DeEPn

DeEPn annotates enzyme sequences across all seven Enzyme Commission (EC) classes (EC1–EC7) using a deep neural network to provide precise functional classification.

Key Features:

• Comprehensive Classification: Provides functional annotation across all seven EC classes (EC1 to EC7), including EC7.
• Deep Neural Network Architecture: Employs deep learning neural network models to capture complex sequence-function patterns for enzyme classification.
• Performance Superiority: Demonstrated higher predictive quality in comparative analyses against existing methods such as ECPred and SVM-Prot.

Scientific Applications:

• Functional Genomics: Maps enzyme sequences to EC classes to support pathway and functional annotation studies.
• Metabolic Engineering: Enables identification and classification of enzymes for pathway design and optimization.
• Drug Discovery: Facilitates identification of enzyme functions relevant to target discovery and therapeutic strategies.

Methodology:

Implements a deep neural network architecture for classification of enzymes across EC1–EC7 and evaluates predictive performance via comparative analyses against ECPred and SVM-Prot.