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Experimental optimization of protein refolding with a genetic algorithm
Author(s) -
Anselment Bernd,
Baerend Danae,
Mey Elisabeth,
Buchner Johannes,
WeusterBotz Dirk,
Haslbeck Martin
Publication year - 2010
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.488
Subject(s) - bottleneck , folding (dsp implementation) , solubilization , yield (engineering) , protein folding , genetic algorithm , chemistry , variety (cybernetics) , enzyme , computational biology , biological system , computer science , biochemistry , biology , materials science , machine learning , artificial intelligence , engineering , electrical engineering , metallurgy , embedded system
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.