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Experimental optimization of protein refolding with a genetic algorithm

  • Technical University of Munich

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Refolding of proteins from solubilized inclusion bodies still represents a major challenge for many recombinantly expressed proteins and often constitutes a major bottleneck. As in vitro refolding is a complex reaction with a variety of critical parameters, suitable refolding conditions are typically derived empirically in extensive screening experiments. Here, we introduce a new strategy that combines screening and optimization of refolding yields with a genetic algorithm (GA). The experimental setup was designed to achieve a robust and universal method that should allow optimizing the folding of a variety of proteins with the same routine procedure guided by the GA. In the screen, we incorporated a large number of common refolding additives and conditions. Using this design, the refolding of four structurally and functionally different model proteins was optimized experimentally, achieving 74-100% refolding yield for all of them. Interestingly, our results show that this new strategy provides optimum conditions not only for refolding but also for the activity of the native enzyme. It is designed to be generally applicable and seems to be eligible for all enzymes. Published by Wiley-Blackwell.

Original languageEnglish
Pages (from-to)2085-2095
Number of pages11
JournalProtein Science
Volume19
Issue number11
DOIs
StatePublished - Nov 2010

Keywords

  • Genetic algorithms
  • High-throughput optimization
  • Inclusion bodies
  • Multiobjective optimization
  • Protein refolding

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