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Multi-input chemical control of protein dimerization for programming graded cellular responses


Chemical and optogenetic methods for post-translationally controlling protein function have enabled modulation and engineering of cellular functions. However, most of these methods only confer single-input, single-output control. To increase the diversity of post-translational behaviors that can be programmed, we built a system based on a single protein receiver that can integrate multiple drug inputs, including approved therapeutics. Our system translates drug inputs into diverse outputs using a suite of engineered reader proteins to provide variable dimerization states of the receiver protein. We show that our single receiver protein architecture can be used to program a variety of cellular responses, including graded and proportional dual-output control of transcription and mammalian cell signaling. We apply our tools to titrate the competing activities of the Rac and Rho GTPases to control cell morphology. Our versatile tool set will enable researchers to post-translationally program mammalian cellular processes and to engineer cell therapies.

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Fig. 1: Design of a danoprevir/NS3a complex reader.
Fig. 2: Design and testing of a grazoprevir/NS3a complex reader.
Fig. 3: Temporal, graded and proportional transcriptional control using PROCISiR.
Fig. 4: Graded and proportional control of GTPase-driven signaling pathways.

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Data availability

The atomic coordinates and experimental data of the DNCR2/danoprevir/NS3a crystal structure have been deposited in the Protein Data Bank (ID 6N4N). Illumina data are deposited in the Sequence Read Archive under accession number SRP126450. Key DNA constructs are deposited in Addgene. All plasmid sequences are available in this Benchling project:

Code availability

ROSETTA software can be downloaded from and is available free to academic users. Online documentation can be found at Instructions for RosettaScripts syntax are available at A comprehensive list of command line options for ROSETTA can be found at All RosettaScripts written for this study are included in the Supplementary Design Scripts. Python scripts written for data processing are available on github (


  1. Antebi, Y. E. et al. Combinatorial signal perception in the BMP pathway. Cell 170, 1184–1196.e24 (2017).

    Article  CAS  Google Scholar 

  2. Giorgetti, L. et al. Noncooperative interactions between transcription factors and clustered DNA binding sites enable graded transcriptional responses to environmental inputs. Mol. Cell 37, 418–428 (2010).

    Article  CAS  Google Scholar 

  3. Freed, D. M. et al. EGFR ligands differentially stabilize receptor dimers to specify signaling kinetics. Cell 171, 683–695.e18 (2017).

    Article  CAS  Google Scholar 

  4. Daniel, R., Rubens, J. R., Sarpeshkar, R. & Lu, T. K. Synthetic analog computation in living cells. Nature 497, 619–623 (2013).

    Article  CAS  Google Scholar 

  5. Weinberg, B. H. et al. Large-scale design of robust genetic circuits with multiple inputs and outputs for mammalian cells. Nat. Biotechnol. 35, 453–462 (2017).

    Article  CAS  Google Scholar 

  6. Stanton, B. Z., Chory, E. J. & Crabtree, G. R. Chemically induced proximity in biology and medicine. Science 359, eaao5902 (2018).

    Article  Google Scholar 

  7. Banaszynski, L. A., Chen, L.-C., Maynard-Smith, L. A., Ooi, A. G. L. & Wandless, T. J. A rapid, reversible, and tunable method to regulate protein function in living cells using synthetic small molecules. Cell 126, 995–1004 (2006).

    Article  CAS  Google Scholar 

  8. Chung, H. K. et al. Tunable and reversible drug control of protein production via a self-excising degron. Nat. Chem. Biol. 11, 713–720 (2015).

    Article  CAS  Google Scholar 

  9. Lin, M. Z. & Tsien, R. Y. TimeSTAMP tagging of newly synthesized proteins. Curr. Protoc. Protein Sci. Chapter 26, Unit 26.5–26.5.11 (2010).

