Aggrescan4D: A comprehensive tool for pH-dependent analysis and engineering of protein aggregation propensity

Area of Science:

• Biochemistry
• Computational Biology
• Protein Science

Background:

• Protein aggregation is a critical factor in many diseases and impacts protein stability.
• Predicting and managing protein aggregation is essential for therapeutic development and biotechnology.
• Existing tools like Aggrescan3D (A3D) provided a foundation, but lacked advanced pH-dependent and structural flexibility considerations.

Purpose of the Study:

• To review the enhanced functionalities of Aggrescan4D (A4D), an updated computational tool for protein aggregation prediction.
• To elucidate the principles of A4D's pH-dependent calculations and its integration with protein engineering.
• To evaluate A4D's performance using an antibody case study and compare it with other structure-based predictors.

Main Methods:

• Utilizing structural information and pH influence for aggregation propensity prediction.
• Implementing advanced protein engineering protocols within pH-dependent calculations.
• Incorporating structural flexibility analysis into aggregation propensity assessments.
• Leveraging a comprehensive set of precalculated predictions.

Main Results:

• A4D demonstrates enhanced capabilities in predicting protein aggregation compared to previous versions.
• The tool effectively integrates pH-dependent calculations and structural flexibility for improved accuracy.
• Case study analysis validates A4D's utility in advising solubility-enhancing mutations, particularly for antibodies.

Conclusions:

• Aggrescan4D (A4D) offers a significant advancement in predicting protein aggregation, incorporating crucial pH and structural flexibility factors.
• The tool provides valuable insights for protein engineering strategies aimed at enhancing protein solubility.
• A4D opens new research avenues for understanding and managing protein aggregation, accessible via a web server.