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

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.