    PubMed  Google Scholar 

  10. Tague, E. P., Dotson, H. L., Tunney, S. N., Sloas, D. C. & Ngo, J. T. Chemogenetic control of gene expression and cell signaling with antiviral drugs. Nat. Methods 15, 519–522 (2018).

    Article  CAS  Google Scholar 

  11. Jacobs, C. L., Badiee, R. K. & Lin, M. Z. StaPLs: versatile genetically encoded modules for engineering drug-inducible proteins. Nat. Methods 15, 523–526 (2018).

    Article  CAS  Google Scholar 

  12. Spencer, D. M., Wandless, T. J., Schreiber, S. L. & Crabtree, G. R. Controlling signal transduction with synthetic ligands. Science 262, 1019–1024 (1993).

    Article  CAS  Google Scholar 

  13. Liang, F.-S., Ho, W. Q. & Crabtree, G. R. Engineering the ABA plant stress pathway for regulation of induced proximity. Sci. Signal. 4, rs2–rs2 (2011).

    Article  Google Scholar 

  14. Miyamoto, T. et al. Rapid and orthogonal logic gating with a gibberellin-induced dimerization system. Nat. Chem. Biol. 8, 465–470 (2012).

    Article  CAS  Google Scholar 

  15. Hill, Z. B., Martinko, A. J., Nguyen, D. P. & Wells, J. A. Human antibody-based chemically induced dimerizers for cell therapeutic applications. Nat. Chem. Biol. 14, 112–117 (2018).

    Article  CAS  Google Scholar 

  16. Gao, Y. et al. Complex transcriptional modulation with orthogonal and inducible dCas9 regulators. Nat. Methods 13, 1043 EP–1041049 (2016).

    Article  Google Scholar 

  17. Gao, X. J., Chong, L. S., Kim, M. S. & Elowitz, M. B. Programmable protein circuits in living cells. Science 361, 1252–1258 (2018).

    Article  CAS  Google Scholar 

  18. Fleishman, S. J. et al. RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite. PLoS ONE 6, e20161 (2011).

    Article  CAS  Google Scholar 

  19. Park, K. et al. Control of repeat-protein curvature by computational protein design. Nat. Struct. Mol. Biol. 22, 167–174 (2015).

    Article  CAS  Google Scholar 

  20. Brunette, T. J. et al. Exploring the repeat protein universe through computational protein design. Nature 528, 580–584 (2015).

    Article  CAS  Google Scholar 

  21. King, I. C. et al. Precise assembly of complex beta sheet topologies from de novo designed building blocks. eLife 4, e11012 (2015).

    Article  Google Scholar 

  22. Schneidman-Duhovny, D., Inbar, Y., Nussinov, R. & Wolfson, H. J. PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res. 33, 7 (2005).

    Article  Google Scholar 

  23. Romano, K. P. et al. The molecular basis of drug resistance against hepatitis C virus NS3/4A protease inhibitors. PLoS Pathog. 8, e1002832 (2012).

    Article  CAS  Google Scholar 

  24. Soumana, D. I., Ali, A. & Schiffer, C. A. Structural analysis of asunaprevir resistance in HCV NS3/4A protease. ACS Chem. Biol. 9, 2485–2490 (2014).

    Article  CAS  Google Scholar 

  25. Dou, J. et al. De novo design of a fluorescence-activating β-barrel. Nature 561, 485–491 (2018).

    Article  CAS  Google Scholar 

  26. Kügler, J. et al. High affinity peptide inhibitors of the hepatitis C virus NS3-4A protease refractory to common resistant mutants. J. Biol. Chem. 287, 39224–39232 (2012).

    Article  Google Scholar 

  27. Cunningham-Bryant, D. et al. A chemically disrupted proximity system for controlling dynamic cellular processes. J. Am. Chem. Soc. 141, 3352–3355 (2019).

    Article  CAS  Google Scholar 

  28. Suh, B.-C., Inoue, T., Meyer, T. & Hille, B. Rapid chemically induced changes of PtdIns(4,5)P2 gate KCNQ ion channels. Science 314, 1454–1457 (2006).

