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

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...

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Publicado en:Journal of the American Chemical Society Vol. 140; no. 13; pp. 4517 - 4522
Autores principales: Bozkurt, Esra, Perez, Marta A. S., Hovius, Ruud, Browning, Nicholas J., Rothlisberger, Ursula
Formato: Artículo
Publicado: American Chemical Society 4/4/2018
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 4/4/2018
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      pub: American Chemical Society
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        10.1021/jacs.7b10660
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        atl: Genetic Algorithm Based Design and Experimental Characterization of a Highly Thermostable Metalloprotein.
      aug:
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          Bozkurt, Esra
          Perez, Marta A. S.
          Hovius, Ruud
          Browning, Nicholas J.
          Rothlisberger, Ursula
        affil:
          Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
          Laboratory of Protein Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
      su:
        Structural optimization
        Genetic algorithms
        Synthetic biology
        Metalloproteins
        Molecular dynamics
        Mathematical models
      sug:
        subj:
          Structural optimization
          Genetic algorithms
          Synthetic biology
          Metalloproteins
          Molecular dynamics
          Mathematical models
      ab: 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.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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