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Computationally optimizing the direc...

Voigt, Christopher Ashby.

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Computationally optimizing the directed evolution of proteins.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computationally optimizing the directed evolution of proteins./
作者:	Voigt, Christopher Ashby.
面頁冊數:	330 p.
附註:	Source: Dissertation Abstracts International, Volume: 63-06, Section: B, page: 2752.
Contained By:	Dissertation Abstracts International63-06B.
標題:	Biophysics, General. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3057865
ISBN:	0493731644

Computationally optimizing the directed evolution of proteins.
Voigt, Christopher Ashby.

Computationally optimizing the directed evolution of proteins. - 330 p.

Source: Dissertation Abstracts International, Volume: 63-06, Section: B, page: 2752.

Thesis (Ph.D.)--California Institute of Technology, 2003.

Directed evolution has proven a successful strategy for protein engineering. To accelerate the discovery process, we have developed several computational methods to optimize the mutant libraries by targeting specific residues for mutagenesis, and subunits for recombination. In achieving this goal, a statistical model was first used to study the dynamics of directed evolution as a search algorithm. These simulations improved our understanding of the relationship between parameters describing the search space (e.g., interactions between amino acids) and experimental search parameters (e.g., mutation rate and library size). Based on these simulations, a more detailed model was used to calculate the structural tolerance of each residue to amino acid substitutions. Further, a computational model was developed to optimize recombination experiments, based on the three-dimensional structure. Together, these computational techniques represent a major step towards information-driven combinatorial protein design.

ISBN: 0493731644Subjects--Topical Terms:

1019105
Biophysics, General.

Computationally optimizing the directed evolution of proteins.
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520 $a Directed evolution has proven a successful strategy for protein engineering. To accelerate the discovery process, we have developed several computational methods to optimize the mutant libraries by targeting specific residues for mutagenesis, and subunits for recombination. In achieving this goal, a statistical model was first used to study the dynamics of directed evolution as a search algorithm. These simulations improved our understanding of the relationship between parameters describing the search space (e.g., interactions between amino acids) and experimental search parameters (e.g., mutation rate and library size). Based on these simulations, a more detailed model was used to calculate the structural tolerance of each residue to amino acid substitutions. Further, a computational model was developed to optimize recombination experiments, based on the three-dimensional structure. Together, these computational techniques represent a major step towards information-driven combinatorial protein design.
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