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An evolution-based model for designing chorismate mutase enzymes.

William P Russ1, Matteo Figliuzzi2, Christian Stocker3

  • 1University of Texas Southwestern Medical Center, Dallas, TX, USA.

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|July 25, 2020
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Summary
This summary is machine-generated.

Scientists developed a method to design novel proteins using evolutionary data. This approach enables the creation of enzymes with natural-like functions and vast sequence diversity, paving the way for artificial protein design.

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Area of Science:

  • Biochemistry
  • Protein Engineering
  • Computational Biology

Background:

  • Enzyme rational design is crucial for fundamental research and practical applications.
  • Existing methods often struggle with the complexity of protein sequence-function relationships.

Purpose of the Study:

  • To develop a general process for evolution-based design of artificial proteins.
  • To learn protein specification constraints directly from evolutionary sequence data.

Main Methods:

  • Learning protein sequence constraints from evolutionary data.
  • Designing and synthesizing libraries of synthetic genes.
  • Testing gene libraries in vivo using quantitative complementation assays.
  • Applying the process to chorismate mutase, an enzyme in aromatic amino acid biosynthesis.

Main Results:

  • Demonstrated the design of natural-like catalytic function with substantial sequence diversity for chorismate mutase.
  • Showed that sequence-based statistical models can specify functional proteins.
  • Optimized the generative model for function within a specific genomic context.
  • Confirmed access to an enormous space of functional protein sequences.

Conclusions:

  • Evolutionary sequence data is sufficient to specify proteins.
  • The developed process provides a foundation for general artificial protein design.
  • This work opens new avenues for engineering proteins with desired functions.