UC Davis

Enzyme discovery through mining nature’s biocatalysis dataset, also known as enzyme genome mining, has gained a lot of popularity in recent years, mainly because of the fast-growing number of sequenced genes available resulted from the development of DNA sequencing technologies. The newly found enzymes with novel activity or disparate specificity can drastically expand enzyme repertoire. However, when a target reaction is identified, how to recognize the candidates from a large pool of enzymes remain a problem. For my thesis, I started on a real-world problem, citrus bitterness. I explored the possible glycosyltransferases to solve the problem. Secondly, I proposed an integrative pipeline of functional profiling and machine learning models to predict and decipher the complicated glycosyltransferase’s acceptor specificity puzzle. In my third project, I genome mined ketoacid decarboxylases for oxaloacetate decarboxylating reaction, which is the key reaction to build a useful biosynthetic pathway of 3-HP. For the fourth and final project I have focused on, I applied computational enzyme design techniques to tailor the enzyme active site to utilize an unnatural redox cofactor. Overall, I will demonstrate the effectiveness of the combination of enzyme genome mining and enzyme engineering in food processing, natural product formation, and synthetic biology. Chapter 1. A major problem in the orange industry is “delayed” bitterness, which is caused by limonin, a bitter compound developing from its non-bitter precursor limonoate A-ring lactone (LARL) during and after extraction of orange juice. The glucosidation of LARL by limonoid UDP-glucosyltransferase (LGT) to form non-bitter glycosyl-limonin during orange maturation has been demonstrated to be the natural way to debitter by preventing the formation of limonin. Here the debittering potential of heterogeneously expressed maltose-binding protein (MBP) fused-cuLGT from Citrus unishiu Marc in Escherichia coli is evaluated. An LC-MS method was established to determine the concentration of limonin and its derivatives. The protocols to obtain its potential substrates, LARL and limonoate (limonin with both A and D ring open), were also developed. Surprisingly, MBP-cuLGT didn’t exibit any detectable effect on limonin degradation when Navel orange juice was used as the substrate and was unable to biotransform either LARL or limonoate as purified substrates. However, it was found that MBP-cuLGT displayed a broad activity spectrum towards flavonoids, confirming the enzyme produced was active under the conditions evaluated in vitro. Our results demonstrate that cuLGT functionality was incorrectly identified, and highlight the need for further efforts to identify the enzyme responsible for LGT activity in order to develop biotechnology based approaches for producing orange juice from varietals that traditionally have a delayed bitterness. Chapter 2. Accurate prediction of enzyme function and substrate specificity remains a long-lasting problem. Sequence similarity- and structure- based function annotation methods provide a coarse prediction ability, but it is not sufficient to distinguish the nuanced substrate specificity and scope among the closely related enzymes. Previous efforts on glycosyltransferase family 1 (GT1) function prediction such as glycosyl acceptor selectivity perform poorly in that GT1 orthologues display diverse function profiles utilizing a broad spectrum of substrates and available computational algorithm failed to recapitulate the complex sequence-function relationship. Here, throughout characterization of 22 selected evolutionary distant GT1s representing the entire GT1 protein space are performed against 47 structurally and functionally diverse acceptors. The acquired activity dataset enables us to train and evaluate different machine learning models with features encoding multiple aspects of the reactions thought bioinformatics and cheminformatic analysis. The random forest-based model, named with GtRF, has the best performance, as gauged by various statistic indicators, with advantage of interpretability and generality. GtRF also shows the comparable accuracy and recall when it is adopted for forward prediction on novel enzyme-substrate pairs. Finally, we proposed an extensive usage of the predication models to annotate the whole GT1 family. Chapter 3. 3-Hydroxypropionate (3-HPA) is a 3-carbon precursor to many commodity and specialty chemicals. The construction of workhorse microorganisms with economically viable biosynthetic pathways is essential for the production of 3-HPA. Despite different pathways reported, all suffer from a variety of drawbacks, including low efficiency and yield, using expensive substrates and generating multiple side products. Here, a novel routine to produce 3-HPA using glucose as a feedstock is proposed. This pathway converts phosphoenolpyruvate, one of the metabolites in glycolysis, into oxaloacetate. Decarboxylation reaction of oxaloacetate is then involved to obtain 3-oxopropanoate, followed by a reduction reaction to produce 3-HPA. However, no enzymes have been reported to catalyze the decarboxylation reaction of oxaloacetate to 3-oxopropanoate. To find an active enzyme towards oxaloacetate, we applied 2 rounds of genome enzyme mining in thiamine diphosphate (TPP) dependent ketoacid decarboxylases and discovered a panel of enzymes with promiscuity towards oxaloacetate. The most active candidate D1Y3P7 from Pyramidobacter piscolens was then rationally engineered; However, we did not achieve increased catalytic efficiency. We also screened and characterized the dehydrogenases and pyruvate carboxykinases that are favorable for the pathway construction. All of these enzymes were tested in in-vitro system and current work served as the basis for next step fermentation experiment for the production of 3-HPA. Chapter 4. Unnatural redox cofactors which operate in an orthogonal manner to NAD(P) + in vivo may mediate specific reducing power delivery in whole-cell biomanufacturing. Furthermore, using less expensive analogs of NAD(P) + in vitro may greatly reduce the cost of cell-free biotransformation. Here, we have developed an unnatural redox cofactor system based on NMN+ . We explored its applications in lower-cost redox cycling in vitro by accomplishing two proof-of-concept example enzymes, Bs GDH and Ec GapA. This was enabled by a computationally designed methods and achieved resulting specificity switch towards an unnatural nicotinamide cofactor: GDH design with a 107 -fold cofactor specificity switch towards NMN + over NAD(P) while GapA double mutant A180S-G10R with ~200-fold cofactor specificity switch from NAD + to NMN+ . Overall, this work opened opportunities to design biochemistry using unnatural redox cofactors.