    Article  CAS  Google Scholar 

  29. Chavez, A. et al. Highly efficient Cas9-mediated transcriptional programming. Nat. Methods 12, 326–328 (2015).

    Article  CAS  Google Scholar 

  30. Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).

    Article  CAS  Google Scholar 

  31. Loew, R., Heinz, N., Hampf, M., Bujard, H. & Gossen, M. Improved Tet-responsive promoters with minimized background expression. BMC Biotechnol. 10, 81 (2010).

    Article  Google Scholar 

  32. Zalatan, J. G. et al. Engineering complex synthetic transcriptional programs with CRISPR RNA scaffolds. Cell 160, 339–350 (2015).

    Article  CAS  Google Scholar 

  33. MacKay, J. L. & Kumar, S. Simultaneous and independent tuning of RhoA and Rac1 activity with orthogonally inducible promoters. Integr. Biol. (Camb.) 6, 885–894 (2014).

    Article  CAS  Google Scholar 

  34. de Leuw, P. & Stephan, C. Protease inhibitor therapy for hepatitis C virus-infection. Expert Opin. Pharmacother. 19, 577–587 (2018).

    Article  Google Scholar 

  35. Di Stasi, A. et al. Inducible apoptosis as a safety switch for adoptive cell therapy. N. Engl. J. Med. 365, 1673–1683 (2011).

    Article  Google Scholar 

  36. Stavrou, M. et al. A rapamycin-activated caspase 9-based suicide gene. Mol. Ther. 26, 1266–1276 (2018).

    Article  CAS  Google Scholar 

  37. Wu, C.-Y., Roybal, K. T., Puchner, E. M., Onuffer, J. & Lim, W. A. Remote control of therapeutic T cells through a small molecule–gated chimeric receptor. Science 350, aab4077–aab4077 (2015).

    Article  Google Scholar 

  38. Rose, J. C. et al. A computationally engineered RAS rheostat reveals RAS-ERK signaling dynamics. Nat. Chem. Biol. 13, 119–126 (2017).

    Article  CAS  Google Scholar 

  39. Deaner, M. & Alper, H. S. Systematic testing of enzyme perturbation sensitivities via graded dCas9 modulation in Saccharomyces cerevisiae. Metab. Eng. 40, 14–22 (2017).

    Article  CAS  Google Scholar 

  40. Escalante-Chong, R. et al. Galactose metabolic genes in yeast respond to a ratio of galactose and glucose. Proc. Natl Acad. Sci. USA 112, 1636–1641 (2015).

    Article  CAS  Google Scholar 

  41. Morsut, L. et al. Engineering customized cell sensing and response behaviors using synthetic notch receptors. Cell 164, 780–791 (2016).

    Article  CAS  Google Scholar 

  42. Wittekind, M., Weinheimer, S. & Zhang, Y. Modified forms of hepatitis C NS3 protease for facilitating inhibitor screening and structural studies of protease:inhibitor complexes. US patent 6,800,456 (2004).

  43. Tsao, K.-L., Debarbieri, B., Michel, H. & Waugh, D. S. A versatile plasmid expression vector for the production of biotinylated proteins by site-specific, enzymatic modification in Escherichia coli. Gene 169, 59–64 (1996).

    Article  CAS  Google Scholar 

  44. Fleishman, S. J. et al. Computational design of proteins targeting the conserved stem region of influenza hemagglutinin. Science 332, 816–821 (2011).

    Article  CAS  Google Scholar 

  45. Otwinowski, Z. & Minor, W. Processing of X-ray diffraction data collected in oscillation mode. Meth. Enzymol. 276, 307–326 (1997).

