Biology Reference
In-Depth Information
With the identification of metabolic reactions that have not been previously characterized
by enzymes in known organisms, enzymes that are capable of similar chemical
bioconversions need to be engineered to perform the desired bioconversion of a substrate to
the desired product. This is to fill in the biochemical
found in the pathway prediction
programs, since the programs do not pinpoint which specific enzyme is needed in the novel
pathway. One way to fill in such gaps is to engineer an enzyme for carrying out a particular
nonnatural reaction of interest. A successful case of such engineering enzymes by directed
evolution is the engineering of an (
R
)-selective transaminase for sitagliptin synthesis.
44
By
using computational tools for active site analysis, the substrate binding site was evaluated.
The enzyme was then subjected to multiple rounds of mutations to expand the binding site
in order to accommodate a truncated homologue of sitagliptin. Further engineering of the
enzyme ultimately led to an efficient biocatalyst for sitagliptin synthesis. Similarly, Baker
and colleagues have computationally designed enzymes and experimentally demonstrated
de novo reactions. Nonnatural enzymatic retro-aldol,
45
Kemp-elimination,
46
and Diels-
Alder reactions
47
are successful stories of designing biocatalysts using the Rosetta
methodologies assisted with RosettaMatch and RosettaDesign to in silico design a novel
enzyme
,
to select for the ideal protein backbones and optimize the matches. These examples
deserve particular attention, since the enzymes developed for each study carry out chemical
reactions that have never been observed in nature. Although the aforementioned
biocatalysts need to be in specific in vitro reaction conditions, the computational methods
for designing enzymes can be of great assistance to synthetic biology for stain design,
because without exploring the enzyme evolutions, pathway prediction algorithms alone
may not be sufficient for actually producing recombinant strains for high titer production of
chemicals from renewable feedstock.
'
gaps
'
COMPUTATIONAL TOOLS FOR STRAIN OPTIMIZATION
In addition to these computational tools for synthetic biology components, the in silico
genome-scale metabolic model is another aspect in strain design. Employing constraints-
based flux balance analysis (FBA), an insight into cellular metabolism under various genetic
and/or environmental perturbed conditions can be obtained and strategies into modifying
the metabolic network through genetic manipulations can be developed.
48
To this end,
various algorithms have been developed to dissect and understand genotype
151
phenotype
relationships and to represent strategies for improving production of the desired
bioproducts.
49
In order to evaluate the physiological features of strains under gene knockout conditions,
minimization of metabolic adjustment (MOMA) and OptKnock algorithms have been
developed.
50,51
In MOMA, the metabolic fluxes under knockout mutant strain are assumed
to lead the minimal flux redistribution with respect to those of the wild-type. Therefore,
MOMA results represent a unique flux distribution which has very similar fluxes to the wild-
type strain.
50
For example, to further strain optimization, Park et al. used MOMA to
improve the L-valine production in
E. coli
.
11
The triple knockout genes (
aceF
,
mdh
, and
pfkA
)
were identified by using MOMA simulation, which allowed a 45.5% increase in L-valine
production, and also as high as 0.378 g of L-valine per gram of glucose.
OptKnock was also developed to identify knockout gene targets.
51
OptKnock included two
competing objective functions such as cellular growth and biochemical production. This
bilevel optimization algorithm leads to the overproduction of desired biochemicals by
adjusting the metabolic fluxes under gene knockout conditions. The redistributed fluxes
were then expected to improve production of the target metabolites which are essential
components for cellular growth. Recently, OptKnock was successfully demonstrated for the
production of 1,4-butanediol(BDO), a nonnatural chemical, in
E. coli
.
52
OptKnock results
represented a promising strategy, removing four genes (
adhE
,
pfl
,
ldh
,and
mdh
). This strategy
