Statistical approaches to maximize recombinant protein expression in Escherichia coli: A general review

Christos P. Papaneophytou, George Kontopidis

Research output: Contribution to journalReview articlepeer-review

89 Citations (Scopus)

Abstract

The supply of many valuable proteins that have potential clinical or industrial use is often limited by their low natural availability. With the modern advances in genomics, proteomics and bioinformatics, the number of proteins being produced using recombinant techniques is exponentially increasing and seems to guarantee an unlimited supply of recombinant proteins. The demand of recombinant proteins has increased as more applications in several fields become a commercial reality. Escherichia coli (E. coli) is the most widely used expression system for the production of recombinant proteins for structural and functional studies. However, producing soluble proteins in E. coli is still a major bottleneck for structural biology projects. One of the most challenging steps in any structural biology project is predicting which protein or protein fragment will express solubly and purify for crystallographic studies. The production of soluble and active proteins is influenced by several factors including expression host, fusion tag, induction temperature and time. Statistical designed experiments are gaining success in the production of recombinant protein because they provide information on variable interactions that escape the "one-factor-at-a-time" method. Here, we review the most important factors affecting the production of recombinant proteins in a soluble form. Moreover, we provide information about how the statistical design experiments can increase protein yield and purity as well as find conditions for crystal growth.

Original languageEnglish
Pages (from-to)22-32
Number of pages11
JournalProtein Expression and Purification
Volume94
DOIs
Publication statusPublished - 2014

Keywords

  • Escherichia coli
  • Fractional factorial
  • Recombinant protein
  • Response surface methodology (RSM)
  • Solubility enhancement
  • Statistically designed experiments

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