Abstract
The design of modular protein logic for regulating protein function at the post-transcriptional level is a challenge for synthetic biology. Here we describe the design of 2-input AND, OR, NAND, NOR, XNOR, and NOT gates built from de novo designed proteins. These gates regulate the association of arbitrary protein units ranging from split enzymes to transcriptional machinery in vitro, in yeast and in primary human T cells, where they control the expression of the TIM3 gene related to T cell exhaustion. Designed binding interaction cooperativity, confirmed by native mass spectrometry, makes the gates largely insensitive to stoichiometric imbalances in the inputs, and the modularity of the approach enables ready extension to 3-input OR, AND, and disjunctive normal form gates. The modularity and cooperativity of the control elements, coupled with the ability to de novo design an essentially unlimited number of protein components, should enable design of sophisticated post-translational control logic over a wide range of biological functions.
Protein-protein interactions are ubiquitous in cellular decision making and controlling them will be increasingly important in synthetic biology (1–4). Although protein interactions are central to natural biological circuits, efforts to create new logic circuits have focused on control at the level of DNA (5, 6), transcription (7–18), or RNA (13, 19–22). Recently, protein-based circuits have been generated by rewiring native signaling pathways (23–28), bringing proteins together with coiled coils (29), or creating protease cascades (30, 31); these approaches rely on a small number of components which limits their modularity and scalability. The ability to de novo design protein-based logic gates modulating arbitrary protein-protein interactions could open the door to new protein-based control systems in and out of cells.
In principle, it should be possible to design a wide range of logic gates de novo using a set of heterodimeric molecules. For example, given hypothetical heterodimer pairs A:A’, B:B’, and C:C’, an AND gate modulating the association of A with C’ can be constructed by genetically fusing A’ and B, and B’ and C: association occurs only in the presence of both A’-B, and B’-C (here and below “:” denotes noncovalent interaction, and “-” a genetic fusion through flexible linkers). Several building block properties are desirable for constructing such associative logic gates. First, there should be many mutually orthogonal heterodimeric pairs, so that gate complexity is not limited by the number of individual elements. Second, the building blocks should be modular and similar in structure so that differences in building block shape and other properties do not have to be considered when constructing the gates. Third, single building blocks should be able to bind to multiple partners with different and tunable affinities, allowing inputs to perform negation operations by disrupting pre-existing lower affinity interactions. Fourth, the interactions should be cooperative so gate activation is not sensitive to stoichiometric imbalances in the inputs. In the above AND gate, for example, if the interactions are not cooperative, a large excess of A’-B will pull the equilibrium towards partially assembled complexes (A’-B with either A or B’-C but not both), which will limit gate activation.
Here, we explored the possibility of designing logic gates satisfying all four of the above criteria using de novo designed protein heterodimers with hydrogen bond network-mediated specificity (32). Sets of mutually orthogonal designed heterodimers (DHDs, hereafter referred to by numbers, e.g. 1 and 1’ form one cognate pair. Table S1.) with hydrogen bond network mediated specificity (see Fig. 1A inset for example) are available for logic gate construction, satisfying condition 1 (orthogonality). The heterodimeric interfaces all share the same four helix bundle topology (Fig. 1A), satisfying condition 2 (modularity). The shared interaction interface allows a limited amount of cross talk between pairs, leading to a hierarchy of binding affinities, satisfying condition 3 (multiple binding specificities). Inspired by cooperative systems in nature (33, 34), we sought to achieve condition 4 (cooperativity) by constructing the monomer fusions (A’-B and B’-C in the above example) in such a way that the interaction surfaces (with A and C’) are buried within the fusions. The free energy required to expose these buried interfaces would oppose gate activation, and we reasoned that the system could be tuned so that the sum of the binding energies of the two partners, but not either one alone, would be sufficient to overcome this barrier, ensuring cooperative gate activation. If condition 2 (modularity) holds, then a single scheme for ensuring cooperativity could in principle work for a wide range of gate configurations.
Figure 1. Cooperativity of CIPHR logic gates.
(A) Left: Backbone structure of A:A’ heterodimer building block, with its hydrogen bond network in inset. Bottom: Shorthand representations used throughout figures. (B) Thermodynamic cycle describing the induced dimerization system. (C) Simulation of the induced dimerization system under thermodynamic equilibrium. A and B’ monomers were held constant at 10 μM each while titrating in various initial amounts of the A’-B dimerizer proteins. If binding is not cooperative (small c), the final amount of trimeric complexes decreases when the dimerizer protein is in excess. (D) Equilibrium denaturation experiments monitored by CD for designs with 6- and 12- amino acid (AA) linkers. Circles represent experimental data, and lines are fits to the 3-state unimolecular unfolding model. (E) Experimental SAXS profile of 1’−2’ with a 6-residue linker (in black), fitted to the calculated profile of 1:1’ heterodimer. (F) An induced dimerization system using a 6-residue linker. (G) Native MS titration of 2 against 1’−2’ in the presence (red) or absence (blue) of 1. (H) Native MS titration of 1’−2’ against 1 and 2. Dimer 1 and 2 refer to partial dimeric complexes consisting of the dimerizer binding to either of the monomers. For comparison, the thermodynamic model result with c = 991,000 is shown in cyan. (I) Schematic of testing of the induced dimerization system in yeast, with in vivo results in (J). Pg, progesterone. (K) A two-input AND gate schematic, with native MS titration results in (L). Trimer 1 and 2 refer to partial trimeric complexes of the two dimerizer proteins binding to either one of the monomers. (M) A three-input AND gate, with native MS titration results in (N). Tetramer 1 and 2 refer to partial tetrameric complexes of the three dimerizer proteins binding to either one of the monomers. All error bars are reported as standard deviations of n=3 independent replicates.