    Article  CAS  Google Scholar 

  46. Romano, K. P., Ali, A., Royer, W. E. & Schiffer, C. A. Drug resistance against HCV NS3/4A inhibitors is defined by the balance of substrate recognition versus inhibitor binding. Proc. Natl Acad. Sci. USA 107, 20986–20991 (2010).

    Article  CAS  Google Scholar 

  47. Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).

    Article  CAS  Google Scholar 

  48. Collaborative Computational Project, Number 4. The CCP4 suite: programs for protein crystallography. Acta Crystallogr. D 50, 760–763 (1994).

    Article  Google Scholar 

  49. Chen, T. S., Palacios, H. & Keating, A. E. Structure-based redesign of the binding specificity of anti-apoptotic Bcl-x(L). J. Mol. Biol. 425, 171–185 (2013).

    Article  CAS  Google Scholar 

  50. Dutta, S., Chen, T. S. & Keating, A. E. Peptide ligands for pro-survival protein Bfl-1 from computationally guided library screening. ACS Chem. Biol. 8, 778–788 (2013).

    Article  CAS  Google Scholar 

  51. Foight, G. W., Chen, T. S., Richman, D. & Keating, A. E. Enriching peptide libraries for binding affinity and specificity through computationally directed library design. Methods Mol. Biol. 1561, 213–232 (2017).

    Article  CAS  Google Scholar 

  52. Procko, E. et al. Computational design of a protein-based enzyme inhibitor. J. Mol. Biol. 425, 3563–3575 (2013).

    Article  CAS  Google Scholar 

  53. Berger, S. et al. Computationally designed high specificity inhibitors delineate the roles of BCL2 family proteins in cancer. eLife 5, e20352 (2016).

    Article  Google Scholar 

  54. Fowler, D. M., Araya, C. L., Gerard, W. & Fields, S. Enrich: software for analysis of protein function by enrichment and depletion of variants. Bioinformatics 27, 3430–3431 (2011).

    Article  CAS  Google Scholar 

  55. Costes, S. V. et al. Automatic and quantitative measurement of protein–protein colocalization in live cells. Biophys. J. 86, 3993–4003 (2004).

    Article  CAS  Google Scholar 

  56. Barry, D. J., Durkin, C. H., Abella, J. V. & Way, M. Open source software for quantification of cell migration, protrusions, and fluorescence intensities. J. Cell Biol. 209, 163–180 (2015).

    Article  CAS  Google Scholar 

  57. Matreyek, K. A., Stephany, J. J. & Fowler, D. M. A platform for functional assessment of large variant libraries in mammalian cells. Nucleic Acids Res. 45, e102 (2017).

    Article  Google Scholar 

  58. Untergasser, A. et al. Primer3—new capabilities and interfaces. Nucleic Acids Res. 40, e115–e115 (2012).

    Article  CAS  Google Scholar 

  59. Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25, 402–408 (2001).

    Article  CAS  Google Scholar 

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We acknowledge the Institute for Protein Design Protein Production Core for production of proteins. We thank D. Fowler and J. Zalatan and their laboratories for helpful discussions and materials, G. Butterfield and J. Nelson for assistance with Illumina sequencing, and E. Dieter (UW, Seattle) and J. Rose (UW, Seattle) for construction of stable cell line resources. This work was supported by the NIH (grant no. R01GM086858 (D.J.M.) and grant no. S10 OD016240 (UW W.M. Keck Center)). D.B. acknowledges support from HHMI. Design calculations were facilitated though the use of advanced computational, storage and networking infrastructure provided by the Hyak supercomputer system at the University of Washington. We are grateful to the staff at Advanced Light Source beamlines BL 8.2.1 and 8.2.2 for assistance with synchrotron data collection. G.W.F. and Z.W. were funded by Washington Research Foundation Innovation Postdoctoral Fellowships.

