To develop our strategy, we began by searching for all the sequences of the five enzymes in databases such as NCBI. Obtaining these FASTA sequences, structural modeling by homology was carried out using the SWISS-MODEL software for the five native enzymes (Figure 5).

Figure 5. Models from our proteins.

Source: Authors, 2023.

In which, A represents the CsHCS1 model; B, the dimeric OS; C, the dimeric OAC; D, the CsPT4 and E, the CBDAS model.

After obtaining these models, we again carried out structural modeling but with the incorporation of the T2A sequences using an extension of PyMOL (PyMOL Molecular Graphics System): MODELLER, which provided a way of incorporating these new amino acids into the initial structure in three different ways: at the beginning of the original sequence, at the end of this sequence or both. Next, we used ProCheck and MolProbity to determine which of the constructed sequences presented the most stable models. This allowed us to observe the structural differences resulting from the insertion of the new sequences and to identify which of the constructions with the T2A sequences presented the best energetic, structural and geometric qualities (Figure 6). Once this stage was completed, we used the best results to assemble the necessary biological circuits.

Figure 6. Models from our proteins with T2A sequence

Source: Authors, 2023.

In which, A represents the CsHCS1 T2A model with the sequence at the end; B, the monomeric OS T2A model with the sequence on both beginning and end; C, the monomeric OAC T2A model with the sequence at the beginning; D, the CsPT4 without the transit peptide and T2A at the beginning and E, the CBDAS T2A model with the sequence at the end. All the T2A sequences are in red.

Expression vectors were constructed using the plasmids pRS425 and pRS426 using the Gibson Assembly method, a technique used to clone DNA fragments continuously. Around 72 biological circuits compatible with the expression system were designed from Snapgene, using the enzymes along with the T2A sequences. The details of this construction can be seen in Model page - Biological Circuits .

The criteria used by the team to select the circuits was based on the ease of separating the synthesis into two main parts: olivetolic acid synthesis and cannabidiol synthesis, so that the synthesis could be carried out independently.

Figure 7. Developed pRS425s plasmid for the expression of the enzymes CsPT4 (prenyltransferase) and CsCBDAS (cannabidiolic acid synthase).

Source: Authors, 2023.

Figure 8. Developed pRS426 plasmid for the expression of the enzymes CsHCS (Hexanoyl-CoA Synthetase), OS (Olivetol synthase) and OAC (Olivetolic acid synthase).

Source: Authors, 2023.

Finally, we conducted a phylogenetic analysis by running MUSCLE-aligned sequences on the Jalview software, in which we compared the sequences of the enzymes in Cannabis sativa with other sequences of similar proteins in different families and genera. This allowed us to study whether the protein domains had been maintained, confirming that the catalytic sites had not been altered and that there were no problems related to the structural integrity and function of the enzymes. This analysis also allowed us to discover uncatalogued characteristics of the enzymes studied, such as some binding and catalytic positions.

To complement our analyses, we performed molecular docking, which consists of a computational technique that models the interaction between a protein and substrate and predicts how these molecules fit together, and spatially, how they interact. We used this technique to study how different substrates interact with our five proteins of interest, taking into account conserved residues. By looking at which residues are involved in the interaction with substrates and how these residues have evolved, we can gain insights into how the protein works and how its activity may have evolved to play different biological roles.

This integrated approach between molecular docking and phylogenetic analysis allows for a deeper understanding of how the protein interacts with its substrate and how this interaction has evolved over time (see Modeling page, on the Docking section for more information).

It is important to remember that for the analysis and validation of the docking, some procedures were followed. Firstly, phylogenetics helped to understand the structural conformation based on the functionality of different proteins from the same protein family and, therefore, similar roles in reaction catalysis. In this way, it was possible to identify catalytic, binding, H+ acceptor or donor sites and their respective physicochemical characteristics, based on the conservation study of certain regions of the domain of the primary sequences of the enzymes evaluated.