To explore the design of cooperative building blocks, we focused on the simple system A + A’-B + B’ (we refer to this as induced dimerization below, A and B’ as the monomers, and A’-B as the dimerizer). If binding is not cooperative, the amount of the trimeric complex decreases when A’-B is in stoichiometric excess relative to A and B’: the formation of intermediate dimeric species of the dimerizer binding to either of the monomers competes with formation of trimeric complexes. On the contrary, if binding is cooperative such that no binding to either monomer occurs in the absence of the other, the amount of trimeric complex formed becomes insensitive to an excess of the dimerizer. A simple thermodynamic model of the effect of binding cooperativity on the stoichiometry dependence of such induced dimerization systems (Fig. 1B, supplemental materials modeling section) shows that as the binding cooperativity decreases, there is a corresponding decrease in the population of full trimeric complexes at high dimerizer concentrations (Fig. 1C).
We hypothesized that a folded four helix bundle like state of the A’-B dimerizer could oppose binding to either A or B’, as the relatively hydrophobic interacting surfaces would likely be sequestered within the folded structure (Fig. S1A). We tested different flexible linker lengths connecting A’ with B using heterodimers 1:1’ and 2:2’ as a model system. At all linker lengths tested (between 0 and 24 residues), constructs were folded and stable in circular dichroism (CD) guanidine hydrochloride (GdnHCl) denaturation experiments, with unfolding free energies greater than 13 kcal/mol (Fig. 1D, Fig. S2, Table S3). Although 1’−2’ dimerizer constructs with short linkers of 0 and 2 residues, or with a very long 24 residue linker could be purified as monomers (Fig. S1B), they were prone to aggregation, perhaps due to domain swapping. In contrast, designs with 6 and 12 residue linkers remained largely monomeric (Table S4). Small angle x-ray scattering (SAXS) experiments (35) indicate their hydrodynamic radii are close to those of folded four-helix bundle DHDs (Fig. 1E, Table S2). Linkers in this length range likely allow the two monomers (1’ and 2’) to fold back on each other such that the largely hydrophobic interaction surfaces are buried against each other; such a structure would have to partially unfold for 1’−2’ to interact with either 1 or 2. The magnitude of the unfolding energy (ΔGopen in Fig. 1B), determines the extent of cooperativity for the gate. We selected linker lengths of 6-, 10- or 12- residues for all of the following experiments.
We studied the cooperativity of the induced dimerizer system in vitro using native mass spectrometry (nMS, 36, 37) which can directly measure the populations of different oligomeric species in a sample (Tables S5–S8, for calibration curve see Fig. S3). We first measured the extent to which 1 activates the binding of 2 to 1’−2’ (Fig. 1F). 1, 2 and 1’−2’ were separately expressed in E.coli and purified. At 10 μM each of 2 and 1’−2’, the fraction of 2 in complex with 1’−2’ increased from 3% to 100% upon addition of 20 μM 1 (Fig. 1G); a fold increase comparable with naturally occurring allosteric systems (33). To assess the sensitivity of binding to stoichiometric imbalance, 10 μM 1 and 2 were titrated with increasing concentrations of 1’−2’ (Fig. 1H), and the species formed determined by nMS. The heterotrimeric 1:1’−2’:2 complex was observed over a wide range of 1’−2’ concentrations (Fig. 1H). Even in the presence of a 6 fold excess of 1’−2’, there was no decrease in the amount of 1:1’−2’:2 formed, and neither 1:1’−2’ nor 1’−2’:2 were detected (Fig. 1H). We define a cooperativity parameter c as the ratio of the affinities in the presence and absence of the other monomer, which in our model directly relates to the free energy of opening of the dimerizer (, see supplementary materials). The estimated c value from fits of the thermodynamic model to nMS data (Fig. 1H, cyan line) is 991,000 ± 21 (for reference, the c value of the naturally occurring N-Wasp system is 350, but system differences complicate quantitative comparisons). This value of c corresponds to ΔGopen of 8.2 kcal/mol, which is about half the measured unfolding free energy of 1’−2’ (Table S3), suggesting that binding may not require complete unfolding of the four helix bundle state of the dimerizer.