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Authors and Affiliations



G.W.F., Z.W., D.B. and D.J.M. conceived of the work. G.W.F. and D.J.M. wrote the manuscript. G.W.F. designed and validated the grazoprevir/NS3a reader, performed all improvement and validation of the danoprevir/NS3a reader and completed and analyzed all mammalian cell experiments. Z.W. performed the danoprevir/NS3a reader crystallography. Z.W., P.J.G. and K.P. wrote the design script and contributed the danoprevir/NS3a reader design. C.T.W. designed and screened the grazoprevir/NS3a reader library. K.M.W. performed the CD95 titration. D.C.-B. established the ANR/NS3a system. T.B. contributed the DHR scaffold set. W.S. created RIFdock.

Corresponding author

Correspondence to Dustin J. Maly.

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Competing interests

The authors G.W.F., Z.W., C.T.W., P.J.G., D.C.-B., K.P., D.B. and D.J.M. are inventors on US Patent application 62/775,171, ‘Reagents and Methods for Controlling Protein Function and Interaction’, submitted by the University of Washington.

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Integrated supplementary information

Supplementary Figure 1 Design and characterization of danoprevir:NS3a complex reader libraries.

a, Process of Rosetta re-design-informed design of a combinatorial D5 interface library. b, Enrichment ratios of the DNCR1 site saturation mutagenesis (SSM) library sorted for (positive sort, top) or against (negative sort, bottom) binding to 50 nM NS3a in the presence of 500 nM danoprevir. The color scale has been flipped for the negative sort such that for both sorts, blue corresponds to predicted weaker binders, and red corresponds to predicted tighter binders. Gray boxes with letters are the wild-type residue and other gray boxes are positions with <15 counts in the naïve library sequencing results. c, Sequence logos of the theoretical library for the second combinatorial library varying the DNCR1 interface (top), and the mutations found in the final enriched clones (bottom). Residue identities at the varied positions are indicated for the starting DNCR1 and final DNCR2. d, Progression of binding improvement from DHR79 to D5 to DNCR1 to DNCR2 as measured by the deviation from average enrichment ratio of the DNCR1 SSM values at each position. Gray shaded region indicates the range of enrichment ratios of all amino acids at each position, and vertical gray bars indicate positions at the interface.

Supplementary Figure 2 Analysis of the DNCR2:danoprevir:NS3a complex crystal structure and the specificities of drug/NS3a complex reader proteins.

a, 1 µM NS3a with avidity binding to yeast-displayed D5, DNCR1, or DNCR2. Technical triplicates and their mean are shown. b, Binding of 1 nM NS3a to DNCR2 displayed on the surface of yeast in the presence of increasing concentrations of danoprevir. Technical triplicates are shown. c, An overlay of DNCR2 (blue) from the DNCR2:danoprevir:NS3a complex with the original DHR79 scaffold (orange) crystal structure (PDBID: 5CWP) (Nature 528, 580-584, 2015) . Regions where there are modest changes in the backbone conformation are circled with a dotted line, including missing density for helix 8 and an unraveled helix 7 N-terminus. d, NS3a:danoprevir (blue) from the DNCR2:danoprevir:NS3a complex aligns closely to a crystal structure of NS3a:danoprevir (yellow) alone (PDBID: 3M5L) (PNAS 107, 20986-20991, 2010). e, Size exclusion chromatograms of DNCR2, NS3a, or DNCR2:NS3a complexes in the presence or absence of danoprevir. Representative of three technical replicates. f, Crystal structure of DNCR2:danoprevir:NS3a (blue) aligned to structures of asunaprevir:NS3a (lavender, PDBID: 4WF8) or grazoprevir:NS3a (yellow, PDBID: 3SUD) with clashes (red) between residues of DNCR2 and asunaprevir and grazoprevir highlighted (PLoS Pathog 8, e1002832, 2012; ACS Chem Biol 9, 2485-2490, 2014).

