PoPS

PoPS models and predicts protease specificity by constructing computational specificity models that integrate amino acid sequence data, experimental measurements, expert knowledge, and substrate structural information to identify and rank protease cleavage sites.

Key Features:

• Novel modeling methodology: Constructs specificity models from diverse data inputs including amino acid sequences, experimental data, and expert knowledge.
• Prediction and ranking: Predicts and ranks potential protease cleavage sites within single substrates and across entire proteomes.
• Structural considerations: Incorporates secondary and tertiary structural information of substrates to screen out unlikely cleavage sites.
• Model inference and comparison: Infers new specificity models, compares existing models, and tests their predictive power.
• Public database integration: Stores and retrieves specificity models in a publicly accessible database.

Scientific Applications:

• Proteomics research: Predicts cleavage sites across proteomes to map proteolytic networks and protein turnover.
• Drug discovery: Identifies protease targets and cleavage preferences to inform development of protease inhibitors.
• Biological process regulation: Elucidates protease roles in processes such as apoptosis, signal transduction, and immune responses by defining specificity.

Methodology:

Computational construction of specificity models from amino acid sequences, experimental data, and expert knowledge; incorporation of secondary and tertiary substrate structure to filter unlikely sites; prediction and ranking of cleavage sites within substrates and across proteomes; inference, comparison, and testing of model predictive power; storage and retrieval of specificity models in a public database.