Strader, L., Weijers, D. & Wagner, D. Plant transcription factors—being in the right place with the right company. Curr. Opin. Plant Biol. 65, 102136 (2022).
O’Malley, R. C. et al. Cistrome and epicistrome features shape the regulatory DNA landscape. Cell 165, 1280–1292 (2016).
Galli, M. et al. The DNA binding landscape of the maize AUXIN RESPONSE FACTOR family. Nat. Commun. 9, 4526 (2018).
Sanborn, A. L. et al. Simple biochemical features underlie transcriptional activation domain diversity and dynamic, fuzzy binding to Mediator. eLife 10, e68068 (2021).
Dyson, H. J. & Wright, P. E. Role of Intrinsic protein disorder in the function and interactions of the transcriptional coactivators CREB-binding protein (CBP) and p300. J. Biol. Chem. 291, 6714–6722 (2016).
Ferreira, M. E. et al. Mechanism of transcription factor recruitment by acidic activators. J. Biol. Chem. 280, 21779–21784 (2005).
Hermann, S., Berndt, K. D. & Wright, A. P. How transcriptional activators bind target proteins. J. Biol. Chem. 276, 40127–40132 (2001).
Kim, J. Y. & Chung, H. S. Disordered proteins follow diverse transition paths as they fold and bind to a partner. Science 368, 1253–1257 (2020).
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).
Kotha, S. R. & Staller, M. V. Clusters of acidic and hydrophobic residues can predict acidic transcriptional activation domains from protein sequence. Genetics 225, iyad131 (2023).
Hummel, N. F. C. et al. The trans-regulatory landscape of gene networks in plants. Cell Syst. 14, 501–511 (2023).
Staller, M. V. et al. A high-throughput mutational scan of an intrinsically disordered acidic transcriptional activation domain. Cell Syst. 6, 444–455 (2018).
Konishi, M. & Yanagisawa, S. The role of protein–protein interactions mediated by the PB1 domain of NLP transcription factors in nitrate-inducible gene expression. BMC Plant Biol. 19, 90 (2019).
Hahn, S. & Young, E. T. Transcriptional regulation in Saccharomyces cerevisiae: transcription factor regulation and function, mechanisms of initiation, and roles of activators and coactivators. Genetics 189, 705–736 (2011).
Emenecker, R. J., Griffith, D. & Holehouse, A. S. Metapredict: a fast, accurate, and easy-to-use predictor of consensus disorder and structure. Biophys. J. 120, 4312–4319 (2021).
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).
Hope, I. A. & Struhl, K. Functional dissection of a eukaryotic transcriptional activator protein, GCN4 of yeast. Cell 46, 885–894 (1986).
Mitchell, P. J. & Tjian, R. Transcriptional regulation in mammalian cells by sequence-specific DNA binding proteins. Science 245, 371–378 (1989).
Mahatma, S. et al. Prediction and functional characterization of transcriptional activation domains. In 57th Annual Conference on Information Sciences and Systems (CISS) 1–6 (2023).
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 (2020).
Lundberg, S. & Lee, S.-I. A unified approach to interpreting model predictions. In Proc. 31st International Conference on Neural Information Processing Systems 4768–4777 (2017).
Hussain, R. M. F., Sheikh, A. H., Haider, I., Quareshy, M. & Linthorst, H. J. M. Arabidopsis WRKY50 and TGA transcription factors synergistically activate expression of PR1. Front. Plant Sci. 9, 930 (2018).
Li, J. et al. Activation domains for controlling plant gene expression using designed transcription factors. Plant Biotechnol. J. 11, 671–680 (2013).
Cho, S. et al. Analysis of the C-terminal region of Arabidopsis thaliana APETALA1 as a transcription activation domain. Plant Mol. Biol. 40, 419–429 (1999).
Sakuma, Y. et al. Functional analysis of an Arabidopsis transcription factor, DREB2A, involved in drought-responsive gene expression. Plant Cell 18, 1292–1309 (2006).
Kotak, S., Port, M., Ganguli, A., Bicker, F. & von Koskull-Doring, P. Characterization of C-terminal domains of Arabidopsis heat stress transcription factors (Hsfs) and identification of a new signature combination of plant class A Hsfs with AHA and NES motifs essential for activator function and intracellular localization. Plant J. 39, 98–112 (2004).
