Modern
synthetic biology procedures rely on the ability to generate
stable genetic constructs that keep their functionality over long
periods of time. However, maintenance of these constructs requires
energy from the cell and thus reduces the host’s fitness. Natural
selection results in loss-of-functionality mutations that negate the
expression of the construct in the population. Current approaches
for the prevention of this phenomenon focus on either small-scale,
manual design of evolutionary stable constructs or the detection of
mutational sites with unstable tendencies. We designed the Evolutionary
Stability Optimizer (ESO), a software tool that enables the large-scale
automatic design of evolutionarily stable constructs with respect
to both mutational and epigenetic hotspots and allows users to define
custom hotspots to avoid. Furthermore, our tool takes the expression
of the input constructs into account by considering the guanine-cytosine
(GC) content and codon usage of the host organism, balancing the trade-off
between stability and gene expression, allowing to increase evolutionary
stability while maintaining the high expression. In this study, we
present the many features of the ESO and show that it accurately predicts
the evolutionary stability of endogenous genes. The ESO was created
as an easy-to-use, flexible platform based on the notion that directed
genetic stability research will continue to evolve and revolutionize
current applications of synthetic biology. The ESO is available at
the following link:
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