To investigate the cooperativity of the induced dimerizer system in living cells, we used a two-hybrid like assay in yeast. 11’ was fused to the DNA binding domain ZF43 (14), 7 to the transactivation domain VP16, and the dimerizer 11–7’ was placed under the control of a progesterone responsive element. Association of the DNA binding and activation domains results in transcription of red fluorescent protein (RFP) (Fig. 1I). Treating cells with increasing amount of progesterone resulted in up to a 4.5-fold increase in RFP signal, with only a small drop at saturating progesterone concentrations (Fig. 1J). Based on calibration curves, under these conditions 11–7’ is expected to be in greater than 5-fold molar excess over 11’ and 7 (Fig. S4), suggesting that 11–7’ binds cooperatively to 11’ and 7 in cells. Thus the cooperativity of the dimerizer system makes it robust to fluctuating component stoichiometries in cells.
With dimerizers displaying cooperative binding, we reasoned that the lack of dependence on stoichiometric excesses of one of the components should extend to more complex gates. Using nMS, we investigated the cooperativity of a 2-input AND gate constructed with the two dimerizers 1’−3’ and 3–2’ as inputs, and monomers 1 and 2 brought together by the two inputs (Fig. 1K). As the concentration of the 2 inputs increased, the amount of heterotetrameric complex plateaued at a stoichiometry of 2:1, and then largely remained constant with a small drop at molar ratio of 6:1. Only very small amounts of partial complexes (heterotrimers and heterodimers) were observed, further indicating high cooperativity (Fig. 1L). We constructed a 3-input AND gate from 1’−4’, 4–3’, and 3–2’, which together control the association of 1 and 2 (Fig. 1M). Similar to the 2-input AND gate, the amount of full, pentameric complexes only decreased slightly at greater than stoichiometric concentrations of inputs with no detectable competing tetrameric complexes (Fig. 1N).
We explored the modular combination of DHDs (Table S1) to generate a range of 2-input Cooperatively Inducible Protein HeterodimeR (CIPHR) logic gates. Monomers from individual DHDs were linked to effector proteins of interest via genetic fusion such that the inputs (linked heterodimer subunits) control colocalization or dissociation of the effector proteins. Taking advantage of previously measured all-by-all specificity matrices for the DHDs (32), we explored constructing gates from two interaction modalities: cognate binding between designed protein pairs, or competitive binding involving multispecific interactions (Fig. 2A).
Figure 2. CIPHR two input logic gates.
(A) CIPHR gates are built from DHDs (top) with monomers or covalently connected monomers as inputs (left); some gates utilize only the designed cognate interactions (left side of middle panel), while others take advantage of observed binding affinity hierarchies (right side of middle panel). (B-C) Two-input AND (B) and OR (C) CIPHR logic gates based on orthogonal DHD interactions. (D-G) NOT (D), NOR (E), XNOR (F), and NAND (G) CIPHR logic gates made from multispecific and competitive protein binding. For each gate, black dots represent individual Y2H growth measurement corrected over background growth, with their average values shown in green bars. * indicates no yeast growth over background. 0s and 1s in the middle and right blocks represent different input states and expected outputs, respectively.
We began by constructing AND and OR gates, reading out gate function using a yeast-two-hybrid (Y2H) setup similar to previously described yeast-four-hybrid systems (38, 39). To construct an AND gate, we fused 2 to the Gal4 activation domain (AD), and 1 to the Gal4 DNA binding domain (DBD). In this scheme, the colocalization of AD and DBD, and resulting transcriptional activation of the His3 gene, should require the expression of both input proteins (1’−5, 5’−2’). Indeed, growth in media lacking histidine required expression of both inputs (Fig. 2B). An OR gate was similarly constructed by linking the 1–6 fusion to the AD and 7’ to the DBD. Expression of either of the inputs 1’−7 or 6’−7 results in growth by driving association of AD with DBD (Fig. 2C).
We explored the construction of additional boolean logic gates by exploiting binding affinity hierarchies identified in all by all Y2H experiments (32). 8 alone forms a homodimer, but in the presence of 8’ it dissociates to form the 8:8’ heterodimer (Fig. S5A). We constructed a NOT gate by fusing 8 to both AD or DBD; the 8:8 homodimer supports yeast growth but in the presence of co-expressed 8’ input protein, the interaction is broken and growth is slowed (Fig. 2D). Based on the affinity hierarchy 9:9’ ≈ 10:10’ > 9:10’ (Fig. S5B), we constructed a NOR gate in which 9 was fused to the AD and 10’ to the DBD, with 9’ and 10 the two inputs. Either or both of the inputs outcompete the 9:10’ interaction and hinder yeast growth (Fig. 2E). Based on the affinity hierarchy 9’:1’ > 9:9’ ≈ 1:1’ > 9:1 (Fig. S5B), an XNOR gate was constructed by fusing 9 to AD, 1 to DBD, and using 9’ and 1’ as the two inputs: the presence of either outcompetes the 9:1 binding and blocks growth, but when both are expressed they instead interact with each other and growth is observed (Fig. 2F). Similarly, a NAND gate was designed based on the interaction hierarchy 1’:10’ > 1:1’ ≈ 10:10’ > 1:10 (Fig. S5B). Neither 1 nor 10 alone can outcompete the 1’:10’ interaction and hence growth occurs, but when both are expressed, the free energy of formation of both 1:1’ and 10:10’ outweighs that of 1’:10’ and growth is blocked (Fig. 2G).
