## 3 Results and Discussion
### Improving Fragment Terpenoid Production via Enzyme Fusion  
Having demonstrated that _ERG9_ repression can improve the production titer of fragment terpenoid, we next turned to the second strategy of fusing the FPP synthase and the terpene synthase enzymes. Previous works have shown that creating a spatial constraint between the enzyme and the substrate can increase the efficiency of the metabolic pathway resulting in a higher product titer [26]. This can be achieved by one of the following approaches: engineering fusion proteins, using protein scaffolds, and compartmentalizing metabolic pathways. In this work, we focused on fusion proteins as the two latter approaches risk either overburdening the cells with expressing additional scaffold proteins (which could deplete cellular resources that could otherwise go to making the compound of interest) or obtaining poor product titer due to the lack of suitable organelle(s) and its corresponding transporters for transporting the FPP substrate in and the terpenoid product out. Enzyme fusion improves the flux toward FPP and further to the terpenoid of interest by enabling substrate channeling, which is the direct migration of the product of one enzyme to the active site of the second enzyme [27; 28]. Substrate channeling is faster than random diffusion of the intermediate through the bulk solution. For this strategy, we constructed enzyme fusions of FPPS and each of the selected terpene synthases (_Ac_HS1, _Amd_GS1, _Zz_BES2, and _C_/IPS2). A large body of work (see review [28]) demonstrates that the linker regions between the functional domains can influence the expression levels [29; 30], folding and stability [31; 32; 33; 34], as well as biological activities of the fusion enzymes [35; 36; 37]. In this study, we used two of the most commonly employed flexible linkers, the Gly-Ser-Gly (GSG) and the Gly-Gly-Gly-Gly-Ser (GGGGS) linkers [38; 34; 39], to fuse FPPS and the terpene synthases. As enzyme fusion can affect enzyme folding and stability, we also took structure prediction of the resulting enzyme fusions into consideration. Recent development in machine learning has led to significant advances in structural biology [40; 41]. In particular, AlphaFold (along with the subsequent release AlphaFold v2) has solved a long-standing challenge of predicting structures of a wide variety of proteins with remarkable accuracy using only the amino acid sequences as input [42; 43]. Using AlphaFold v2, we obtained predicted structures for the four selected terpene synthases and FPPS--as both standalone enzymes as well as fusion enzymes--and verified that the fused enzymes maintained the overall structures of the individual enzymes (Figures S5-S16). In addition to the type of linker used for fusing the enzymes, previous work by [PERSON] and coworkers has shown that the fusion order is also important as it can affect protein folding and the biological activities of these enzymes [44]. Notably, putting FPPS at the N terminus and the terpene synthase (germacrene synthase in this case) at the C terminus led to a germacrene titer up to two-fold higher than the level observed in the other enzyme fusion orientation. Using the work by [PERSON] and coworkers [44] as our guide, we constructed plasmids for expressing these enzyme fusions: FPPS-GSG-_Ac_HS1, FPPS-GGGGS-_Ac_HS1, FPPS-GGGGS-_Amd_GS1, FPPS-GGGGS-_Z_BES2, FPPS-GGGGS-_Z_BES2, FPPS-GSG-_C_/TPS2 and FPPS-GGGGS-_C_/TPS2. Additionally, we constructed plasmids for overexpressing the individual terpene synthases and the FPPS as distinct enzymes as controls to verify that the differences in the terpenoid titers are not due to additional expression of FPPS. We then transformed strain FPPY005 P\({}_{\text{MET3}}\)_-ERG9_ with these plasmids and measured the resulting strains' terpenoid levels when grown in a medium containing additional L-methionine supplementation. Enzyme fusion improved the production titer for only two out of the four terpenoid targets, \(\alpha\)-santalene and \(\delta\)-guaiene (Figure 8).