A bio-inspired cell-free system for cannabinoid production from inexpensive inputs.

Moving cannabinoid production away from the vagaries of plant extraction and into engineered microbes could provide a consistent, purer, cheaper and environmentally benign source of these important therapeutic molecules, but microbial production faces notable challenges. An alternative to microbes and plants is to remove the complexity of cellular systems by employing enzymatic biosynthesis. Here we design and implement a new cell-free system for cannabinoid production with the following features: (1) only low-cost inputs are needed; (2) only 12 enzymes are employed; (3) the system does not require oxygen and (4) we use a nonnatural enzyme system to reduce ATP requirements that is generally applicable to malonyl-CoA-dependent pathways such as polyketide biosynthesis. The system produces ~0.5 g l-1 cannabigerolic acid (CBGA) or cannabigerovarinic acid (CBGVA) from low-cost inputs, nearly two orders of magnitude higher than yeast-based production. Cell-free systems such as this may provide a new route to reliable cannabinoid production.

A simple method for modeling transmembrane helix oligomers.

We describe an effective procedure for modeling the structures of simple transmembrane helix homo-oligomers. The method differs from many previous approaches in that the only structural constraint we use to help select the correct model is the oligomerization state of the protein. The method involves the following steps: (1) perform 100-250 independent Monte Carlo energy minimizations of helix pairs to produce a large collection of well-packed structures; (2) filter the minimized structures to find those that are consistent with the expected symmetry of the oligomer; (3) cluster the structures that pass the symmetry filter; and (4) select a representative of the most populous cluster as the final prediction. We applied the method to the transmembrane helices of five proteins and compare our results to the available experimental data. Our predictions of glycophorin A, neu, the M2 channel and phospholamban resulted in a single model for each protein that agreed with the experimental results. In the case of erbB-2, however, we obtained three structurally distinct clusters of approximately equal sizes, so it was not possible to identify a clearly favored structure. This may reflect a real heterogeneity of packing modes for erbB-2, which is known to interact with different receptor subunits. Our method should be useful for obtaining structural models of transmembrane domains, improving our understanding of structure/function relationships for particular membrane proteins.