Skip to main page content
U.S. flag
See More

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Sep 30:14:RP104956.
doi: 10.7554/eLife.104956.

Design principles of transcription factors with intrinsically disordered regions

Wencheng Ji  1   2 , Ori Hachmo  1 , Naama Barkai  3 , Ariel Amir  1   2
Affiliations

Design principles of transcription factors with intrinsically disordered regions

Wencheng Ji et al. Elife. .

Abstract

Transcription factors (TFs) are proteins crucial for regulating gene expression. Effective regulation requires the TFs to rapidly bind to their correct target, enabling the cell to respond efficiently to stimuli such as nutrient availability or the presence of toxins. However, the search process is hindered by slow diffusive movement and the presence of 'false' targets - DNA segments that are similar to the true target. In eukaryotic cells, most TFs contain an intrinsically disordered region (IDR), which is commonly assumed to behave as a long, flexible polymeric tail composed of hundreds of amino acids. Recent experimental findings indicate that the IDR of certain TFs plays a pivotal role in the search process. However, the principles underlying the IDR's role remain unclear. Here, we reveal key design principles of the IDR related to TF binding affinity and search time. Our results demonstrate that the IDR significantly enhances both of these aspects. Furthermore, our model shows good agreement with experimental results, and we propose further experiments to validate the model's predictions.

Keywords: S. cerevisiae; intrinsically disordered regions; physics of living systems; polymer physics; statistical physics; transcription factors.

