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Updated: Jul 18, 2025

Directed Evolution Method in Saccharomyces cerevisiae: Mutant Library Creation and Screening
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EnzyHTP Computational Directed Evolution with Adaptive Resource Allocation.

Qianzhen Shao1, Yaoyukun Jiang1, Zhongyue J Yang1,2,3,4,5

  • 1Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.

Journal of Chemical Information and Modeling
|August 23, 2023
PubMed
Summary
This summary is machine-generated.

We developed a computational directed evolution protocol using adaptive resource allocation to improve enzyme engineering efficiency. This method significantly reduces computational costs and successfully identifies beneficial enzyme variants.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Enzyme Engineering

Background:

  • Directed evolution is crucial for enzyme engineering but relies on efficient screening.
  • Developing
  • smart libraries
  • to identify beneficial enzyme variants remains a challenge.

Purpose of the Study:

  • To develop a novel computational directed evolution protocol.
  • To enhance screening efficiency through adaptive resource allocation.
  • To validate the protocol's effectiveness in identifying beneficial enzyme variants.

Main Methods:

  • Implemented an adaptive resource allocation strategy using a Python library for enzyme modeling.
  • Utilized EnzyHTP software for automated enzyme modeling.
  • Tested the protocol with fluoroacetate dehalogenase and Kemp eliminase (KE07).
  • Employed molecular dynamics (MD) and quantum mechanics (QM) calculations for variant analysis.

Main Results:

  • Adaptive resource allocation saved 87% CPU hours and 14% GPU hours compared to fixed allocation.
  • The protocol successfully identified all four experimentally observed target variants for Kemp eliminase (KE07).
  • Computational tasks, including 18.4 μs MD and 18,400 QM calculations, were completed in 3 days.

Conclusions:

  • The developed computational directed evolution protocol with adaptive resource allocation enhances efficiency and reduces costs.
  • This approach effectively identifies beneficial enzyme variants, aiding enzyme engineering efforts.
  • The protocol demonstrates a significant advancement in computational enzyme design.