ArticleMetabolic engineering communications2017
Selection Finder (SelFi): A computational metabolic engineering tool to enable directed evolution of enzymes.
Article in Metabolic engineering communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 8 citations in OpenAlex.
- Rigidifying Flexible Regions of a Bacterial Laccase Enables High-Temperature Aflatoxin B1 Degradation.Microorganisms · 2026Article
- Establishing synthesis pathway-host compatibility via enzyme solubility.Biotechnology and bioengineering · 2019Article
- OptCouple: Joint simulation of gene knockouts, insertions and medium modifications for prediction of growth-coupled strain designs.Metabolic engineering communications · 2019Article
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Authors and funding
5 authors at 1 institution in 1 country.
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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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