We next investigated 3-input CIPHR logic gates. We first used native MS to characterize a 3-input AND gate (Fig. 1M) in which monomers 1 and 2 are brought into proximity by the three inputs 1’−4’, 4–3’, and 3–2’. We experimentally tested all eight possible input combinations (Fig. 3A) with both 1 and 2 present, quantifying all complexes using nMS. Consistent with 3-input AND gate function, 1 and 2 only showed significant co-assembly when all three inputs are present (Fig. 3B).
Figure 3. Three-input CIPHR logic gates.
(A) Schematic of a three-input AND gate. (B) Native MS results indicate proper activation of the 3-input AND gate only in the presence of all three inputs. (C) Schematic of a three-input OR gate. (D) Y2H results confirmed activation of the 3-input OR gate with either of the inputs. (E) Schematic of a DNF gate. (F) Y2H results confirmed proper activation of the gate. For each gate, black dots represent individual measurements with their average values shown in green bars. For Y2H-based measurements (D,F), the growth measurements are corrected over background growth.
To test 3-input CIPHR gate function in cells, we designed two additional gates using the same 4 pairs of DHDs and tested them by Y2H. To make a 3-input OR gate, 1’−6–7 was fused to AD, and 11’ to DBD. Any one of the 3 inputs (11–1, 11–6’, 11–7’) connects the AD to the DBD through 1’, 6 or 7 respectively (Fig. 3C). Y2H results confirmed the expected behavior of this logic gate in cells: any of the input proteins induced cell growth (Fig. 3D). We constructed a CIPHR disjunctive normal form (DNF, [A AND B] OR C) gate by fusing 1’−6 to AD, 11’ to DBD with inputs 11–7’, 7–1, or 11–6’ (Fig. 3E). In Y2H experiments, the DNF gate functioned as designed, with low yeast growth levels when no input or only one of the 11–7’ and 7–1 input proteins are present, and high yeast growth levels otherwise (Fig. 3F).
To test the transferability of CIPHR logic gates, we explored the ability of CIPHR logic gates to reconstitute split enzyme activity by controlling the association of the two halves of the NanoBiT split luciferase system (40–42). Monomers from 1:1’, 2:2’, 4:4’ and 9:9’ (Fig. 4A) were fused in pairs to the two split domains (smBiT and lgBiT), and produced by in vitro transcription and translation, which facilitated a rapid testing cycle enabling the full 4×4 interaction affinity hierarchy to be determined by monitoring luciferase activity following mixing (Fig. S6A). Based on this hierarchy, we constructed and experimentally verified an induced dimerization circuit with 4-smBiT, 1-lgBiT, and 1’−4’ as the input (Fig. 4B, Fig. S6C–D); characterization of the time dependence of the response revealed a 7-fold increase in signal 5 minutes after adding inputs (Fig. S6D). We also constructed an AND gate with 4-smBiT, 1-lgBiT, and 1’−2 and 2’−4’ as the inputs (Fig. 4C) and a NOR gate with 1’-smBiT, 2’-lgBiT, and 1 and 2 as the inputs (Fig. 4D), both of which had the designed dependence of gate function (i.e., luciferase activity) on the inputs. We investigated the response of the NOR gate to varying concentrations of the inputs against the NanoBiT components held at 5nM, and found a sharp drop in signal around 5 nM for both inputs consistent with NOR logic (Fig. 4E, Fig. S6E).
Figure 4. Transferability of CIPHR logic gates.
(A) Four pairs of DHDs were modularly combined to construct CIPHR logic gates that can be used to control different functions: (B-E) catalytic activity of split luciferase, and (F-G) gene expression in primary human T cells. (B) Induced dimerization system, (C) AND gate, and (D) NOR gate coupled to NanoBiT split luciferase system, tested by in vitro translation and monitoring luminescence. (E) In vitro titration of the two inputs of the NOR gate in D while keeping 1’-smBiT and 2’-lgBiT fixed at 5 nM. (F) NOT gate and (G) OR gate using a split TALE-KRAB repression system to control expression of TIM3 proteins in primary human T cells, tested by flow cytometry.
Engineered T cell therapies are promising therapeutic modalities (43–45), but their efficacy for treating solid tumors is limited at least in part by T cell exhaustion (46, 47). Immune checkpoint genes including TIM3 are believed to play critical roles in modulating T cell exhaustion (48–50). To put the transcription of such proteins under the control of the CIPHR logic gates, we took advantage of potent and selective transcriptional repressors of immune checkpoint genes in primary T cells that combine sequence-specific transcription activator-like effector (TALE) DNA binding domains with the Krüppel-associated box (KRAB) repressor domain (51). Repression activity is preserved in split systems pairing a DNA recognition domain fused to a DHD monomer with a repressor domain fused to the complementary DHD monomer (51). We reasoned that this system could be exploited to engineer programmable therapeutic devices by making the joining of the DNA recognition and transcriptional repression functionalities dependent on CIPHR gates. Use of a repressive domain effectively reverses the logic of CIPHR gates when expression level of the target gene is measured as the output.
