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Precise redesign for improving enzyme robustness based on coevolutionary analysis and multidimensional virtual

Jie Luo1, Chenshuo Song1, Wenjing Cui1

  • 1Key Laboratory of Industrial Biotechnology (Ministry of Education), School of Biotechnology, Jiangnan University Wuxi Jiangsu 214122 China zhmzhou@jiangnan.edu.cn hanlaichuang@jiangnan.edu.cn.

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A new strategy combining coevolutionary analysis and multidimensional virtual screening (Co-MdVS) precisely improves enzyme robustness. This method efficiently identified a nattokinase mutant with a 31-fold longer half-life at 55°C and enhanced stability.

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Area of Science:

  • Biotechnology
  • Enzyme Engineering
  • Computational Biology

Background:

  • Natural enzymes function optimally under physiological conditions but lack industrial robustness.
  • Current mutation strategies are limited by residue accessibility and accuracy.
  • Improving enzyme stability and performance is crucial for industrial applications.

Purpose of the Study:

  • To develop a precise enzyme design strategy for enhanced robustness.
  • To utilize coevolutionary analysis and multidimensional virtual screening for enzyme engineering.
  • To improve the thermostability and acid resistance of enzymes like nattokinase.

Main Methods:

  • Coevolutionary analysis and multidimensional virtual screening (Co-MdVS) strategy.
  • Screening of a large virtual mutation library for nattokinase.
  • Iterative combination of mutations and molecular dynamics simulations.
  • Validation of the strategy on l-rhamnose isomerase and PETase.

Main Results:

  • Identified 8 dual mutants with enhanced thermostability from 7980 virtual mutants.
  • Developed an optimal mutant (M6) with a 31-fold increased half-life at 55°C.
  • M6 demonstrated significantly enhanced acid resistance and improved catalytic efficiency.
  • Molecular dynamics revealed reduced flexibility in key regions contributed to M6's robustness.

Conclusions:

  • The Co-MdVS strategy enables precise design for improved enzyme robustness.
  • This approach offers a viable method for engineering enzymes for industrial demands.
  • Validated success on multiple enzyme models including nattokinase, l-rhamnose isomerase, and PETase.