PubMed Disclaimer

Conflict of interest statement

WJ, OH, NB No competing interests declared, AA Reviewing editor, eLife

Figures

Figure 1.
Figure 1.. Model illustration of a TF with the IDR and its search process.
(a) Illustration of a TF locating its target site (highlighted in orange) on the DNA strand (depicted as a green curve). Surrounding the target is a region of length L, where the TF’s IDR interacts with the DNA. The TF performs 3D diffusion until it encounters and binds to this ‘antenna’ region. (b) Illustration of a TF that includes an IDR composed of AAs. On the right: a model of the TF, a polymer chain, comprising a single binding site on the DNA Binding Domain (DBD), and multiple binding sites on the IDR (also known as short linear motifs Jonas et al., 2025). (c) Once bound to the antenna, the TF performs effective 1D diffusion until it reaches its target. The 1D diffusion is via the binding and unbinding of sites along the IDR, a process we coin ‘octopusing’. V1 is the targets’ volume, d the separation, and EB the binding energy per binding site, where each site corresponds to a short linear motif.
Figure 2.
Figure 2.. Design principles across various parameters: l0/d, nb, nt, and EB.
(a) Plots of Q≡PTF/Psimple varying with l0/d are shown for several combinations of nt and nb for typical values of the relevant quantities: the nucleus volume is 1μm3, EDBD=15, EB=10, V1=(0.34nm)3, and d=50⋅0.34nm. (b) A heatmap of Q vs. nb and nt at EB=10 and d=l0. (c) Contour lines at different EB at PTF=0.9. Blue hexagrams represent the design principle nb∗=nt∗, with the black dashed line as a visual guide. The inset shows the dependence of nb∗ on energy. (d) Q for varying EB at d=l0 for several nb=nt. The vertical line represents Eth as determined by Equation 4, the solid curves illustrate the approximation of Q obtained from Equation 3, and the horizontal line indicates where PTF=1.
Figure 3.
Figure 3.. Mean search time varying with the antenna length , t3D and t1D vs. at EB∗ and n~∗.
The solid curves correspond to ttotal , t3D and t1D in Equation 5. Inset: probability density function (pdf) exponentially decays at large t.pdf≈exp(−t/ttotal)/ttotal (black solid line). The unit t0 represents the time for one AA to diffuse 1bp. a=0.5bp, d=10bp, and l0=5bp.
Figure 4.
Figure 4.. Comparisons with experimental results.
(a) Relative binding probability varies with the truncation length of the IDR, which quantitatively agrees with the experimental data in Brodsky et al., 2020, where we only vary EDBD (binding energy of DBD) to ensure that the maximal value reaches 1 at zero truncation length. Antenna length L=1000nm, EDBD=22 and EB=11. Other parameters are set the same as in Figure 2. (b) The search time estimated by our model (from Equation 5 and divided by a TF copy number of 100) is quantitatively comparable with the experimental results (Larson et al., 2011), as guided by the shaded area. c, The on-rate kon=ttotal/Vc and off-rate koff=(1−PTF)/(PTFttotal) varying with the IDR length, indicating that kD≡koff/kon ranges from 0.01 to 10 nM, with its variation primarily dominated by the off-rate.
Appendix 1—figure 1.
Appendix 1—figure 1.. f(r,l0) aligns well with Equation 10 (solid curves) for moderate m at various.
b0 Inset: aeff2/a02 vs. b0/a0 from Equation 12. (b) Equation 10 serves as a good approximation (solid lines) even at small m.
Appendix 2—figure 1.
Appendix 2—figure 1.. A diagram of the bound configurations at n=3.
Appendix 3—figure 1.
Appendix 3—figure 1.. Maximal Q is observed at l0∼d for all combinations of nb and nt.
Appendix 4—figure 1.
Appendix 4—figure 1.. Binding probability varying with nb and n1.
(a) PTF varying with nb at nt=12. PTF initially increases rapidly, until eventually saturating. (b) PTF varies non-monotonically with nt. PTF slightly decreases with increasing nt at nt>nb. Three examples at nb=7,8,9 are displayed. PTF is denoted by pentagrams at nt=nb.
Appendix 5—figure 1.
Appendix 5—figure 1.. Contour lines at PTF=0.9 and PTF=0.5 on the plane defined by the number of binding sites, nb, and the number of binding targets, nt.
The black dashed line is added to guide the relationship nb=nt.
Appendix 6—figure 1.
Appendix 6—figure 1.. Plots of the affinity enhancement, quantified by the ratio Q≡PTF/Psimple, for the case where IDR sites as well as their targets are randomly positioned on the IDR and DNA, respectively.
See the analogous Figure 2 in the main text for the case of homogeneously distributed IDR sites and targets. (a) Q is maximal when l0∼d (shaded region) for different values of nt=nb. The average distance between IDR sites is l0, and the average distance between IDR targets is d. (b) A heatmap of Q at EB=10 and l0=d. (c) Contour lines at different EB at PTF=0.5 exhibit an approximate symmetrical L-shape, which indicates nb∗=nt∗, as shown by the red triangles. nb∗ decreases with increasing EB. (d) Q varies with EB for several different constants of nt=nb. The characteristic energy Eth, as predicted by Equation 4 in the main text, corresponds to the region where a notable increase is observed.
Appendix 7—figure 1.
Appendix 7—figure 1.. Demonstration of a single round of 3D-1D search in simulations.
(a) A trajectory (red) of TF in the cell nucleus (light blue). is the average distance of the TF to the antenna, and Xc is the position of the center of mass along the antenna direction. The trajectory is displayed with a time interval of 106t0. (b) The average number of rounds the TF hits the antenna, nhits, before reaching the DBD target. The optimal mean total search time is obtained at L∗=300bp.
Appendix 7—figure 2.
Appendix 7—figure 2.. Search times as a function of EB and n~.
(a) ttotal, t3D, and t1D vs. at fixed L∗ and n~∗. The red solid line is the prediction of t3D from Equation 5, and the blue line (also in the inset) is the exponential fit for t1D. The black curve in the inset is the number of rounds the TF hits the antenna, nhits, vs. EB. (b) ttotal, t3D and t1D vs. n~ at fixed L∗ and EB∗. The red and blue solid lines represent the predictions of t3D and t1D from Equation 5, respectively. nhits varying with n~ is shown in the inset.
Appendix 9—figure 1.
Appendix 9—figure 1.. Left: an example of the target configuration (the antenna) generated by the worm-like chain model, where L=300bp, d=10bp, and the persistence length also equals 10bp.
Right: Mean total search times weakly vary with persistence length when targets are generated using the worm-like chain model. The values are represented by the black dots, with parameters set to EB=11, L=300 bp, and n~=4. For each persistence length, we obtained ten mean total search times by considering ten different target configurations. The mean total search times do not exhibit significant differences compared to that for targets arranged linearly (indicated by the horizontal orange line).
Appendix 10—figure 1.
Appendix 10—figure 1.. Search in a complex DNA configuration.
(a) Configuration of DNA (in red) at ν=0.008 with a complex structure and a trajectory of TF (in black). A TF acts as a point searcher that diffuses in 3D space and becomes trapped once it hits the DNA, which is 1nm wide. It detaches at a rate of D31nm2exp⁡(−Enonsp/kBT). (b) MSD at different Enonsp.
Appendix 11—figure 1.
Appendix 11—figure 1.. Property of 1D octopusing walk.
(a) Wn,n+1 (or Wn,n−1) denoted by ‘on’ (or ‘off’) varies with n for EB=6 and n~=12. Inset: Mean square displacement (MSD) suggests diffusive motion, as indicated by the dashed line following MSD∝t. (b) Steady probability distribution of bound sites.
See this image and copyright information in PMC

Update of

  • doi: 10.48550/arXiv.2307.11180
  • doi: 10.7554/eLife.104956.1
  • doi: 10.7554/eLife.104956.2

References

    1. Afek A, Schipper JL, Horton J, Gordân R, Lukatsky DB. Protein−DNA binding in the absence of specific base-pair recognition. PNAS. 2014;111:17140–17145. doi: 10.1073/pnas.1410569111. - DOI - PMC - PubMed
    1. Bénichou O, Loverdo C, Moreau M, Voituriez R. Intermittent search strategies. Reviews of Modern Physics. 2011;83:81–129. doi: 10.1103/RevModPhys.83.81. - DOI
    1. Berg HC, Purcell EM. Physics of chemoreception. Biophysical Journal. 1977;20:193–219. doi: 10.1016/S0006-3495(77)85544-6. - DOI - PMC - PubMed
    1. Berg OG, Winter RB, Von Hippel PH. Diffusion-driven mechanisms of protein translocation on nucleic acids. 1. models and theory. Biochemistry. 1981;20:6929–6948. doi: 10.1021/bi00527a028. - DOI - PubMed
    1. Berg OG, von Hippel PH. Selection of DNA binding sites by regulatory proteins. Journal of Molecular Biology. 1987;193:723–743. doi: 10.1016/0022-2836(87)90354-8. - DOI - PubMed

MeSH terms

Substances

LinkOut - more resources