Supplementary Figure 3 Grazoprevir:NS3a complex reader binding and improvement.

a, Predicted mutational preferences of the G3 interface for binding to NS3a:grazoprevir, as defined by the frequencies of mutations found in Rosetta re-designs of the interface. b, Sequence logos of the theoretical library for the combinatorial library varying the G3 interface (top), and the mutations found in the final enriched library (bottom). Residue identities at the varied positions are indicated for the first-generation reader G3 and optimized reader GNCR1. c, Binding of 1 µM NS3a with avidity to yeast-displayed G3 or GNCR1 in the presence of grazoprevir, danoprevir, asunaprevir, or DMSO. Technical triplicates and their means are shown.

Supplementary Figure 4 Characterization of the kinetics and affinity of the DNCR2:danoprevir:NS3a complex in mammalian cells.

a, Kinetics of DNCR2-EGFP association with myristoylated NS3a-mCherry after adding 5 µM danoprevir (time of drug addition is denoted by the dark gray vertical line). Mean and standard deviation of the cytoplasmic EGFP fluorescence (normalized to first and last frame) of 18 NIH3T3 cells collected from 4 separate experiments. b, Schematic of danoprevir-mediated PI3K-Akt pathway activation through recruitment of an inter-Src homology 2 domain (iSH2) of the regulatory PI3K subunit p85/DNCR2 fusion (DNCR2-iSH2) to myristoylated NS3a-mCherry (left panel). Western blots of phospho-AKT (pSer473) and AKT performed in COS-7 cells transfected with control plasmid (GFP), DNCR1-iSH2-only, DNCR2-iSH2-only, myristoyl-NS3a-only, or DNCR1-iSH2 or DNCR2-iSH2 co-transfected with myristoyl-NS3a treated with DMSO or 10 μM danoprevir from one experiment. See Supplementary Figure 15 for the full Western blots. c, Quantification of Western blot in (b) (singlicate for controls; two well replicates for DMSO and danoprevir conditions).

Supplementary Figure 5 Combination of reader pairs for inducible two-location and colocalization control with NS3.

a, Colocalization of NS3a-mCherry with GNCR1-BFP-CAAX or Tom20-DNCR2-EGFP after treatment with danoprevir (5 µM), grazoprevir (5 µM), or DMSO. b, Colocalization of NS3a-mCherry with ANR-BFP-CAAX or NLS-DNCR2-EGFP after treatment with danoprevir (5 µM), grazoprevir (5 µM), or DMSO. See Fig. 2c,d for quantification of multiple cells and associated entry in Supplementary Table 3 for the number of cells and replicates of which these images are representative.

Supplementary Figure 6 Drug-regulated control of subcellular protein localization with intermediate-affinity danoprevir:NS3a reader DNCR1.

a, Colocalization of DNCR1-EGFP with mitochondria-, Golgi-, nuclear-, or plasma membrane-localized NS3a-mCherry under DMSO (left panel) or 10 µM danoprevir (right panel) treatment. b, Colocalization of mCherry-NS3a with mitochondria-, Golgi-, or nuclear-localized DNCR1-EGFP under DMSO (left panel) or 10 µM danoprevir (right panel) treatment. Each panel in (a,b) is representative of the majority population of n≥18 NIH3T3 cells from one well. Quantification of colocalization of mCherry-NS3a with c, Golgi- or d, mitochondrially-localized DNCR1-EGFP after treatment with grazoprevir (10 µM), danoprevir (10 µM), asunaprevir (10 µM), or DMSO. The mean and standard deviation of the Pearson’s r of red/green pixel intensities is given for the number of cells stated in Supplementary Table 3 for each condition, along with the distributions for multiple NIH3T3 cells. See Supplementary Table 3 for sample sizes and P values.

