Aggregation in protein-based biotherapeutics: computational studies and tools to identify aggregation-prone regions

Area of Science:

• Biopharmaceutical development
• Protein chemistry
• Computational biology

Background:

• Biotherapeutics' complex structures make them susceptible to physicochemical stresses during processing.
• Protein aggregate formation is a significant concern, potentially leading to immunogenic reactions.
• Developing biotherapeutics with low aggregation propensity is crucial for improved drug developability.

Purpose of the Study:

• To comprehensively review computational studies on sequence and structural factors influencing protein and peptide aggregation.
• To identify computational tools for predicting aggregation in therapeutic proteins.
• To provide insights for the rational design of stable and soluble biotherapeutic candidates.

Main Methods:

• Review of computational approaches including coarse-grain models, atomistic molecular simulations, and bioinformatics.
• Analysis of studies focusing on aggregation mechanisms and identification of aggregation-prone motifs.
• Survey of available computational tools for aggregation prediction.

Main Results:

• Computational studies have elucidated key sequence and structural determinants of protein aggregation.
• Various computational methods offer insights into aggregation mechanisms and prediction.
• Identified aggregation-prone motifs can guide the design of more stable biotherapeutics.

Conclusions:

• Computational approaches provide valuable insights into protein aggregation.
• Understanding sequence-structure relationships is key to designing biotherapeutics with enhanced stability and solubility.
• These insights are applicable to both novel and follow-on biotherapeutic development.