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

Neda Hassanpour, Ehsan Ullah, Mona Yousofshahi, Nikhil U. Nair, Soha Hassoun

Research output: Contribution to journalArticle

6 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)37-47
Number of pages11
JournalMetabolic Engineering Communications
Publication statusPublished - 1 Jun 2017
Externally publishedYes



  • Directed evolution of enzymes
  • Enzyme engineering
  • Flux-balance analysis
  • Pathway analysis
  • Pathway synthesis
  • Selection

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism
  • Biomedical Engineering

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