Supplementary Figure 7 Additional PROCISiR combinations for two-location control of NS3.

a, Colocalization of GNCR1-BFP or DNCR2-EGFP with NS3a-mCherry-CAAX after treatment with danoprevir (5 µM), grazoprevir (5 µM), or DMSO. b, Colocalization of NS3a-mCherry with Tom20-BFP-ANR or DNCR2-EGFP-CAAX after treatment with danoprevir (5 µM), grazoprevir (5 µM), or DMSO. Images are representative of two separate wells and the number of cells given in Supplementary Table 3. c,d, The mean (marked by dot) and standard deviation (error bars) of the Pearson’s r of red/blue or red/green pixel intensities for the number of cells stated in Supplementary Table 3 is given for each condition in (a,b), along with the distributions of Pearson’s r.

Supplementary Figure 8 Titration of gene expression with Gal4/UAS and a two-gene dCas9 system.

a, Titration of mCherry expression from a UAS-minCMV promoter using Gal4-NS3a and DNCR2-VPR (left). Median mCherry values are shown in the middle panel, with the histograms for one replicate shown on the right to illustrate that the full population shifts to intermediate levels of gene expression. b, Expression of CXCR4 and GFP in cells expressing an MS2 scRNA targeting CXCR4, a PP7 scRNA targeting a GFP reporter, GNCR1-MCP, DNCR2-PCP, and NS3a-VPR after treatment with DMSO, danoprevir, or grazoprevir. Fold changes relative to DMSO are given for each 10 µM drug response for three biological replicates from one experiment. See Supplementary Table 3 for P values. c, CXCR4 immunofluorescence from titration of grazoprevir alone in the same system as (b). d, GFP fluorescence from titration of danoprevir alone in the same system as (b). (a,c,d) are fit to a one-site, specific binding Hill equation, and each point shows the mean and standard deviation of 3 biological replicates from one experiment, with background fluorescence levels from a DMSO-only condition subtracted. e,f, Raw median fluorescence values for experiment shown in Fig. 3e, f (mean of two replicates, not DMSO-subtracted).

Supplementary Figure 9 Modeling of NS3a:danoprevir and NS3a:grazoprevir occupancies.

The fraction of NS3a bound to danoprevir (orange, left axis) and the fraction of NS3a bound to grazoprevir (yellow, right axis) was calculated for a constant concentration of 100 nM danoprevir, with increasing concentrations of grazoprevir. The vertical gray lines mark the grazoprevir concentrations used for the experiments in Fig. 4, Supplementary Fig. 11, and Supplementary Fig. 12.

Supplementary Figure 10 Switchable repression, overexpression, and three-gene control.

Median immunofluorescence of CXCR4 (a,b) or CD95 (c,d) expression controlled by danoprevir-promoted recruitment of (a,c) DNCR2-VPR or (b,d) DNCR2-KRAB to NS3a-dCas9 in the absence or presence of guides targeting the CXCR4 (a,b) or CD95 (c,d) promoter region. Fold change (a,c) or inverse fold change (b,d) are given above each DMSO/danoprevir condition pair. e, Switching between repression and overexpression is achieved from endogenous promoters for CXCR4 and CD95 (f) using dCas9 with MCP-NS3a, GNCR1-VPR, and DNCR2-KRAB-MeCP2. Fold change or inverse fold change is shown for treatment with 100 nM grazoprevir or danoprevir, respectively. (a-f) The mean of the median immunofluorescence intensities are given in arbitrary units for data from 3 independent wells from one experiment. g, Expression of GFP, CD95, and CXCR4 using a MS2 scRNA targeting a GFP reporter, a PP7 scRNA targeting CD95, and a com scRNA targeting CXCR4 with MCP-ANR, PP7-DNCR2, and com-GNCR1, respectively. The mean of 3 independent wells from one experiment is given for each gene relative to untransfected cells. See Supplementary Table 3 for all P values.