To test the feasibility of this concept, we used a TALE-KRAB fusion engineered to repress the immune checkpoint gene TIM3 (51). We designed a NOT gate, with 1 fused to the TALE DNA recognition domain, 9’ fused to KRAB, and the 1’−9 dimerizer protein as the input (see Fig. S7A for T cell DHD specificity matrix). In this scheme, 1’−9 brings KRAB to the promoter region bound by the TALE, therefore triggering repression of TIM3 (Fig. 4F). Taking advantage of the interaction between 9 and 1’, we built an OR gate with 9-TALE and 1’-KRAB fusions; TIM3 is repressed in the absence of inputs, but upon addition of either 9’ or 1, the weaker 9:1’ interaction is outcompeted in favor of the stronger 9:9’ and 1:1’ interactions, restoring TIM3 expression (Fig. 4G). These results suggest that the combination of CIPHR and TALE-KRAB systems could be directly applied to add signal processing capabilities to adoptive T cell therapy.
The systematic design of logic gates described in this paper takes advantage of the strengths of de novo protein design. Since the building block heterodimers are designed de novo, many more components can be generated for gate construction with nearly identical overall topology than are available by repurposing biological motifs. The encoding of specificity using designed hydrogen bond networks enables a wide range of binding affinities between monomers with similar structures, which in turn allows the construction of more complex gates based on competitive binding. From the protein biophysics perspective, our results highlight the strong synergy between de novo design of protein complexes and native MS, and more generally, the ability of de novo protein design to generate complex cooperative assemblies. For example, detecting and quantifying the 33-fold trans-activation of binding in Fig. 1G depended critically on the ability to resolve all species formed in solution by native MS. Analysis of the three input logic gates in Fig. 3B required distinguishing the designed heteropentameric assemblies -- composed from five distinct protein chains -- from the very large number of alternative possible hetero-tetrameric, trimeric and dimeric complexes. The ability to generate highly cooperative and well defined assemblies composed of five distinct polypeptide chains demonstrates that de novo protein design is starting to approach the complexity of naturally occurring protein assemblies, which are responsible for much of biological function.
Unlike nucleic acid based logic gates, CIPHR gates can be directly coupled to arbitrary protein actuation domains, offering greater diversity in the types of functional outputs. We illustrate here the coupling to transcriptional activation and repression, and split enzyme reconstitution; in principle any function that can be modulated by protein-protein association can be put under the control of the CIPHR gates. Since the designed components are hyperstable proteins and no additional cellular machinery is required, the gates should function in a wide range of conditions inside and out of cells (here we demonstrate function with purified components, in cell free extracts, yeast cells, and T-cells). The small size of DHDs and hence their genetic payload make them attractive for mammalian cell engineering. The sophistication of the circuits could be further increased by proteolytic activation as in elegant recent protease based protein circuits (30, 31); our purely protein interaction based circuits have advantages in bioorthogonality, demonstrated scalability to three inputs, composability (the output, like the input and the computing machinery, are interactions between building blocks with common design features), and extensibility as an essentially unlimited repertoire of heterodimeric building blocks can be created using de novo design.
Supplementary Material
ACKNOWLEDGEMENTS
We thank A. Ng, T. Nguyen, D. Younger, M. Xie, and B. Groves for assistance with yeast experiments; C. Chow for assistance with protein purification; W. Lim for discussions on protein binding cooperativity; M. Elowitz, R. Schulman and N. Woodall for feedback on the manuscript; S. Bermeo, M. Lajoie and A. Ljubetič for useful discussions; S. Pennington for making media for Y2H assays.
Funding: This work was supported by the Howard Hughes Medical Institute (D.B.), the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program (D.B. and Z.C.), IPD-WA State funding Y5 / 07-5568 (D.B.), NIH BTRR Yeast Resource Grant Y8-12 / 61-3650 (Z.C. and R.D.K.), Bruce and Jeannie Nordstrom / Patty and Jimmy Barrier Gift for the Institute for Protein Design (Z.C. and R.D.K.), Spark ABCA4 / 63-3819 (Z.C.), NIH P41 GM103533 (R.D.K.), Open Philanthropy (D.B. and B.I.M.W.), EMBO / 80-7223 (B.I.M.W.), Burroughs Wellcome Fund Career Award at the Scientific Interface (S.E.B.), the Army Research Office W911NF-18-1-0200 (M.C.J.), the Air Force Research Laboratory Center of Excellence Grant FA8650-15-2-5518 (M.C.J), the David and Lucile Packard Foundation (M.C.J.) and the Camille Dreyfus Teacher-Scholar Program (M.C.J.). A.H. was supported by the Department of Defense (DOD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program. T cell engineering work was supported by a charitable contribution from GlaxoSmithKline to the Altius Institute for Biomedical Sciences (J.R.P., H.L., M.W., C.C., J.A.S.) and NHLBI Grant 7R33HL120752 (J.A.S). V.H.W. was supported by National Institutes of Health Grant P41 GM128577 and Ohio Eminent Scholar funds. H.E.-S. was supported by the Defense Advanced Research Projects Agency, Contract No. HR0011-16-2-0045, and is a Chan-Zuckerberg investigator. SAXS data were collected at the Advanced Light Source (ALS) at LBNL, supported by the following grants from NIH (P30 GM124169-01, ALS-ENABLE P30 GM124169, and S10OD018483), NCI SBDR (CA92584) and DOE-BER IDAT (DE-AC02-05CH11231).
