Mini Staple: bGCN4

The bGCN4 Mini staple is a fusion of the basic leucine zipper GCN4 and rGCN4, offering a smaller, compact protein staple. With its well-characterized subunits and strong in silico and experimental validation, this Mini staple serves as a versatile foundation for expanding to similar staples.

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

Small basic-region leucine zipper (bZip) proteins are characterized by their DNA-binding. The bZip motif consists of a coiled-coil leucine zipper dimerization domain, and a highly charged basic region that binds to DNA (Hollenbeck & Oakley, 2000). One well characterized example is the General Control Protein 4 (GCN4), a well-characterized transcriptional activator from yeast (Arndt & Fink, 1986).
At its N-terminus, GCN4 contains basic residues, the so-called bZip domain, through which it binds specifically to the CRE (cyclic AMP response element) DNA sequence (5' ATGACGTCAT 3') (Hollenbeck et al., 2002). A variant of GCN4 with the DNA binding bZip-domain at the C-terminus (rGCN4) has been engineered to bind to the inverted CRE sequence, INV2 (5' GTCAtaTGAC 3', upper case letters indicate direct interaction between protein and DNA) with similar affinity (Hollenbeck et al., 2001). By genetically fusing GCN4 to rGCN4, we created a small, bivalent DNA binding staple with less than 150 amino acids.

3. Assembly and Part Evolution

The amino acid sequences for GCN4 and rGCN4 were obtained from literature (Hollenbeck et al. 2001), and codon-optimized for Escherichia coli. The two leucine zippers were combined with a GSG linker harbouring a BamHI site to adapt the construct with different linker designs, based on our dry lab DaVinci model. 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. Expression of FLAG-GCN4 was done under an IPTG inducible T7 promoter in E. coli BL21 (DE3) cells.

4. Results

The current version of the part with GCN4-rGCN4 fusion, linked by a GSG peptide linker has not demonstrated any DNA binding in the tests conducted thus far. Nevertheless, we believe the part to still be a valuable addition, as it can be further engineered with different linker types to create a functioning staple. Through dry lab modelling, various linker designs have already been simulated to predict improved dimerization and DNA binding.

4.1 Protein Expression and Purification

The bZip proteins GCN4, rGCN4 and the fusion thereof, bGCN4, were fused N-terminally to a FLAG-tag (DYKDDDDK). All proteins could be readily expressed under the T7 promoter in E. coli BL21 DE3 and purified with Anti-FLAG affinity columns. The purity of the proteins was confirmed by SDS-PAGE (Fig. 2).

Figure 2: SDS-PAGE Analysis of Protein Purification. Analysis of fractions eluate of purified protein taken during Anti-FLAG affinity chromatography 1 µL of each sample was prepared with Laemmli buffer and loaded on 4-15% TGX-Gel. Correct bands of interest are highlighted by red

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 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).

4.2.1 Qualitative DNA Binding Analysis

To asses possible DNA binding, a qualitative EMSA was performed with the purified proteins. The same binding buffer conditions were used, as previously described for GCN4 and rGCN4 (Hollenbeck et al. 2001). DNA binding could only be shown for GCN4 and rGCN4, no visible band was observed for the bGCN4 fusion protein (Fig. 4).

The 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 counterpart. (Hellman & Fried, 2007). Mobility-shift assays can be used both 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 bGCN4 was incubated in binding buffer with a 20 bp DNA probe containing the either the CRE (GCN4 binding) sequence (5' ATGACGTCAT 3') or the INVii (rGCN4 binding) sequence (5' GTCAtaTGAC 3') until equilibration. Subsequently the formed protein-DNA complexes were loaded on a native PAGE. Afterwards the DNA bands were stained with SYBR-safe.
The bGCN4 fusion protein did not show any DNA binding for both target sites.

Figure 4: Qualitative EMSA DNA Binding 0.5 µM of annealed oligos (20 bp) containing either one CRE or INVii binding site were incubated with 200 µM of protein and equilibrated in binding buffer (137 mM NaCl, 2.7 mM KCl, 10 mM Na ₂HPO₄, 1.8 mM KH₂HPO₄, 0.1 % (v/v) IGEPAL® CA-360, 1 mM EDTA). Gel electrophoresis was performed with a pre-equilibrated TGX-Gel in TBE running buffer. Gel-bands were visualized by staining with SYBR Safe and imaged with an UV transilluminator.

4.2.2 Quantitative DNA Binding Analysis

To further analyze DNA binding of the staple subunits, quantitative shift assays were performed for GCN4 and rGCN4. Here 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 5: K_d Calculation of GCN4 and rGCN4 Quantitative assessment of binding affinity for GCN4 and rGCN4. Proteins of varying concentrations were incubated with 0.5 µM DNA (20 bp, containing one binding site for CRE or INVii) in Binding buffer 1, and the bound fraction analyzed by dividing pixel intensity of bound fraction with pixel intensity of bound and unbound fraction using ImageJ. 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 INVii a 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 6: 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).