-Feature-CPG--Tears-as-the-next-generation-of-diagnostics-and-monitoring---439100-1:Widescreen?$responsive$&qlt=90,0&resMode=sharp2&fmt=webp&op_sharpen=1 "Our tears can help diagnose cancer, Parkinson’s, and Alzheimer’s")
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).
Albanese, K. I., Barbe, S., Tagami, S., Woolfson, D. N. & Schiex, T. Computational protein design. Nat. Rev. Methods Primers 5, 13 (2025).
Tesei, G., Pesce, F. & Lindorff-Larsen, K. Computational design of intrinsically disordered proteins. Curr. Opin. Struct. Biol. 96, 103210 (2026).
Moses, D. et al. Structural biases in disordered proteins are prevalent in the cell. Nat. Struct. Mol. Biol. 31, 283–292 (2024).
Das, R. K., Ruff, K. M. & Pappu, R. V. Relating sequence encoded information to form and function of intrinsically disordered proteins. Curr. Opin. Struct. Biol. 32, 102–112 (2015).
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).
Staller, M. V. et al. A high-throughput mutational scan of an intrinsically disordered acidic transcriptional activation domain. Cell Syst. 6, 444–455 (2018).
Pesce, F. et al. Design of intrinsically disordered protein variants with diverse structural properties. Sci. Adv. 10, eadm9926 (2024).
Zarin, T., Tsai, C. N., Nguyen Ba, A. N. & Moses, A. M. Selection maintains signaling function of a highly diverged intrinsically disordered region. Proc. Natl Acad. Sci. USA 114, E1450–E1459 (2017).
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).
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).
Novak, B., Lotthammer, J. M., Emenecker, R. J. & Holehouse, A. S. Accurate predictions of disordered protein ensembles with STARLING. Nature 652, 240–250 (2026).
González-Foutel, N. S. et al. Conformational buffering underlies functional selection in intrinsically disordered protein regions. Nat. Struct. Mol. Biol. 29, 781–790 (2022).
Zeno, W. F. et al. Molecular mechanisms of membrane curvature sensing by a disordered protein. J. Am. Chem. Soc. 141, 10361–10371 (2019).
Halladin, D. K. et al. Entropy-driven translocation of disordered proteins through the Gram-positive bacterial cell wall. Nat. Microbiol. 6, 1055–1065 (2021).
Berlow, R. B., Dyson, H. J. & Wright, P. E. Hypersensitive termination of the hypoxic response by a disordered protein switch. Nature 543, 447–451 (2017).
Schuler, B., König, I., Soranno, A. & Nettels, D. Impact of in-cell and in-vitro crowding on the conformations and dynamics of an intrinsically disordered protein. Angew. Chem. Int. Ed. https://doi.org/10.1002/anie.202016804 (2021).
Ruff, K. M. et al Molecular grammars of predicted intrinsically disordered regions that span the human proteome. Cell 189, 323–342 (2025).
Moses, D. et al. Revealing the hidden sensitivity of intrinsically disordered proteins to their chemical environment. J. Phys. Chem. Lett. 11, 10131–10136 (2020).
Marsh, J. A. & Forman-Kay, J. D. Sequence determinants of compaction in intrinsically disordered proteins. Biophys. J. 98, 2383–2390 (2010).
Müller-Späth, S. et al. From the cover: charge interactions can dominate the dimensions of intrinsically disordered proteins. Proc. Natl Acad. Sci. USA 107, 14609–14614 (2010).
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).
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).
Boeynaems, S. et al. Aberrant phase separation is a common killing strategy of positively charged peptides in biology and human disease. Preprint at bioRxiv https://doi.org/10.1101/2023.03.09.531820 (2023).
Schmidt, H. B. & Görlich, D. Transport selectivity of nuclear pores, phase separation, and membraneless organelles. Trends Biochem. Sci. 41, 46–61 (2016).
Enriquez-Toledo, C. et al. A molecular grammar for environmental sensitivity in intrinsically disordered protein regions. Preprint at bioRxiv https://doi.org/10.64898/2026.04.20.719774 (2026).
Soranno, A. et al. Single-molecule spectroscopy reveals polymer effects of disordered proteins in crowded environments. Proc. Natl Acad. Sci. USA 111, 4874–4879 (2014).
