Selection Finder (SelFi): A computational metabolic engineering tool to enable directed evolution of enzymes

Metabolic Engineering Communications
Neda HassanpourSoha Hassoun

Abstract

Directed evolution of enzymes consists of an iterative process of creating mutant libraries and choosing desired phenotypes through screening or selection until the enzymatic activity reaches a desired goal. The biggest challenge in directed enzyme evolution is identifying high-throughput screens or selections to isolate the variant(s) with the desired property. We present in this paper a computational metabolic engineering framework, Selection Finder (SelFi), to construct a selection pathway from a desired enzymatic product to a cellular host and to couple the pathway with cell survival. We applied SelFi to construct selection pathways for four enzymes and their desired enzymatic products xylitol, D-ribulose-1,5-bisphosphate, methanol, and aniline. Two of the selection pathways identified by SelFi were previously experimentally validated for engineering Xylose Reductase and RuBisCO. Importantly, SelFi advances directed evolution of enzymes as there is currently no known generalized strategies or computational techniques for identifying high-throughput selections for engineering enzymes.

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Citations

Feb 26, 2019·Biotechnology and Bioengineering·Sara A AminSoha Hassoun
May 13, 2018·Genes·Denis ShepelinMarkus J Herrgård

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Methods Mentioned

BETA
Fluorescence-Activated Cell Sorting

Software Mentioned

retroProPath
CosMos
CCOpt
SelFi
Selection Finder ( SelFi )
MOMAKnock
OptORF
OptForce
OptReg
MC

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