Staple Subunit: GCN4

GCN4 is a yeast transcription factor belonging to the bZip family of DNA-binding proteins. We used GCN4 to study DNA-binding kinetics in our "Mini staples" that bring two DNA target sites into proximity by binding them simultaneously.

Figure 1: How our part collection can be used to engineer new staples

While synthetic biology has in the past focused on engineering the genomic sequence of organisms, the 3D spatial organization of DNA is well-known to be an important layer of information encoding in particular in eukaryotes, playing a crucial role in gene regulation and hence cell fate, disease development, evolution, and more. However, tools to precisely manipulate and control the genomic spatial architecture are limited, hampering the exploration of 3D genome engineering in synthetic biology. We - the iGEM Team Heidelberg 2024 - have developed PICasSO, a powerful molecular toolbox for rationally engineering genome 3D architectures in living cells, based on various DNA-binding proteins.

The PICasSO part collection offers a comprehensive, modular platform for precise manipulation and re-programming of DNA-DNA interactions using engineered "protein staples" in living cells. This enables researchers to recreate naturally occurring alterations of 3D genomic interactions, such as enhancer hijacking in cancer, or to design entirely new spatial architectures for artificial gene regulation and cell function control. Specifically, the fusion of two DNA binding proteins enables to artificially bring otherwise distant genomic loci into spatial proximity. To unlock the system's full potential, we introduce versatile chimeric CRISPR/Cas complexes, connected either at the protein or - in the case of CRISPR/Cas-based DNA binding moieties - the guide RNA level. These complexes are referred to as protein- or Cas staples, respectively. Beyond its versatility with regard to the staple constructs themselves, PICasSO includes robust assay systems to support the engineering, optimization, and testing of new staples in vitro and in vivo. Notably, the PICasSO toolbox was developed in a design-build-test-learn engineering cycle closely intertwining wet lab experiments and computational modeling and iterated several times, yielding a collection of well-functioning and -characterized parts.

At its heart, the PICasSO part collection consists of three categories.
(i) Our DNA-binding proteins include our finalized Cas staple experimentally validated using an artificial "enhancer hijacking" system as well as "half staples" that can be combined by scientists to compose entirely new Cas staples in the future. We also include our Simple staples comprised of particularly small, simple and robust DNA binding domains well-known to the synthetic biology community, which serve as controls for successful stapling and can be further engineered to create alternative, simpler, and more compact staples.
(ii) As functional elements, we list additional parts that enhance and expand the functionality of our Cas and Basic staples. These consist of staples dependent on cleavable peptide linkers targeted by cancer-specific proteases or inteins that allow condition-specific, dynamic stapling in vivo. We also include several engineered parts that enable the efficient delivery of PICasSO's constructs into target cells, including mammalian cells, with our new interkingdom conjugation system.
(iii) As the final category of our collection, we provide parts that underlie our custom readout systems. These include components of our established FRET-based proximity assay system, enabling users to confirm accurate stapling. Additionally, we offer a complementary, application-oriented testing system based on a luciferase reporter, which allows for straightforward experimental assessment of functional enhancer hijacking events in mammalian cells.

The following table gives a comprehensive overview of all parts in our PICasSO toolbox. The highlighted parts showed exceptional performance as described on our iGEM wiki and can serve as a reference. The other parts in the collection are versatile building blocks designed to provide future iGEMers with the flexibility to engineer their own custom Cas staples, enabling further optimization and innovation in the new field of 3D genome engineering.

Our part collection includes:

1. Sequence overview

2. Usage and Biology

GCN4 is a yeast transcription factor from the bZip family of DNA-binding proteins, first discovered by McKnight and co-workers in 1988. The bZip motif features a coiled-coil leucine zipper dimerization domain paired with a highly charged basic region that binds to DNA. GCN4 binds specifically to the cyclic AMP response element (CRE) DNA sequence (5' ATGACGTCAT 3') in the promoter regions of target genes, primarily through its basic residues at the N-terminus.

In our project we fused GCN4 to rGCN4 (BBa_K5237008) to create a 150 amino acid long "Mini staple" that can bring two DNA target sites into close proximity.

The DNA-binding properties of GCN4 were tested using an electrophoretic mobility shift assay (EMSA) to quantify binding affinity and calculate kinetics. EMSA is a widely adopted method to study DNA-protein interactions. It works on the principle that nucleic acids bound to proteins exhibit reduced electrophoretic mobility compared to unbound nucleic acids (Hellman & Fried, 2007). EMSA can be employed both qualitatively, to assess DNA-binding capabilities, and quantitatively, to determine critical parameters such as binding stoichiometry and the apparent dissociation constant (K_d) (Fried, 1989).

