Solubis: a webserver to reduce protein aggregation through mutation.

Area of Science:

• Biochemistry and Molecular Biology
• Protein Engineering
• Computational Biology

Background:

• Protein aggregation limits therapeutic and biotechnological applications of proteins like enzymes and antibodies.
• Understanding aggregation principles enables rational protein redesign for improved expression and solubility.
• Aggregation-prone regions (APRs) are critical for protein stability and function, requiring careful mutation selection.

Purpose of the Study:

• To provide an automated pipeline for identifying mutations that decrease protein aggregation.
• To ensure selected mutations do not compromise protein structure or thermodynamic stability.
• To guide protein engineering efforts by highlighting critical APRs needing improvement.

Main Methods:

• Utilizes the TANGO algorithm to predict and reduce intrinsic protein aggregation propensity.
• Employs the FoldX empirical force-field to assess and maintain native protein structure stability.
• Develops a scoring system that combines aggregation propensity with local stability for APRs.

Main Results:

• An automated pipeline is available at http://solubis.switchlab.org/ for identifying aggregation-reducing mutations.
• The pipeline integrates sequence aggregation propensity with structural stability predictions.
• A plot visualizes corrected aggregation propensity scores for APRs, aiding prioritization in protein engineering.

Conclusions:

• Rational protein engineering can mitigate aggregation issues, enhancing protein applications.
• The developed computational pipeline offers a valuable tool for designing more soluble and stable proteins.
• This approach facilitates the optimization of proteins for biotechnological and therapeutic uses by addressing aggregation challenges.