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).
Wright, P. E. & Dyson, H. J. Intrinsically disordered proteins in cellular signalling and regulation. Nat. Rev. Mol. Cell Biol. 16, 18–29 (2015).
Jonas, S. & Izaurralde, E. The role of disordered protein regions in the assembly of decapping complexes and RNP granules. Genes Dev. 27, 2628–2641 (2013).
Staller, M. V. et al. A high-throughput mutational scan of an intrinsically disordered acidic transcriptional activation domain. Cell Syst. 6, 444–455 (2018).
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).
Sanborn, A. L. et al. Simple biochemical features underlie transcriptional activation domain diversity and dynamic, fuzzy binding to Mediator. eLife 10, e68068 (2021).
Van Roey, K. et al. Short linear motifs: ubiquitous and functionally diverse protein interaction modules directing cell regulation. Chem. Rev. 114, 6733–6778 (2014).
Calabretta, S. & Richard, S. Emerging roles of disordered sequences in RNA-binding proteins. Trends Biochem. Sci 40, 662–672 (2015).
He, S., Valkov, E., Cheloufi, S. & Murn, J. The nexus between RNA-binding proteins and their effectors. Nat. Rev. Genet. 24, 276–294 (2023).
Van Nostrand, E. L. et al. A large-scale binding and functional map of human RNA-binding proteins. Nature 583, 711–719 (2020).
Dominguez, D. et al. Sequence, structure, and context preferences of human RNA binding proteins. Mol. Cell 70, 854–867 (2018).
Passmore, L. A. & Coller, J. Roles of mRNA poly(A) tails in regulation of eukaryotic gene expression. Nat. Rev. Mol. Cell Biol. 23, 93–106 (2022).
Keryer-Bibens, C., Barreau, C. & Osborne, H. B. Tethering of proteins to RNAs by bacteriophage proteins. Biol. Cell 100, 125–138 (2008).
Luke, B. et al. Saccharomyces cerevisiae Ebs1p is a putative ortholog of human Smg7 and promotes nonsense-mediated mRNA decay. Nucleic Acids Res. 35, 7688–7697 (2007).
Reynaud, K., McGeachy, A., Noble, D., Meacham, Z. & Ingolia, N. Surveying the global landscape of post-transcriptional regulators. Nat. Struct. Mol. Biol. 30, 740–752 (2023).
Matlashov, M. E. et al. A set of monomeric near-infrared fluorescent proteins for multicolor imaging across scales. Nat. Commun. 11, 239 (2020).
Peterman, N. & Levine, E. Sort-seq under the hood: Implications of design choices on large-scale characterization of sequence–function relations. BMC Genomics 17, 206 (2016).
Dvir, S. et al. Deciphering the rules by which 5′-UTR sequences affect protein expression in yeast. Proc. Natl Acad. Sci. USA 110, E2792–E2801 (2013).
Hook, B. A., Goldstrohm, A. C., Seay, D. J. & Wickens, M. Two yeast PUF proteins negatively regulate a single mRNA. J. Biol. Chem. 282, 15430–15438 (2007).
Bresson, S., Tuck, A., Staneva, D. & Tollervey, D. Nuclear RNA decay pathways aid rapid remodeling of gene expression in yeast. Mol. Cell 65, 787–800.e5 (2017).
Webster, M. W., Stowell, J. A. & Passmore, L. A. RNA-binding proteins distinguish between similar sequence motifs to promote targeted deadenylation by Ccr4–Not. eLife 8, e40670 (2019).
Fabian, M. R. et al. Structural basis for the recruitment of the human CCR4–NOT deadenylase complex by tristetraprolin. Nat. Struct. Mol. Biol. 20, 735–739 (2013).
Keskeny, C. et al. A conserved CAF40-binding motif in metazoan NOT4 mediates association with the CCR4–NOT complex. Genes Dev. 33, 236–252 (2019).
Parker, R. RNA degradation in Saccharomyces cerevisae. Genetics 191, 671–702 (2012).
Nishimura, K. & Kanemaki, M. T. Rapid depletion of budding yeast proteins via the fusion of an auxin-inducible degron (AID). Curr. Protoc. Cell Biol. 2014, 20.9.1–20.9.16 (2014).
Mugridge, J. S., Tibble, R. W., Ziemniak, M., Jemielity, J. & Gross, J. D. Structure of the activated Edc1–Dcp1–Dcp2–Edc3 mRNA decapping complex with substrate analog poised for catalysis. Nat. Commun. 9, 1152 (2018).
Varier, R. A. et al. m6A reader Pho92 is recruited co-transcriptionally and couples translation efficacy to mRNA decay to promote meiotic fitness in yeast. eLife 11, e84034 (2022).
Gruner, S. et al. Structural motifs in eIF4G and 4E-BPs modulate their binding to eIF4E to regulate translation initiation in yeast. Nucleic Acids Res. 46, 6893–6908 (2018).
