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Genetic Algorithm Based Design and Experimental Characterization of a Highly Thermostable Metalloprotein.

Esra BozkurtMarta A S PerezRuud HoviusNicholas J BrowningUrsula Rothlisberger
Published in: Journal of the American Chemical Society (2018)
The development of thermostable and solvent-tolerant metalloproteins is a long-sought goal for many applications in synthetic biology and biotechnology. In this work, we were able to engineer a highly thermostable and organic solvent-stable metallo variant of the B1 domain of protein G (GB1) with a tetrahedral zinc binding site reminiscent of the one of thermolysin. Promising candidates were designed computationally by applying a protocol based on classical and first-principles molecular dynamics simulations in combination with genetic algorithm optimization. The most promising of the computationally predicted mutants was expressed and structurally characterized and yielded a highly thermostable protein. The experimental results thus confirm the predictive power of the applied computational protein engineering approach for the de novo design of highly stable metalloproteins.
Keyphrases
  • molecular dynamics simulations
  • machine learning
  • protein protein
  • genome wide
  • amino acid
  • deep learning
  • ionic liquid
  • molecular docking
  • small molecule
  • dna methylation
  • multidrug resistant
  • wild type
  • oxide nanoparticles