Computational Pathway Design for Funneling Lignin Intermediates to Aromatic Products

The heterogeneity of the aromatic products from lignin catalytic depolymerization is one of the major challenges for lignin valorization. Microbes have evolved many catabolic pathways to tackle the challenge by funneling such heterogeneous intermediates to a few central aromatic products, which lead further to intra- or extradiol ring opening to produce value-added chemicals. However, such funneling pathways are only well-characterized in a few organisms such as Sphingobium sp. SYK-6 and Pseudomonas putida KT2440. Further effort to explore novel bacterial funneling pathways is necessary. Herein, we apply the de novo pathway design tool (novoStoic [1]) to computationally prospect all possible pathways for lignin-derived mono- and biaryls. First, we augment the MetRxn database with a new dataset of elementally balanced reaction operators using the automated atom mapping based reaction rule extraction procedure termed rePrime. For each reaction, the rePrime procedure identifies and captures as reaction rules the molecular graph topological changes underpinning the substrate to product graph conversion. A reaction rule captures the location of active reaction centers affected by the conversion of substrates to products. The reaction rules and known reactions are then operated upon a mixed integer linear programming algorithm (novoStoic) to identify a mass-balanced biochemical network that converts a source to a target metabolite while minimizing the number of de novo steps. We demonstrate the application of novoStoic in a few case studies such as designing shorter pathways from ferulate to protocatechuate and exploring C𝛼-C𝛽 cleavage pathway of guaiacylglycerol-𝛽-guaiacyl ether. By exploring the uncharted chemical space enabled by enzyme promiscuity, novoStoic paves the way to identify more carbon/energy efficient lignin funneling pathways with minimal heterologous enzymes to engineer, while exploring the organism’s potential underground metabolism.

References

[1] Kumar, A., et al. Pathway design using de novo steps through uncharted biochemical spaces. Nature communications 9.1 (2018): 184.