iCataly-PseAAC

iCataly-PseAAC predicts catalytic sites in enzymes from amino acid sequences to identify residues directly involved in chemical catalysis.

Key Features:

• Sequence Evolution Integration: Incorporates sequence evolution information via the grey system model GM(2,1) to inform feature representation.
• Pseudo Amino Acid Composition (PseAAC): Employs PseAAC to extract sequence-derived features that represent protein characteristics.
• Sequence-Only Prediction: Operates using only amino acid sequence information without requiring structural data.
• Predictive Performance Evaluation: Performance was validated using jackknife tests and independent dataset evaluations.

Scientific Applications:

• Enzyme Function Analysis: Identifies catalytic residues to inform studies of enzyme mechanisms.
• Drug Development: Locates catalytic sites to support inhibitor or activator design for therapeutic discovery.

Methodology:

Features are generated via pseudo amino acid composition (PseAAC) that incorporates sequence evolution information processed by the grey system model GM(2,1); predictive performance was assessed using jackknife tests and independent dataset evaluations.