AGGRESCAN and its evolution: A two-decade perspective on protein aggregation prediction.

Area of Science:

• Biochemistry and Molecular Biology
• Biotechnology
• Biomedical Research

Background:

• Protein aggregation is implicated in neurodegenerative diseases and affects therapeutic protein production.
• Aberrant protein self-assembly poses significant challenges in biological and biotechnological applications.
• Predicting aggregation-prone regions (APRs) is crucial for understanding and mitigating these issues.

Purpose of the Study:

• To provide a comprehensive overview of the Aggrescan platform's evolution and applications.
• To detail the development of tools for predicting protein aggregation.
• To highlight the impact of Aggrescan in various scientific fields.

Main Methods:

• Review of the Aggrescan platform, including AGGRESCAN, Aggrescan3D, and Aggrescan4D.
• Examination of algorithmic foundations and empirical validation of prediction tools.
• Integration of AlphaFold models for proteome-scale aggregation analysis.

Main Results:

• The Aggrescan platform has evolved from sequence-based predictions to incorporate 3D structures and environmental factors.
• Aggrescan tools have been validated empirically and applied across diverse research areas.
• Integration with AlphaFold enables large-scale proteome analysis of protein aggregation determinants.

Conclusions:

• The Aggrescan family of tools has become a reliable and accessible resource for studying protein aggregation.
• Aggrescan facilitates the understanding and redesign of protein aggregation processes.
• Continued evolution of Aggrescan supports advancements in biotechnology and biomedical research.