pepsickle

pepsickle predicts proteasomal cleavage sites to support antigen processing analyses and neoepitope identification for immunological research.

Key Features:

• Enhanced Prediction Performance: Achieves higher area under the curve (AUC) for predicting in vivo cleavage sites compared to existing models, improving proteasomal cleavage prediction accuracy.
• Computational Speed: Optimized for computational speed to enable large-scale analyses.
• Model Flexibility: Provides multiple prediction profiles trained on in-vivo epitope data (default), in-vitro constitutive proteasome data, and in-vitro immunoproteasome data.
• Input Versatility: Accepts direct amino acid sequences or FASTA files as input.
• Post Hoc Filtering for Neoepitopes: Performs post hoc filtering of predicted patient neoepitopes to enrich immune-responsive epitopes for vaccine development.

Scientific Applications:

• Antigen Processing and Presentation: Predicts proteasomal cleavage sites to study antigen processing by the immune system.
• Epitope Prediction: Improves identification of immune-responsive epitopes via post hoc filtering of predicted neoepitopes.
• Protein Turnover Studies: Analyzes protein degradation pathways to investigate cellular protein turnover.

Methodology:

Uses algorithms trained on in-vivo epitope data, in-vitro constitutive proteasome data, and in-vitro immunoproteasome data.