autoinhibition of the HMGB1 protein via electrostatic fuzzy interactions of intrinsically disordered regions. J. Mol. Biol. 433, 167122 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Oksuz, O. et al. Transcription factors interact with RNA to regulate genes. Mol. Cell 83, 2449–2463.e2413 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Hnisz, D., Shrinivas, K., Young, R. A., Chakraborty, A. K. & Sharp, P. A. A phase separation model for transcriptional control. Cell 169, 13–23 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Boehning, M. et al. RNA polymerase II clustering through carboxy-terminal domain phase separation. Nat. Struct. Mol. Biol. 25, 833–840 (2018).

    Article  CAS  PubMed  Google Scholar 

  123. Stortz, M., Presman, D. M. & Levi, V. Transcriptional condensates: a blessing or a curse for gene regulation? Commun. Biol. 7, 187 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Ahn, J. H. et al. Phase separation drives aberrant chromatin looping and cancer development. Nature 595, 591–595 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Kent, S. et al. Phase-separated transcriptional condensates accelerate target-search process revealed by live-cell single-molecule imaging. Cell Rep. 33, 108248 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Chowdhary, S., Kainth, A. S., Paracha, S., Gross, D. S. & Pincus, D. Inducible transcriptional condensates drive 3D genome reorganization in the heat shock response. Mol. Cell 82, 4386–4399.e4387 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Lee, J. et al. Transcription factor condensates, 3D clustering, and gene expression enhancement of the MET regulon. eLife 13, RP96028 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  128. Chowdhary, S., Kainth, A. S., Pincus, D. & Gross, D. S. Heat shock factor 1 drives intergenic association of its target gene loci upon heat shock. Cell Rep. 26, 18–28.e15 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Wollman, A. J. M. et al. Transcription factor clusters regulate genes in eukaryotic cells. eLife 6, e27451 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  130. Hyman, A. A., Weber, C. A. & Jülicher, F. Liquid–liquid phase separation in biology. Annu. Rev. Cell Dev. Biol. 30, 39–58 (2014).

    Article  CAS  PubMed  Google Scholar 

  131. Lyon, A. S., Peeples, W. B. & Rosen, M. K. A framework for understanding the functions of biomolecular condensates across scales. Nat. Rev. Mol. Cell Biol. 22, 215–235 (2021).

    Article  CAS  PubMed  Google Scholar 

  132. Banani, S. F., Lee, H. O., Hyman, A. A. & Rosen, M. K. Biomolecular condensates: organizers of cellular biochemistry. Nat. Rev. Mol. Cell Biol. 18, 285–298 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Shin, Y. & Brangwynne, C. P. Liquid phase condensation in cell physiology and disease. Science 357, eaaf4382 (2017).

    Article  PubMed  Google Scholar 

  134. Lyons, H. et al. Functional partitioning of transcriptional regulators by patterned charge blocks. Cell 186, 327–345.e328 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Lin, Y., Currie, S. L. & Rosen, M. K. Intrinsically disordered sequences enable modulation of protein phase separation through distributed tyrosine motifs. J. Biol. Chem. 292, 19110–19120 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. 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).

    Article  CAS  PubMed  Google Scholar 

  137. Martin, E. W. et al. Valence and patterning of aromatic residues determine the phase behavior of prion-like domains. Science 367, 694–699 (2020). This experimental study demonstrated the sequence grammar of phase separation in prion-like proteins, which led to the proposal of the predictive sticker-and-spacer model.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Ginell, G. M. & Holehouse, A. S. An introduction to the stickers-and-spacers framework as applied to biomolecular condensates. Meth. Mol. Biol. 2563, 95–116 (2023).

    Article  Google Scholar 

  139. Morgunova, E. & Taipale, J. Structural perspective of cooperative transcription factor binding. Curr. Opin. Struct. Biol. 47, 1–8 (2017).

    Article  CAS  PubMed  Google Scholar 

  140. Lupo, O. et al. The architecture of binding cooperativity between densely bound transcription factors. Cell Syst. 14, 732–745.e5 (2023). This study largely refuted the proposed role of PPIs in the IDR-based target specificity of Msn2, by showing that deleting 14 co-binding TFs did not have a substantial effect on the TF binding pattern.