Furthermore, the corroboration of the catalytic pockets inserted in the most likely cavities indicated by the CavityPlus server (Wang et al., 2023), helped to corroborate how likely the ligand would be to fit into the given conformation. Finally, the ten best evaluated docking results by energy quality and occurrence, on the chosen server, HDOCK (Yan et al., 2020), were related to existing information, with attention to the description of each molecule and each chemical space involved, finding a better model for the enzyme-substrate complexes mentioned in the metabolic pathway.

Figures 9a and 9b. Dockings between the enzymes and substrates presented at metabolic pathway.

Source: Authors, 2023.

In this figure, “A” represents the CsHCS docked with Hexanoic Acid; “B”, “C”, “D” and “E”, the OS docked with different substrates - respectively, Hexanoyl-CoA and Malonyl-CoA in three conformations -; “F”, the OAC docked with Olivetol; “G” and “H”, CsPT4 sequentially docked with Olivetolic Acid and GPP, and finally, CBDAS docked with CBGA and catalyzing the reaction to produces CBDA.

Thinking of a way to model the expression and evidence mechanism to obtain CBDA (cannabidiolic acid) and subsequently dehydrate it to CBD (cannabidiol) to obtain the final product of this research, our time searched for different standards and models applicable to yeasts. From this, we found the mixed effects (ME) model, being a statistical representation of the form of gene expression after transformation (Llamosi et al. , 2016).

With ME, we are examining a simplified dynamic scenario of gene expression, where we take into account that the translational parameter depends on the transcriptional parameter, which, in turn, is associated with the different factors that affect the initiation of transcription (see Modeling page at Expression Modeling section for more information). This type of understanding is very important to describe, as we have several parameters to be evaluated in the Wet Lab and the representativeness of the cellular process can be cataloged.

In this manner, we've developed a model to guide our experimental analysis. It allows us to examine different conditions for each sample point and grasp the expression patterns within our yeast profile. By doing so, we can identify potential bottlenecks in the modified cellular metabolism. Consequently, utilizing regression analysis, we can derive the values of the constants k and g for each equation. This model is constructed based on observed sample values, enhancing our comprehension of the production process for the CBDA.

Furthermore, we use a concept that is very present in synthetic biology: logic gates. At the interface between electrical engineering and genetic engineering, doors emerged as an analogy for visualizing and understanding the cell as a productive process, a chassis. Thus, thinking about Boolean logic for biomolecules, as described by Miyamoto et al. (2013), we identified some determinants as our gates: a, being the CDS promoter that first encodes all other enzymes of the biological pathway, in addition to the subsequent proteins, since they are interdependent on each other.

As seen on the Modeling page (see Expression Modeling section), different circuits were designed for different cases of experimentation: the first one with the metabolic pathway of CBDA (Figure 10) and the second one fragmented, between the olivetolic acid production circuit and the CBDA production from olivetolic acid circuit (Figure 11).

Figure 10. Complete Boolean-based logic circuit for CBDA metabolic pathway

Source: Authors, 2023.

Figure 11. Boolean-based logic circuit for Olivetolic Acid and the circuit from Olivetolic Acid to CBDA.

Source: Authors, 2023.

With both analyses made, we could evaluate the probability from each case (see Expression Modeling), concluding, in first, that the second one could give us better responses, despite being a longer-time than the first case.

In summary, the CBDynamics project, developed by the iGEM USP EEL Brazil team, draws on various theoretical foundations of synthetic biology. The application of advanced structural modeling techniques, phylogenetic analyses and molecular docking demonstrates a thorough and multidisciplinary research to produce cannabidiol (CBD) using the yeast Saccharomyces cerevisiae as a base.

The strategy of adding T2A sequences to the enzymes to ensure effective synthesis uses concepts of gene manipulation. In addition, the careful selection of biological circuits and the appropriate use of expression vectors demonstrate the need for good planning, accompanied by a great deal of study. Phylogenetic analysis and molecular docking increase our understanding of the interactions between proteins and substrates, providing valuable information on the function of enzymes and their evolution. This integrated approach is crucial to the success of the project.

All in all, the work done so far lays a solid and promising foundation for the next phase of the project in 2024. This research exemplifies excellence in the application of synthetic biology to produce relevant compounds with the potential to positively impact the pharmaceutical industry and the field of biotechnology.