3. Assembly and Part Evolution

The GCN4 amino acid sequence was taken from literature (Hollenbeck et al. 2001) and codon optimized for E. coli. A FLAG-tag (DYKDDDDK) was added to the N-terminus for protein purification. The FLAG-tag can be cleaved off using an enterokinase, if necessary. The FLAG-GCN4 sequence was cloned into a T7 expression vector and expressed using E. coli BL21 (DE3) cells.

4. Results

4.1 Protein Expression and Purification

The FLAG-GCN4 protein could be readily expressed in E. coli. The protein was purified using an anti-FLAG resin. Fractions taken during purification were analyzed by SDS-PAGE and the protein concentration of the eluted protein determined with a lowry protein assay. A yield of 1.18 mg/mL was obtained, corresponding to 153 µM of monomeric FLAG-GCN4.

Figure 2: SDS-PAGE Analysis of FLAG-GCN4 Purification Fractions analysed for each protein are the raw lysate, flow through and eluate. Depicted is GCN4 (this part), rGCN4 (BBa_K5237008), and bGCN4 (BBa_K5237009). Protein size is indicated next to construct name and purified band with protein of interest highlighted by a red box.

4.2 Electrophoretic Mobility Shift Assay

Figure 3: Overview Image of Electrophoretic Mobility Shift Assay (EMSA)

The Electrophoretic mobility shift assay (EMSA) is a widely adopted method used to study DNA-protein interactions. EMSA functions on the basis that nucleic acids bound to proteins have reduced electrophoretic mobility, compared to their unbound counterpart. (Hellman & Fried, 2007). Mobility-shift assays can both be used to qualitatively assess DNA binding capabilities or quantitatively to determine binding stoichiometry and kinetics such as the apparent dissociation constant (K_d) (Fried, 1989).

To analyze the binding DNA affinity an EMSA was performed, in which GCN4 was incubated in binding buffer with a 20 bp DNA probe containing the CRE GCN4 binding sequence (5' ATGACGTCAT 3') until equilibration. Subsequently the formed protein-DNA complexes were loaded on a native PAGE. Afterwards the DNA bands were stained with SYBR-safe.
To further analyze DNA binding, quantitative shift assays were performed for GCN4 and rGCN4 (BBa_K5237008). 0.5 µM DNA were incubated with varying concentrations of protein until equilibration. After electrophoresis, bands were stained with SYBR-Safe and quantified based on pixel intensity. The obtained values were fitted to equation 1, describing formation of a 2:1 protein-DNA complex:

Θ_app = Θ_min + (Θ_max - Θ_min) × (K_a² [L]_tot²) / (1 + K_a² [L]_tot²) Equation 1

Here [L]_tot describes the total protein monomer concentration, K_a corresponds to the apparent monomeric equilibration constant. The Θ_min/max values are the experimentally determined site saturation values (For this experiment 0 and 1 were chosen for min and max respectively).

Figure 4: Quantitative Assessment of Binding Affinity for GCN4 and rGCN4. Proteins of different concentrations were incubated with 0.5 µM DNA until equilibrium and fraction bound analyzed, after gel electrophoresis, by dividing pixel intensity of bound fraction with pixel intensity of bound and unbound fraction. At least three separate measurements were conducted for each data point. Values are presented as mean +/- SD.

GCN4 binds to its optimal DNA binding motif with an apparent dissociation constant K_D of (0.293 ± 0.033) × 10^-6 M, which is almost identical to the rGCN4 dissociation constant to its target sequence (INVii) K_D of (0.298 ± 0.030) × 10^-6 M. Comparing them to literature values, our dissociation constants are approximately a factor 10 higher then those described in literature ((9±6) × 10^-8 M for GCN4 and (2.9±0.8) × 10^-8 M for rGCN4) (Hollenbeck et al., 2001). The differences could be due to the lower sensitivity of SYBR-Safe staining compared to radio-labeled oligos. Most likely, the protein concentration was miscalculated due to the presence of additional (lower intensity) bands in the SDS-PAGE analysis, indicating co-purification of small amounts of unspecific proteins.

The FLAG-tag fusion to the N-terminus of proteins could potentially decrease binding affinity, likely due to steric hindrance affecting the interaction with DNA. Interestingly, the differences in binding affinity between GCN4 and rGCN4 appear negligible. Since GCN4 binds to DNA via its N-terminus and rGCN4 binds C-terminally, the FLAG-tag likely does not directly influence DNA binding. However, it may influence the dimerization of the proteins, which is necessary for DNA binding. To further investigate this, the FLAG-tag can be cleaved using an enterokinase and potential changes in binding affinity analyzed. Furthermore, coiled coil formation, and the amount of dimeric and monomeric proteins could be further analyzed with circular dichroism spectroscopy (Greenfield, 2006).