Supplementary Figure 11 Membrane localization control in Hela and NIH3T3 cells.

a,b, Confocal image quantification of EGFP-DNCR2 (a) or BFP-GNCR1 (b) colocalization with mCherry-NS3a-CAAX for single-drug titrations in HeLa cells. Mean and standard deviation of the number of cells per condition given in Supplementary Table 3 from one well. Curves are fit to a one-site, total binding equation. c, Confocal image quantification of EGFP-DNCR2 (green) and BFP-GNCR1 (blue) colocalization with mCherry-NS3a-CAAX for drug combinations shown in NIH3T3 cells. The mean (marked by dot) and standard deviation (error bars) of the Pearson’s r of red/blue or red/green pixel intensities for the number of cells stated in Supplementary Table 3 is given for each condition along with the distributions of Pearson’s r. d, Representative images from two experiments of NIH3T3 cells transiently co-expressing EGFP-DNCR2-TIAM, BFP-GNCR1-LARG, and NS3a-CAAX treated with danoprevir (top) or grazoprevir (bottom) for the times indicated (drug was added at t=0). Lifeact-mCherry was also co-expressed to allow visualization of F-actin. Danoprevir treatment led to membrane ruffling (arrow) and grazoprevir treatment led to cell contraction (a red outline illustrating the cell boundary at -10 min is overlaid to illustrate cell contraction). e, Cell morphology parameters (area, perimeter, solidity, and circularity) of NIH3T3 cells transiently co-expressing EGFP-DNCR2-TIAM, BFP-GNCR1-LARG, NS3a-CAAX, and Lifeact-mCherry that were treated with the drug combinations shown in (C) for the times indicated (drug was added at t=0). Mean and s.e.m. for the number of per condition given in Supplementary Table 3 normalized to first frame, from one well.

Supplementary Figure 12 Proportional control of RhoA and Rac1 activation in HeLa cells.

a,c, Change in normalized perimeter (a) or circularity (c) (DMSO baseline subtracted) over time (drug addition at 0 min) in HeLa cells expressing NS3a-CAAX with either DNCR2-TIAM and GNCR1-LARG, DNCR2-TIAM alone, or GNCR1-LARG alone. Lines are colored according to the drug conditions shown on the x-axis of the plot in (b). b,d, Change in normalized perimeter (b) or circularity (d) (average last 10 min-first 10 min). (a-d) Mean and s.e.m. of the number cells per condition listed in Supplementary Table 3 from four independent wells. e-g, Kernel density estimates of distributions of morphology statistics at 60 min post-drug addition in Hela cells expressing NS3a-CAAX with either (e) DNCR2-TIAM and GNCR1-LARG, (f) DNCR2-TIAM alone, or (g) GNCR1-LARG alone. h, Kernel density estimates of the distributions of morphology statistics at the first frame (before drug addition) for the three HeLa cell lines.

Supplementary Figure 13 Yeast and human cell FACS gating strategies.

a, Yeast gating for all events (left), expression positive events used to report median binding fluorescence values (middle), and example sorting gates (right). b, HEK293T gating for all events (left), followed by single cell gating (middle), and gating for transfection-positive cells (BFP positive, right). Untransfected cells (red), NS3aH1-BFP-dCas9-transfected cells (blue).

Supplementary Figure 14 Yeast-displayed design variants binding to NS3a:drug complexes.

Binding of NS3a:drug complexes (danoprevir, grazoprevir, asunaprevir) to yeast-displayed a, DNCR1, b, DNCR2, c, GNCR1. Shown are 3 technical replicates fit with the Hill equation for complete curves.

Supplementary Figure 15 Full western blots for Supplementary Figure. 4b.

Full Western blots (top, anti-rabbit secondary with anti-pSer473-Akt and anti-GAPDH primaries; bottom, anti-mouse secondary with anti-Akt primary). Molecular weight markers are indicated on both blots.

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Supplementary Figs. 1–15, Notes, Tables 1–5 and Design Scripts.

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Foight, G.W., Wang, Z., Wei, C.T. et al. Multi-input chemical control of protein dimerization for programming graded cellular responses. Nat Biotechnol 37, 1209–1216 (2019).

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