Strategies for enhancing protein solubility: methods, applications, and prospects

Area of Science:

• Protein engineering
• Biochemistry
• Food science
• Pharmaceutical science

Background:

• Protein solubility is crucial for food and pharmaceutical applications.
• Current methods for controlling solubility are often empirical and challenging.
• A systematic approach is needed to improve protein design for industrial use.

Purpose of the Study:

• To review the molecular and environmental factors influencing protein solubility.
• To integrate traditional modification strategies with artificial intelligence (AI)-driven approaches.
• To provide a framework for data-driven rational design of proteins.

Main Methods:

• Literature review of protein solubility determinants.
• Analysis of traditional protein modification techniques.
• Exploration of emerging AI-driven strategies in protein engineering.

Main Results:

• Identified key molecular and environmental determinants of protein solubility.
• Highlighted the shift from empirical methods to rational, data-driven protein design.
• Demonstrated the potential of AI in optimizing protein engineering.

Conclusions:

• A systematic framework for protein engineering can advance solutions across industries.
• Integrating AI with traditional methods offers a powerful approach to control protein solubility.
• Data-driven design is essential for future protein applications in food and pharmaceuticals.