4.3 In Silico Characterization using DaVinci

Figure 5: Molecular Dynamics Simulation of GCN4

We developed DaVinci, an in silico model, for rapid engineering and optimization of our PICasSO system. DaVinci serves as a digital twin to PICasSO, analyzing the forces acting on the system, refining experimental parameters, and identifying optimal interactions between protein staples and target DNA. The model was calibrated using literature data and experimental affinity results from rGCN4 EMSA assays with purified proteins.
DaVinci operates in three phases: static structure prediction, all-atom dynamics simulation, and long-range DNA dynamics simulation. We applied the first two phases to our components, allowing us to characterize the structure and dynamics of the DNA-binding interactions.
For our bivalent DNA-binding Mini staple (BBa_K5237009), consisting of GCN4 fused via a GSG-linker to rGCN4 (BBa_K5237008), we predicted the structure and binding affinity and tested various linker options. We evaluated the flexibility and rigidity of the constructs using pLDDT values from the predictions. Flexible linkers, like ('GGGGS')n, and rigid linkers, like ('EAAAK')n, were assessed (Arai et al., 2001). Predictions were colored by pLDDT scores, providing insights into chain rigidity (Akdel et al., 2022; Guo et al., 2022). Construct C (Fig. 5) was tested in the wet lab as part of BBa_K5237009, but it failed to bind DNA due to excessive rigidity, which inhibited subunit dimerization.

Figure 6: Variation of Linkers Connecting Our Mini Staples. Panels A (BBa_K5237007) and B (BBa_K5237008) show orientations of the leucine zipper, each bound to DNA. Panels C to I display linker variations colored by their pLDDT confidence score, which serves as a surrogate for chain flexibility (Akdel et al., 2022). Note that panels H and I are not bound to the second DNA strand. All structures were predicted using the AlphaFold server (Google DeepMind, 2024).

5. References

Fried, M. G. (1989). Measurement of protein-DNA interaction parameters by electrophoresis mobility shift assay. ELECTROPHORESIS, 10(5-6), 366-376. https://doi.org/10.1002/elps.1150100515

Akdel, M., Pires, D. E. V., Pardo, E. P., Janes, J., Zalevsky, A. O., Meszaros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., Shenoy, A., Zhu, W., Kundrotas, P., Serra, V. R., Rodrigues, C. H. M., Dunham, A. S., Burke, D., Borkakoti, N., Velankar, S., … Beltrao, P. (2022). A structural biology community assessment of AlphaFold2 applications. Nat Struct Mol Biol, 29(11), 1056–1067. https://doi.org/10.1038/s41594-022-00849-w

Arai, R., Ueda, H., Kitayama, A., Kamiya, N., & Nagamune, T. (2001). Design of the linkers which effectively separate domains of a bifunctional fusion protein. Protein Engineering, Design and Selection, 14(8), 529–532. https://doi.org/10.1093/protein/14.8.529

Chen, X., Zaro, J. L., & Shen, W.-C. (2013). Fusion protein linkers: Property, design and functionality. Advanced Drug Delivery Reviews, 65(10), 1357–1369. https://doi.org/10.1016/j.addr.2012.09.039

Google DeepMind. (2024). AlphaFold Server. https://alphafoldserver.com/terms

Greenfield, N. J. (2006). Using circular dichroism spectra to estimate protein secondary structure. Nature Protocols, 1(6), 2876–2890. https://doi.org/10.1038/nprot.2006.202

Guo, H.-B., Perminov, A., Bekele, S., Kedziora, G., Farajollahi, S., Varaljay, V., Hinkle, K., Molinero, V., Meister, K., Hung, C., Dennis, P., Kelley-Loughnane, N., & Berry, R. (2022). AlphaFold2 models indicate that protein sequence determines both structure and dynamics. Scientific Reports, 12(1), 10696. https://doi.org/10.1038/s41598-022-14382-9

Hellman, L. M., & Fried, M. G. (2007). Electrophoretic mobility shift assay (EMSA) for detecting protein-nucleic acid interactions. Nature Protocols, 2(8), 1849-1861. https://doi.org/10.1038/nprot.2007.249

Hollenbeck, J. J., Gurnon, D. G., Fazio, G. C., Carlson, J. J., & Oakley, M. G. (2001). A GCN4 Variant with a C-Terminal Basic Region Binds to DNA with Wild-Type Affinity. Biochemistry, 40(46), 13833-13839.

Hollenbeck, J. J., & Oakley, M. G. (2000). GCN4 Binds with High Affinity to DNA Sequences Containing a Single Consensus Half-Site. Biochemistry, 39(21), 6380-6389. https://doi.org/10.1021/bi992705n