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Protein sequence design by conformational landscape optimization.

Christoffer Norn1,2, Basile I M Wicky1,2, David Juergens1,2,3

  • 1Department of Biochemistry, University of Washington, Seattle, WA 98105.

Proceedings of the National Academy of Sciences of the United States of America
|March 13, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for protein design, optimizing amino acid sequences for desired structures by considering the full conformational landscape. This approach, using transform-restrained Rosetta (trRosetta), improves protein folding prediction and stability compared to traditional energy-based methods.

Keywords:
energy landscapemachine learningprotein designsequence optimizationstability prediction

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Area of Science:

  • Computational biology
  • Protein engineering
  • Biophysics

Background:

  • The protein design challenge involves finding amino acid sequences that fold into specific three-dimensional structures.
  • Current methods often focus on finding the lowest energy sequence for a target structure, with structure prediction as a secondary check, leading to high failure rates.
  • Anfinsen's hypothesis posits that a protein's amino acid sequence determines its lowest energy folded state.

Purpose of the Study:

  • To develop a novel computational method for protein design that directly optimizes sequences across all possible structures.
  • To improve the accuracy of predicting folding and stability for de novo designed proteins.
  • To compare the efficacy of conformational landscape optimization with traditional energy-based design methods.

Main Methods:

  • Utilizing gradient backpropagation through the transform-restrained Rosetta (trRosetta) structure prediction network.
  • Directly optimizing amino acid sequences by considering the full conformational landscape in a single calculation.
  • Comparing sequence design outcomes from conformational landscape optimization with Rosetta's energy-based single-point estimations.

Main Results:

  • The trRosetta method, by considering the full conformational landscape, demonstrates superior performance in predicting folding and stability of designed proteins compared to standard Rosetta energy calculations.
  • Conformational landscape optimization results in energy landscapes with fewer alternative energy minima than traditional energy-based design.
  • Combining low-resolution trRosetta for state disfavoring and high-resolution Rosetta for deep energy minima design yields more funneled energy landscapes.

Conclusions:

  • Directly optimizing protein sequences over the conformational landscape using trRosetta offers a more effective approach to de novo protein design.
  • This integrated approach enhances the predictability of protein folding and stability.
  • The hybrid strategy combining trRosetta and Rosetta models optimizes energy landscapes for robust protein design.