Prediction of aggregation-prone regions in structured proteins

Area of Science:

• Biochemistry
• Molecular Biology
• Computational Biology

Background:

• Protein aggregation and amyloid formation are implicated in various diseases.
• Predicting these processes is challenging, especially under physiological conditions involving molecular folding.

Purpose of the Study:

• To develop a novel computational method for identifying critical regions in peptide and protein sequences prone to aggregation.
• To account for the influence of molecular folding and persistent structure on aggregation propensity.

Main Methods:

• The method utilizes amino acid sequence information to predict both folding and aggregation propensities simultaneously.
• It models the competition between these two opposing tendencies.
• The approach was applied to disease-associated and non-associated peptides and proteins.

Main Results:

• Robust identification of protein regions with high intrinsic aggregation propensity is achievable.
• The structural context of these aggregation-prone regions in their monomeric form significantly influences their role in aggregation.
• The interplay between folding and aggregation propensities can be effectively modeled.

Conclusions:

• The developed method provides a powerful tool for understanding the molecular basis of protein aggregation.
• Considering structural context alongside sequence-based propensities is vital for accurate prediction of amyloid formation.
• This approach has implications for the study of neurodegenerative diseases and protein misfolding disorders.