BioRetroSynth: Metabolic Engineering using Retrosynthesis
Our aim is to
demonstrate that retrosynthesis, a widely
used technique in chemistry, can be utilized to engineer chassis
organisms such as E. coli to
biosynthesize specific target compounds.
Retrosynthesis analysis transforms a
synthetic target product into precursors, following pathways to
commercially available starting materials. The
transformations are applied in the reverse direction from the actual
synthesis. In the case of metabolic engineering, retrosynthesis
consists of applying reversed biotransformations (i.e., reversed
enzymes catalyzed reactions) to a target product, following pathways to
substrates that are endogenous to a chassis organism.
Bioretrosynthesis. The algorithm of retrosynthesis that we have developed searchs for heterologous genes and pathways,
which once introduced into a given chassis organism can produce a
target compound. These heterologous genes and their associated
metabolites are organized into an annotated retrosynthesis graph where substrates, products and reactions are coded into
molecular signatures. We use machine learning to mine genomic databases
for predicting protein function and enzymes catalyzing specific substrates.
We are currently using the algorithm to build retrosynthesis graphs for heterologous antibiotics production in E. coli.
Pathway design. Enumerate and rank all possible pathways in
the retrosynthesis graphs to help decide which pathways are best to engineer to produce a given
target product. Enzymes can potentially process multiple substrates or reactions, we study enzyme promiscuity
to enhance enzyme efficiency by protein engineering techniques. In
addition, we have developed a Quantitative Structure-Activity
Relationship (QSAR) for enzyme activity and inhibition based on
experimental databases and toxicity assays. This QSAR is used to reverse engineer enzymes
having specific activity through a process named inverse QSAR.
Metabolic engineering. We engineer E. coli
plasmids in our lab in order to construct combinatorial libraries of highest rank
heterologous pathways found to produce a
target product. Combinatorial libraries are generated through the use of assembly PCR tested first in vitro, then in vivo. Production of the target metabolite is verified by
metabolites concentrations and fluxes.
Engineering optimization. We
use Flux Balance Analysis (FBA) and non-linear optimization methods to
populate the metabolic map of the
engineered chassis with kinetics parameters and to determine positive
negative enzyme regulations to maximize target yield. The robustness of
the optimal fluxes are sampled in order to perform a sensitivity