Sukenik, S., Ren, P. & Gruebele, M. Weak protein-protein interactions in live cells are quantified by cell-volume modulation. Proc. Natl Acad. Sci. USA 114, 6776–6781 (2017).
Speer, S. L., Stewart, C. J., Sapir, L., Harries, D. & Pielak, G. J. Macromolecular crowding is more than hard-core repulsions. Annu. Rev. Biophys. https://doi.org/10.1146/annurev-biophys-091321-071829 (2022).
Ginell, G. M. et al. Sequence-based prediction of intermolecular interactions driven by disordered regions. Science 388, eadq8381 (2025).
Martin, E. W. et al. Valence and patterning of aromatic residues determine the phase behavior of prion-like domains. Science 367, 694–699 (2020).
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).
Marks, R. A. et al. Life on the dry side: a roadmap to understanding desiccation tolerance and accelerating translational applications. Nat. Commun. 16, 3284 (2025).
Olgenblum, G. I., Hutcheson, B. O., Pielak, G. J. & Harries, D. Protecting proteins from desiccation stress using molecular glasses and gels. Chem. Rev. 124, 5668–5694 (2024).
Hibshman, J. D. & Goldstein, B. LEA motifs promote desiccation tolerance in vivo. BMC Biol. 19, 263 (2021).
Nguyen, K. et al. A phase transition modulates the protective function of a tardigrade disordered protein during desiccation. Protein Sci. 34, e70300 (2025).
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).
Tolleter, D. et al. Structure and function of a mitochondrial late embryogenesis abundant protein are revealed by desiccation. Plant Cell 19, 1580–1589 (2007).
Bremer, A., Wolff, M., Thalhammer, A. & Hincha, D. K. Folding of intrinsically disordered plant LEA proteins is driven by glycerol-induced crowding and the presence of membranes. FEBS J. 284, 919–936 (2017).
Shraddha, K. C. et al. Disordered proteins interact with the chemical environment to tune their protective function during drying. eLife 13, RP97231 (2024).
Nicholson, V., Maharjan, S., Sukenik, S. & Boothby, T. C. OsmoFold: a high-throughput tool for predicting the impact of osmolytes on protein structure. Biophys. J. https://doi.org/10.1016/j.bpj.2025.08.034 (2025).
Malki, A. et al. Fibril structure of desiccation-protective tardigrade protein CAHS-8. Angew. Chem. Int. Ed. 65, e19912 (2026).
Sanchez-Martinez, S. et al. Labile assembly of a tardigrade protein induces biostasis. Protein Sci. 33, e4941 (2024).
Romero-Perez, P. S., Dorone, Y., Flores, E., Sukenik, S. & Boeynaems, S. When phased without water: biophysics of cellular desiccation, from biomolecules to condensates. Chem. Rev. 123, 9010–9035 (2023).
Langstein-Skora, I. et al. Sequence and chemical specificity define the functional landscape of intrinsically disordered regions. Nat. Cell Biol. 28, 323–337 (2026).
Greig, J. A. et al. Arginine-enriched mixed-charge domains provide cohesion for nuclear speckle condensation. Mol. Cell 77, 1237–1250 (2020).
Romero-Pérez, P. S. et al. Protein surface chemistry encodes an adaptive tolerance to desiccation. Cell Syst. 16, 101407 (2025).
Solomon, D. A. et al. C9ORF72-derived polyGR polypeptides disrupt passive nucleocytoplasmic transport by tuning protein affinity for the nuclear pore barrier. Preprint at bioRxiv https://doi.org/10.64898/2026.03.16.711670 (2026).
Alshareedah, I. et al. Sequence-specific interactions determine viscoelasticity and aging dynamics of protein condensates. Nat. Phys. 20, 1482–1491 (2024).
Battaglia, M., Olvera-Carrillo, Y., Garciarrubio, A., Campos, F. & Covarrubias, A. A. The enigmatic LEA proteins and other hydrophilins. Plant Physiol. 148, 6–24 (2008).
Tesei, G. et al. Conformational ensembles of the human intrinsically disordered proteome. Nature 626, 897–904 (2024).