Footnotes
Competing interests: Z.C, S.E.B. and D.B. are co-inventors on the U.S. patent application PCT/US19/59654 that incorporates discoveries described in this manuscript. D.B. is a cofounder of and holds equity in Lyell Immunopharma and Sana Biotechnology. J.P. and S.E.B. hold equity in Lyell Immunopharma.
Data and materials availability: Raw data from native MS experiments has been deposited to http://files.ipd.uw.edu/pub/de_novo_logic_2019/190522_native_ms_raw.zip. Plasmids and computer code used during this study are available upon request from the corresponding author.
REFERENCES AND NOTES
- 1.Nussinov R, How do dynamic cellular signals travel long distances? Mol. Biosyst 8, 22–26 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Reinke AW, Baek J, Ashenberg O, Keating AE, Networks of bZIP protein-protein interactions diversified over a billion years of evolution. Science. 340, 730–734 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Antebi YE, Linton JM, Klumpe H, Bintu B, Gong M, Su C, McCardell R, Elowitz MB, Combinatorial Signal Perception in the BMP Pathway. Cell. 170, 1184–1196.e24 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Harris BZ, Lim WA, Mechanism and role of PDZ domains in signaling complex assembly. J. Cell Sci 114, 3219–3231 (2001). [DOI] [PubMed] [Google Scholar]
- 5.Seelig G, Soloveichik D, Zhang DY, Winfree E, Enzyme-free nucleic acid logic circuits. Science. 314, 1585–1588 (2006). [DOI] [PubMed] [Google Scholar]
- 6.Qian L, Winfree E, Scaling up digital circuit computation with DNA strand displacement cascades. Science. 332, 1196–1201 (2011). [DOI] [PubMed] [Google Scholar]
- 7.Elowitz MB, Leibler S, A synthetic oscillatory network of transcriptional regulators. Nature. 403, 335–338 (2000). [DOI] [PubMed] [Google Scholar]
- 8.Gardner TS, Cantor CR, Collins JJ, Construction of a genetic toggle switch in Escherichia coli. Nature. 403, 339–342 (2000). [DOI] [PubMed] [Google Scholar]
- 9.Tamsir A, Tabor JJ, Voigt CA, Robust multicellular computing using genetically encoded NOR gates and chemical “wires.” Nature. 469, 212–215 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Siuti P, Yazbek J, Lu TK, Synthetic circuits integrating logic and memory in living cells. Nat. Biotechnol 31, 448–452 (2013). [DOI] [PubMed] [Google Scholar]
- 11.Bonnet J, Yin P, Ortiz ME, Subsoontorn P, Endy D, Amplifying genetic logic gates. Science. 340, 599–603 (2013). [DOI] [PubMed] [Google Scholar]
- 12.Weinberg BH, Pham NTH, Caraballo LD, Lozanoski T, Engel A, Bhatia S, Wong WW, Large-scale design of robust genetic circuits with multiple inputs and outputs for mammalian cells. Nat. Biotechnol 35, 453–462 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Ausländer S, Ausländer D, Müller M, Wieland M, Fussenegger M, Programmable single-cell mammalian biocomputers. Nature. 487, 123–127 (2012). [DOI] [PubMed] [Google Scholar]
- 14.Khalil AS, Lu TK, Bashor CJ, Ramirez CL, Pyenson NC, Joung JK, Collins JJ, A synthetic biology framework for programming eukaryotic transcription functions. Cell. 150, 647–658 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Roquet N, Soleimany AP, Ferris AC, Aaronson S, Lu TK, Synthetic recombinase-based state machines in living cells. Science. 353, aad8559 (2016). [DOI] [PubMed] [Google Scholar]
- 16.Andrews LB, Nielsen AAK, Voigt CA, Cellular checkpoint control using programmable sequential logic. Science. 361, eaap8987 (2018). [DOI] [PubMed] [Google Scholar]
- 17.Angelici B, Mailand E, Haefliger B, Benenson Y, Synthetic Biology Platform for Sensing and Integrating Endogenous Transcriptional Inputs in Mammalian Cells. Cell Rep. 16, 2525–2537 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Lohmueller JJ, Armel TZ, Silver PA, A tunable zinc finger-based framework for Boolean logic computation in mammalian cells. Nucleic Acids Res. 40, 5180–5187 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Green AA, Silver PA, Collins JJ, Yin P, Toehold switches: de-novo-designed regulators of gene expression. Cell. 159, 925–939 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Green AA, Kim J, Ma D, Silver PA, Collins JJ, Yin P, Complex cellular logic computation using ribocomputing devices. Nature. 548, 117–121 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Rinaudo K, Bleris L, Maddamsetti R, Subramanian S, Weiss R, Benenson Y, A universal RNAi-based logic evaluator that operates in mammalian cells. Nat. Biotechnol 25, 795–801 (2007). [DOI] [PubMed] [Google Scholar]
