DeepCalpain

DeepCalpain predicts calpain cleavage sites using deep neural network algorithms and particle swarm optimization to identify enzyme-specific (m-calpain and μ-calpain) protein cleavage events for studying calpain-mediated regulation in cellular processes and cancer.

Key Features:

• Site prediction: Predicts site-specific calpain-mediated protein cleavage events.
• Algorithms: Integrates deep neural network algorithms and particle swarm optimization.
• Enzyme specificity: Focuses on enzyme-specific cleavage activities of m-calpain and μ-calpain.
• Protein interaction analysis: Incorporates analysis of protein interactions to enrich understanding of calpain–substrate regulatory relationships.
• Mutation analysis: Analyzes mutations associated with calpain cleavage across 11 cancer types and assesses their effects on cleavage events.
• Performance: Demonstrates superior performance over existing tools in predicting calpain cleavage sites.
• Biological context: Links predicted calpain cleavage sites to cellular processes including proliferation, differentiation, cytoskeletal reorganization, and apoptosis.

Scientific Applications:

• Molecular mechanism elucidation: Enables investigation of molecular mechanisms and regulatory roles of calpain-mediated cleavage.
• Cancer research: Supports analysis of calpain involvement in tumor development and progression.
• Mutational impact studies: Facilitates assessment of how cancer-associated mutations alter calpain-mediated cleavage events across 11 cancer types.
• Translational research: Provides data linking cleavage sites, protein interactions, and mutational impacts to inform cancer diagnosis and therapeutic research.

Methodology:

Integrates deep neural network algorithms with particle swarm optimization to predict calpain (m-calpain and μ-calpain) cleavage sites and incorporates protein interaction analysis and mutation analysis across 11 cancer types.