Puig, S., Askeland, E. & Thiele, D. J. Coordinated remodeling of cellular metabolism during iron deficiency through targeted mRNA degradation. Cell 120, 99–110 (2005).
Peter, D. et al. Molecular architecture of 4E-BP translational inhibitors bound to eIF4E. Mol. Cell 57, 1074–1087 (2015).
Blewett, N. H. & Goldstrohm, A. C. A eukaryotic translation initiation factor 4E-binding protein promotes mRNA decapping and is required for PUF repression. Mol. Cell. Biol. 32, 4181–4194 (2012).
Cridge, A. G. et al. Identifying eIF4E-binding protein translationally-controlled transcripts reveals links to mRNAs bound by specific PUF proteins. Nucleic Acids Res. 38, 8039–8050 (2010).
Martin, E. W. et al. Valence and patterning of aromatic residues determine the phase behavior of prion-like domains. Science 367, 694–699 (2020).
Rives, A. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl Acad. Sci. USA 118, e2016239118 (2021).
Meier, J. et al. Language models enable zero-shot prediction of the effects of mutations on protein function. Adv. Neural Inf. Process. Syst. 35, 29287–29303 (2021).
Stärk, H., Dallago, C., Heinzinger, M. & Rost, B. Light attention predicts protein location from the language of life. Bioinform. Adv. 1, vbab035 (2021).
Zarin, T. et al. Proteome-wide signatures of function in highly diverged intrinsically disordered regions. eLife 8, e46883 (2019).
Koren, I. et al. The eukaryotic proteome is shaped by E3 ubiquitin ligases targeting C-terminal degrons. Cell 173, 1622–1635 (2018).
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).
Gietz, R. D. & Schiestl, R. H. High-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method. Nat. Protoc. 2, 31–34 (2007).
Stovicek, V., Borja, G. M., Forster, J. & Borodina, I. EasyClone 2.0: expanded toolkit of integrative vectors for stable gene expression in industrial Saccharomyces cerevisiae strains. J. Ind. Microbiol. Biotechnol. 42, 1519–1531 (2015).
Hahne, F. et al. flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinformatics 10, 106 (2009).
Mészáros, B., Erdös, G. & Dosztányi, Z. IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucleic Acids Res. 46, W329–W337 (2018).
McGeachy, A. M., Meacham, Z. A. & Ingolia, N. T. An accessible continuous-culture turbidostat for pooled analysis of complex libraries. ACS Synth. Biol. 8, 844–856 (2019).
Muller, R., Meacham, Z., Ferguson, L. & Ingolia, N. CiBER-seq dissects genetic networks by quantitative CRISPRi profiling of expression phenotypes. Science 370, eabb9662 (2020).
Green, M. R. & Sambrook, J. Total RNA extraction from Saccharomyces cerevisiae using hot acid phenol. Cold Spring Harb. Protoc. 2021, 523–525 (2021).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).
Thomas, P. D. et al. PANTHER: making genome-scale phylogenetics accessible to all. Protein Sci. 31, 8–22 (2022).
Hentze, M. W., Castello, A., Schwarzl, T. & Preiss, T. A brave new world of RNA-binding proteins. Nat. Rev. Mol. Cell Biol. 19, 327–341 (2018).
Conway, J. R., Lex, A. & Gehlenborg, N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 33, 2938–2940 (2017).
Visser, I. & Speekenbrink, M. depmixS4: an R package for hidden Markov models. J. Stat. Softw. 36, 1–21 (2010).
Vacic, V., Uversky, V. N., Dunker, A. K. & Lonardi, S. Composition Profiler: a tool for discovery and visualization of amino acid composition differences. BMC Bioinformatics 8, 211 (2007).
Byrne, K. P. & Wolfe, K. H. The Yeast Gene Order Browser: combining curated homology and syntenic context reveals gene fate in polyploid species. Genome Res. 15, 1456–1461 (2005).
Waterhouse, A. M., Procter, J. B., Martin, D. M. A., Clamp, M. & Barton, G. J. Jalview Version 2 — a multiple sequence alignment editor and analysis workbench. Bioinformatics 25, 1189–1191 (2009).
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).
Nguyen Ba, A. N. et al. Proteome-wide discovery of evolutionary conserved sequences in disordered regions. Sci. Signal. 5, rs1 (2012).
Bodenhofer, U., Bonatesta, E., Horejš-Kainrath, C. & Hochreiter, S. msa: An R package for multiple sequence alignment. Bioinformatics 31, 3997–3999 (2015).
Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).
Barupal, D. K. & Fiehn, O. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Holehouse, A. S., Das, R. K., Ahad, J. N., Richardson, M. O. G. & Pappu, R. V. CIDER: resources to analyze sequence–ensemble relationships of intrinsically disordered proteins. Biophys. J. 112, 16–21 (2017).
Lobel, J. H. & Ingolia, N. T. Deciphering disordered regions controlling mRNA decay in high-throughput. Zenodo https://doi.org/10.5281/zenodo.14708299 (2025).