- 22.Wroblewska L, Kitada T, Endo K, Siciliano V, Stillo B, Saito H, Weiss R, Mammalian synthetic circuits with RNA binding proteins for RNA-only delivery. Nat. Biotechnol 33, 839–841 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Park S-H, Zarrinpar A, Lim WA, Rewiring MAP kinase pathways using alternative scaffold assembly mechanisms. Science. 299, 1061–1064 (2003). [DOI] [PubMed] [Google Scholar]
- 24.Howard PL, Chia MC, Del Rizzo S, Liu F-F, Pawson T, Redirecting tyrosine kinase signaling to an apoptotic caspase pathway through chimeric adaptor proteins. Proc. Natl. Acad. Sci. U. S. A 100, 11267–11272 (2003). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Groves B, Khakhar A, Nadel CM, Gardner RG, Seelig G, Rewiring MAP kinases in Saccharomyces cerevisiae to regulate novel targets through ubiquitination. Elife. 5 (2016), doi: 10.7554/eLife.15200. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Morsut L, Roybal KT, Xiong X, Gordley RM, Coyle SM, Thomson M, Lim WA, Engineering Customized Cell Sensing and Response Behaviors Using Synthetic Notch Receptors. Cell. 164, 780–791 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Dueber JE, Yeh BJ, Chak K, Lim WA, Reprogramming Control of an Allosteric Signaling Switch Through Modular Recombination. Science. 301, 1904–1908 (2003). [DOI] [PubMed] [Google Scholar]
- 28.Dueber JE, Mirsky EA, Lim WA, Engineering synthetic signaling proteins with ultrasensitive input/output control. Nat. Biotechnol 25, 660–662 (2007). [DOI] [PubMed] [Google Scholar]
- 29.Smith AJ, Thomas F, Shoemark D, Woolfson DN, Savery NJ, Guiding Biomolecular Interactions in Cells Using de Novo Protein-Protein Interfaces. ACS Synth. Biol 8, 1284–1293 (2019). [DOI] [PubMed] [Google Scholar]
- 30.Gao XJ, Chong LS, Kim MS, Elowitz MB, Programmable protein circuits in living cells. Science. 361,1252–1258 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Fink T, Lonzarić J, Praznik A, Plaper T, Merljak E, Leben K, Jerala N, Lebar T, Strmšek Ž, Lapenta F, Benčina M, Jerala R, Design of fast proteolysis-based signaling and logic circuits in mammalian cells. Nat. Chem. Biol 15, 115–122 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Chen Z, Boyken SE, Jia M, Busch F, Flores-Solis D, Bick MJ, Lu P, VanAernum ZL, Sahasrabuddhe A, Langan RA, Bermeo S, Brunette TJ, Mulligan VK, Carter LP, DiMaio F, Sgourakis NG, Wysocki VH, Baker D, Programmable design of orthogonal protein heterodimers. Nature. 565, 106–111 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Prehoda KE, Scott JA, Mullins RD, Lim WA, Integration of multiple signals through cooperative regulation of the N-WASP-Arp2/3 complex. Science. 290, 801–806 (2000). [DOI] [PubMed] [Google Scholar]
- 34.Yu B, Martins IRS, Li P, Amarasinghe GK, Umetani J, Fernandez-Zapico ME, Billadeau DD, Machius M, Tomchick DR, Rosen MK, Structural and energetic mechanisms of cooperative autoinhibition and activation of Vav1. Cell. 140, 246–256 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Dyer KN, Hammel M, Rambo RP, Tsutakawa SE, Rodic I, Classen S, Tainer JA, Hura GL, High-throughput SAXS for the characterization of biomolecules in solution: a practical approach. Methods Mol. Biol 1091, 245–258 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Ruotolo BT, Robinson CV, Aspects of native proteins are retained in vacuum. Curr. Opin. Chem. Biol 10, 402–408 (2006). [DOI] [PubMed] [Google Scholar]
- 37.VanAernum ZL, Busch F, Jones BJ, Jia M, Chen Z, Boyken SE, Sahasrabuddhe A, Baker D, Wysocki VH, Rapid online buffer exchange for screening of proteins, protein complexes and cell lysates by native mass spectrometry. Nat. Protoc (2020), doi: 10.1038/s41596-019-0281-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Pause A, Peterson B, Schaffar G, Stearman R, Klausner RD, Studying interactions of four proteins in the yeast two-hybrid system: structural resemblance of the pVHL/elongin BC/hCUL-2 complex with the ubiquitin ligase complex SKP1/cullin/F-box protein. Proc. Natl. Acad. Sci. U. S. A 96, 9533–9538 (1999). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Sandrock B, Egly JM, A yeast four-hybrid system identifies Cdk-activating kinase as a regulator of the XPD helicase, a subunit of transcription factor IIH. J. Biol. Chem 276, 35328–35333 (2001). [DOI] [PubMed] [Google Scholar]