Yoo, C. Y. et al. Direct photoresponsive inhibition of a p53-like transcription activation domain in PIF3 by Arabidopsis phytochrome B. Nat. Commun. 12, 5614 (2021).
Fernandez-Calvo, P. et al. The Arabidopsis bHLH transcription factors MYC3 and MYC4 are targets of JAZ repressors and act additively with MYC2 in the activation of jasmonate responses. Plant Cell 23, 701–715 (2011).
Tiwari, S. B., Hagen, G. & Guilfoyle, T. The roles of auxin response factor domains in auxin-responsive transcription. Plant Cell 15, 533–543 (2003).
Ulmasov, T., Hagen, G. & Guilfoyle, T. J. Activation and repression of transcription by auxin-response factors. Proc. Natl Acad. Sci. USA 96, 5844–5849 (1999).
Pierre-Jerome, E., Jang, S. S., Havens, K. A., Nemhauser, J. L. & Klavins, E. Recapitulation of the forward nuclear auxin response pathway in yeast. Proc. Natl Acad. Sci. USA 111, 9407–2412 (2014).
Powers, S. K. & Strader, L. C. Regulation of auxin transcriptional responses. Dev. Dyn. 249, 483–495 (2020).
Choi, H. S., Seo, M. & Cho, H. T. Two TPL-binding motifs of ARF2 are involved in repression of auxin responses. Front. Plant Sci. 9, 372 (2018).
Hiratsu, K., Matsui, K., Koyama, T. & Ohme-Takagi, M. Dominant repression of target genes by chimeric repressors that include the EAR motif, a repression domain, in Arabidopsis. Plant J. 34, 733–739 (2003).
Mutte, S. K. et al. Origin and evolution of the nuclear auxin response system. eLife 7, e33399 (2018).
DelRosso, N. et al. Large-scale mapping and mutagenesis of human transcriptional effector domains. Nature 616, 365–372 (2023).
Leydon, A. R. et al. Repression by the Arabidopsis TOPLESS corepressor requires association with the core mediator complex. eLife 10, e66739 (2021).
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).
Kagale, S. & Rozwadowski, K. EAR motif-mediated transcriptional repression in plants: an underlying mechanism for epigenetic regulation of gene expression. Epigenetics 6, 141–146 (2011).
Boer, D. R. et al. Structural basis for DNA binding specificity by the auxin-dependent ARF transcription factors. Cell 156, 577–589 (2014).
Korasick, D. A. et al. Molecular basis for AUXIN RESPONSE FACTOR protein interaction and the control of auxin response repression. Proc. Natl Acad. Sci. USA 111, 5427–5432 (2014).
Havens, K. A. et al. A synthetic approach reveals extensive tunability of auxin signaling. Plant Physiol. 160, 135–142 (2012).
Hillson, N. J., Rosengarten, R. D. & Keasling, J. D. j5 DNA assembly design automation software. ACS Synth. Biol. 1, 14–21 (2012).
Garcia-Nafria, J., Watson, J. F. & Greger, I. H. IVA cloning: a single-tube universal cloning system exploiting bacterial in vivo assembly. Sci. Rep. 6, 27459 (2016).
Gietz, R. D. & Schiestl, R. H. High-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method. Nat. Protoc. 2, 31–34 (2007).
Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Kobak, D. & Berens, P. The art of using t-SNE for single-cell transcriptomics. Nat. Commun. 10, 5416 (2019).
Pierre-Jerome, E., Wright, R. C. & Nemhauser, J. L. Characterizing auxin response circuits in Saccharomyces cerevisiae by flow cytometry. Methods Mol. Biol. 1497, 271–281 (2017).
Wright, R. C., Bolten, N. & Pierre-Jerome, E. flowTime: annotation and analysis of biological dynamical systems using flow cytometry. R version 1.24.0 https://www.bioconductor.org/packages/release/bioc/html/flowTime.html (2023).
White, S. et al. FlowKit: a Python toolkit for integrated manual and automated cytometry analysis workflows. Front. Immunol. 12, 768541 (2021).
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).