    Article  CAS  PubMed  Google Scholar 

  141. Mindel, V. et al. Intrinsically disordered regions of the Msn2 transcription factor encode multiple functions using interwoven sequence grammars. Nucleic Acids Res. 52, 2260–2272 (2024).

    Article  CAS  PubMed  Google Scholar 

  142. Jonas, F., Vidavski, M., Benuck, E., Barkai, N. & Yaakov, G. Nucleosome retention by histone chaperones and remodelers occludes pervasive DNA–protein binding. Nucleic Acids Res. 51, 8496–8513 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Sigler, P. B. Transcriptional activation. Acid blobs and negative noodles. Nature 333, 210–212 (1988).

    Article  CAS  PubMed  Google Scholar 

  144. Li, N. et al. Structure of the origin recognition complex bound to DNA replication origin. Nature 559, 217–222 (2018).

    Article  CAS  PubMed  Google Scholar 

  145. Chappleboim, M., Naveh-Tassa, S., Carmi, M., Levy, Y. & Barkai, N. Ordered and disordered regions of the origin recognition complex direct differential in vivo binding at distinct motif sequences. Nucleic Acids Res. 52, 5720–5731 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Tolstorukov, M. Y., Jernigan, R. L. & Zhurkin, V. B. Protein–DNA hydrophobic recognition in the minor groove is facilitated by sugar switching. J. Mol. Biol. 337, 65–76 (2004).

    Article  CAS  PubMed  Google Scholar 

  147. Gupta, A., Kulkarni, M. & Mukherjee, A. Accurate prediction of B-form/A-form DNA conformation propensity from primary sequence: a machine learning and free energy handshake. Patterns 2, 100329 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. Basham, B., Schroth, G. P. & Ho, P. S. An A-DNA triplet code: thermodynamic rules for predicting A- and B-DNA. Proc. Natl Acad. Sci. USA 92, 6464–6468 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Tolstorukov, M. Y., Ivanov, V. I., Malenkov, G. G., Jernigan, R. L. & Zhurkin, V. B. Sequence-dependent B↔A transition in DNA evaluated with dimeric and trimeric scales. Biophys. J. 81, 3409–3421 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

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All authors researched the literature for the article. All authors contributed substantially to discussion of the content. F.J. and N.B. wrote the article. F.J. and N.B. reviewed and/or edited the manuscript before submission.

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Correspondence to Felix Jonas or Naama Barkai.

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Nature Reviews Genetics thanks Alex Holehouse; H. Courtney Hodges, who co-reviewed with Katerina Cermakova; and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Glossary

Biomolecular condensates

Membrane-less intracellular compartments that can contain proteins and nucleic acids and are often formed through phase separation with multivalent interactions between the constituents.

Degrons

Short peptide sequences (3–10 amino acids) that recruit E3 ligases and trigger degradation by the ubiquitin–proteasome system.

Diffusion coefficient

Variable that describes the velocity of a transciption factor (TF) when diffusing through the nucleus.

Mutational robustness

Property of a gene related to the number of mutations necessary to perturb the function of the encoded protein.

Residence time

The length of time that a DNA-binding protein is bound to DNA. The mean residence time is often used to describe the dissociation reaction using first-order kinetics.

Stickers-and-spacers model

Model to explain the biophysical behaviour of intrinsically disordered regions (IDRs) containing hydrophobic residues (stickers) dispersed in a hydrophilic environment (spacer). The stickers interact with each other while the spacers provide flexibility to the interaction.

Super-enhancers

Group of enhancers (regulatory elements in higher eukaryotes) in close proximity to each other that have a higher propensity to bind transcription factors, recruit the mediator and promote transcription than individual enhancers.

Yeast-1-hybrid

Experimental set-up consisting of a potential DNA-binding protein (DBP) and its partner DNA sequence. The DBP is attached to an activation domain and expressed in yeast, which also contains the respective DNA sequence upstream of a reporter gene (for example, GFP), so that the DBP binding triggers reporter gene expression.

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Jonas, F., Navon, Y. & Barkai, N. Intrinsically disordered regions as facilitators of the transcription factor target search. Nat Rev Genet 26, 424–435 (2025). https://doi.org/10.1038/s41576-025-00816-3

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