- 40.Dixon AS, Schwinn MK, Hall MP, Zimmerman K, Otto P, Lubben TH, Butler BL, Binkowski BF, Machleidt T, Kirkland TA, Wood MG, Eggers CT, Encell LP, Wood KV, NanoLuc Complementation Reporter Optimized for Accurate Measurement of Protein Interactions in Cells. ACS Chem. Biol 11, 400–408 (2016). [DOI] [PubMed] [Google Scholar]
- 41.Kwon Y-C, Jewett MC, High-throughput preparation methods of crude extract for robust cell-free protein synthesis. Sci. Rep 5, 8663 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Porter JR, Stains CI, Jester BW, Ghosh I, A general and rapid cell-free approach for the interrogation of protein-protein, protein-DNA, and protein-RNA interactions and their antagonists utilizing split-protein reporters. J. Am. Chem. Soc 130, 6488–6497 (2008). [DOI] [PubMed] [Google Scholar]
- 43.Maude SL, Laetsch TW, Buechner J, Rives S, Boyer M, Bittencourt H, Bader P, Verneris MR, Stefanski HE, Myers GD, Qayed M, De Moerloose B, Hiramatsu H, Schlis K, Davis KL, Martin PL, Nemecek ER, Yanik GA, Peters C, Baruchel A, Boissel N, Mechinaud F, Balduzzi A, Krueger J, June CH, Levine BL, Wood P, Taran T, Leung M, Mueller KT, Zhang Y, Sen K, Lebwohl D, Pulsipher MA, Grupp SA, Tisagenlecleucel in Children and Young Adults with B-Cell Lymphoblastic Leukemia. N. Engl. J. Med 378, 439–448 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Neelapu SS, Locke FL, Bartlett NL, Lekakis LJ, Miklos DB, Jacobson CA, Braunschweig I, Oluwole OO, Siddiqi T, Lin Y, Timmerman JM, Stiff PJ, Friedberg JW, Flinn IW, Goy A, Hill BT, Smith MR, Deol A, Farooq U, McSweeney P, Munoz J, Avivi I, Castro JE, Westin JR, Chavez JC, Ghobadi A, Komanduri KV, Levy R, Jacobsen ED, Witzig TE, Reagan P, Bot A, Rossi J, Navale L, Jiang Y, Aycock J, Elias M, Chang D, Wiezorek J, Go WY, Axicabtagene Ciloleucel CAR T-Cell Therapy in Refractory Large B-Cell Lymphoma. N. Engl. J. Med 377, 2531–2544 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Fraietta JA, Lacey SF, Orlando EJ, Pruteanu-Malinici I, Gohil M, Lundh S, Boesteanu AC, Wang Y, O’Connor RS, Hwang W-T, Pequignot E, Ambrose DE, Zhang C, Wilcox N, Bedoya F, Dorfmeier C, Chen F, Tian L, Parakandi H, Gupta M, Young RM, Johnson FB, Kulikovskaya I, Liu L, Xu J, Kassim SH, Davis MM, Levine BL, Frey NV, Siegel DL, Huang AC, Wherry EJ, Bitter H, Brogdon JL, Porter DL, June CH, Melenhorst JJ, Determinants of response and resistance to CD19 chimeric antigen receptor (CAR) T cell therapy of chronic lymphocytic leukemia. Nat. Med 24, 563–571 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.June CH, O’Connor RS, Kawalekar OU, Ghassemi S, Milone MC, CAR T cell immunotherapy for human cancer. Science. 359, 1361–1365 (2018). [DOI] [PubMed] [Google Scholar]
- 47.Long AH, Haso WM, Shern JF, Wanhainen KM, Murgai M, Ingaramo M, Smith JP, Walker AJ, Kohler ME, Venkateshwara VR, Kaplan RN, Patterson GH, Fry TJ, Orentas RJ, Mackall CL, 4–1BB costimulation ameliorates T cell exhaustion induced by tonic signaling of chimeric antigen receptors. Nat. Med 21, 581–590 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Wherry EJ, Kurachi M, Molecular and cellular insights into T cell exhaustion. Nat. Rev. Immunol 15, 486–499 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.John Wherry E, Ha SJ, Kaech SM, Nicholas Haining W, Sarkar S, Kalia V, Subramaniam S, Blattman J, Barber DL, Ahmed R, Molecular Signature of CD8+ T Cell Exhaustion during Chronic Viral Infection. Immunity. 27 (2007), doi: 10.1016/j.immuni.2007.11.006. [DOI] [PubMed] [Google Scholar]
- 50.Pauken KE, Sammons MA, Odorizzi PM, Manne S, Godec J, Khan O, Drake AM, Chen Z, Sen DR, Kurachi M, Barnitz RA, Bartman C, Bengsch B, Huang AC, Schenkel JM, Vahedi G, Haining WN, Berger SL, Wherry EJ, Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science. 354, 1160–1165 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Wilken MS, Ciarlo C, Pearl J, Bloom J, Schanzer E, Liao H, Boyken SE, Van Biber B, Queitsch K, Heberlein G, Federation Alexander, Acosta R, Vong S, Otterman E, Dunn D, Wang H, Zrazhevskey P, Nandakumar V, Bates D, Sandstrom R, Chen Z, Urnov FD, Baker D, Funnell A, Green S, Stamatoyannopoulos JA, Promoter keyholes enable specific and persistent multi-gene expression programs in primary T cells without genome modification. bioRxiv (2020), p. 2020.02.19.956730. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.