PASTA

PASTA predicts the stability of putative cross-beta pairings and aggregation propensity in protein sequences to identify amyloidogenic regions relevant to aggregation-related diseases.

Key Features:

• Predictive Capabilities: Evaluates the stability of putative cross-beta pairings between different sequence stretches using a refined energy function re-derived from an expanded dataset of globular protein domains.
• Enhanced Accuracy: Algorithm benchmarked on comprehensive peptide and protein test sets, delivering improved specificity and high accuracy in detecting amyloid-forming regions.
• Comprehensive Analysis: Provides additional predictions of intrinsic disorder and secondary structure to characterize protein features that may influence aggregation behavior.
• Pathogenic Insights: Explores how pathogenic mutations enhance aggregation propensity, identifies aggregation hot spots that stabilize pathological interactions, and assesses cross-amyloid interactions between co-aggregating proteins.

Scientific Applications:

• Neurodegenerative disease research: Applied to study aggregation mechanisms in Alzheimer's disease, Parkinson's disease, and prion diseases.
• Therapeutic target identification: Predicts aggregation propensity and related features to identify potential therapeutic targets.
• Molecular mechanism investigation: Supports understanding of molecular mechanisms driving disease progression through region-level aggregation predictions.
• Cross-amyloid interaction analysis: Enables investigation of cross-amyloid interactions and co-aggregation between proteins.

Methodology:

Assesses cross-beta structure stability using a refined energy function re-derived from an expanded dataset of globular protein domains and was benchmarked on peptide and protein test sets.