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Optimal sequence selection in proteins of known structure by simulated evolution

H W Hellinga1, F M Richards

  • 1Department of Biochemistry, Duke University Medical Center, Durham, NC 27710.

Proceedings of the National Academy of Sciences of the United States of America
|June 21, 1994
PubMed
Summary

This study introduces protein simulated evolution, a computational method for designing protein sequences and structures. It optimizes protein function by simulating natural selection to identify optimal sequences and configurations.

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Area of Science:

  • Computational biology
  • Protein engineering
  • Biophysics

Background:

  • Designing proteins with specific functions requires optimizing amino acid sequences within a fixed backbone.
  • Simultaneously optimizing sequences and 3D structures is a complex challenge in rational protein design.

Purpose of the Study:

  • To present a novel computational method for simultaneously optimizing protein sequences and their 3D atomic configurations.
  • To enable the rational design of proteins with desired functions.

Main Methods:

  • Developed a 'protein simulated evolution' method using simulated annealing.
  • Minimizes a semiempirical potential function across sequence and conformational spaces.
  • Generates sequences via mutations and optimizes side-chain conformations on a fixed backbone.

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Main Results:

  • Predicted energies for designed sequences in the phage lambda cI repressor hydrophobic core correlated with experimental activities.
  • Demonstrated the feasibility of optimizing both sequence and structure computationally.
  • Validated the approach through correlation with biological activity.

Conclusions:

  • Protein simulated evolution offers a powerful 'genetic selection by computer' approach.
  • This method has significant potential for protein engineering and structure-based drug discovery.
  • Enables efficient exploration of sequence-structure-function relationships.