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Related Experiment Video

Updated: Aug 27, 2025

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

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Stabilizing proteins, simplified: A Rosetta-based webtool for predicting favorable mutations.

David F Thieker1, Jack B Maguire1, Stephan T Kudlacek1

  • 1Department of Biochemistry and Biophysics, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.

Protein Science : a Publication of the Protein Society
|September 29, 2022
PubMed
Summary

New web tools predict protein stabilizing mutations using protein structure and Rosetta software. These tools enhance protein stability and expression yields, enabling research previously limited by protein engineering challenges.

Keywords:
Rosetta molecular modeling programmolecular modelingprotein designprotein engineeringprotein stability

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Area of Science:

  • Protein Engineering
  • Computational Biology
  • Structural Biology

Background:

  • Low protein thermodynamic stability often results in poor expression yields and limited functionality.
  • This instability poses significant challenges in research, industrial, and clinical applications.
  • Overcoming these limitations is crucial for advancing various scientific and technological fields.

Purpose of the Study:

  • To introduce two novel web-based tools for predicting protein stabilizing mutations.
  • To leverage high-resolution protein structures and the Rosetta molecular modeling program for mutation prediction.
  • To simplify protein engineering and enable research on unstable proteins.

Main Methods:

  • Utilized high-resolution protein structures and the Rosetta molecular modeling program.
  • Developed two distinct web-based protocols for predicting stabilizing mutations.
  • Applied the protocols to three genetically and structurally diverse proteins.

Main Results:

  • Successfully predicted mutations that significantly improved thermal stability and/or protein yield.
  • Observed an increase in protein unfolding temperatures by over 20°C when combining predicted mutations.
  • Demonstrated the utility of the tools even without extensive multiple sequence alignments.

Conclusions:

  • The developed web tools effectively predict stabilizing mutations for proteins.
  • These tools enhance protein engineering capabilities, particularly for proteins with limited sequence data.
  • The protocols facilitate research and applications previously hindered by poor protein expression and stability.