Pritišanac, I. et al. A functional map of the human intrinsically disordered proteome. Proc. Natl Acad. Sci. USA 123, e2604562123 (2026).
Alamdari, S. et al. Protein generation with evolutionary diffusion: sequence is all you need. Preprint at bioRxiv https://doi.org/10.1101/2023.09.11.556673 (2023).
Strome, B., Elemam, K., Pritisanac, I., Forman-Kay, J. D. & Moses, A. M. Computational design of intrinsically disordered protein regions by matching bulk molecular properties. Preprint at bioRxiv https://doi.org/10.1101/2023.04.28.538739 (2023).
Krueger, R. K., Brenner, M. P. & Shrinivas, K. Generalized design of sequence–ensemble–function relationships for intrinsically disordered proteins. Nat. Comput. Sci. 6, 512–523 (2025).
Bhat, S. et al. De novo design of peptide binders to conformationally diverse targets with contrastive language modeling. Sci. Adv. 11, eadr8638 (2025).
Chen, L. T. et al. Target sequence-conditioned design of peptide binders using masked language modeling. Nat. Biotechnol. 44, 1002–1010 (2025).
An, Y., Webb, M. A. & Jacobs, W. M. Active learning of the thermodynamics-dynamics trade-off in protein condensates. Sci. Adv. 10, eadj2448 (2024).
Brown, C. J., Johnson, A. K., Dunker, A. K. & Daughdrill, G. W. Evolution and disorder. Curr. Opin. Struct. Biol. 21, 441–446 (2011).
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).
Liu, J. X. et al. Generative design of intrinsically disordered protein regions with IDiom. Preprint at bioRxiv https://doi.org/10.64898/2026.04.10.717777 (2026).
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).
Tesei, G. & Lindorff-Larsen, K. Improved predictions of phase behaviour of intrinsically disordered proteins by tuning the interaction range. Open Res. Eur. 2, 94 (2022).
Lotthammer, J. M. et al Metapredict enables accurate disorder prediction across the Tree of Life. Preprint at bioRxiv https://doi.org/10.1101/2024.11.05.622168 (2024).
Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).
Thevenaz, P., Ruttimann, U. E. & Unser, M. A pyramid approach to subpixel registration based on intensity. IEEE Trans. Image Process. 7, 27–41 (1998).
Abraham, M. J. et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).
Wang, L., Brasnett, C., Borges-Araújo, L., Souza, P. C. T. & Marrink, S. J. Martini3-IDP: improved Martini 3 force field for disordered proteins. Nat. Commun. 16, 2874 (2025).
Grünewald, F. et al. Polyply; a Python suite for facilitating simulations of macromolecules and nanomaterials. Nat. Commun. 13, 68 (2022).
Lambert, T. J. FPbase: a community-editable fluorescent protein database. Nat. Methods 16, 277–278 (2019).
Joska, T. M., Mashruwala, A., Boyd, J. M. & Belden, W. J. A universal cloning method based on yeast homologous recombination that is simple, efficient, and versatile. J. Microbiol. Methods 100, 46–51 (2014).
Hood, S. & Lewis, J. A. High-efficiency yeast electroporation v.2. Protocols.io https://doi.org/10.17504/protocols.io.5qpvorr69v4o/v2 (2022).
Calahan, D., Dunham, M., DeSevo, C. & Koshland, D. E. Genetic analysis of desiccation tolerance in Saccharomyces cerevisiae. Genetics 189, 507–519 (2011).
Stokes, R. H. & Robinson, R. A. Standard solutions for humidity control at 25 °C. Ind. Eng. Chem. 41, 2013–2013 (1949).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Dunham, M., Gartenberg, M. & Brown, G. W. Methods in Yeast Genetics and Genomics: A Cold Spring Harbor Laboratory Course Manual, 2015 Edition (Cold Spring Harbor Laboratory Press, 2015)
Li, C. H. & Lee, C. K. Minimum cross entropy thresholding. Pattern Recognit. 26, 617–625 (1993).
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
Hunter, K. et al. Data for ‘Rational design of disordered proteins for sequence–function investigation’. Zenodo https://doi.org/10.5281/zenodo.18717144 (2026).
