CSmetaPred

CSmetaPred predicts and ranks catalytic residues in enzymes by aggregating and normalizing residue-level scores from multiple catalytic residue predictors to improve identification of mechanistic residues for experimental characterization.

Key Features:

• Meta-approach: Integrates residue-level scores from four well-established catalytic residue predictors to produce consensus rankings.
• Score normalization and aggregation: Normalizes residue scores and computes the mean of normalized scores to rank putative catalytic residues.
• CSmetaPred_poc pocket integration: Combines mean residue scores with predicted pocket information to refine prediction accuracy and ranking.
• Evaluation metrics: Assesses performance using Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves and reports Mean Average Specificity (MAS).
• Benchmark datasets: Evaluated on the CSAMAC dataset and three legacy datasets for comparative performance assessment.
• Performance statistics: Achieves a MAS of 0.97 on the CSAMAC dataset and an accuracy of 0.94 when residues ranked ≤20 are treated as true positives.
• Ranking performance: Produces lower median predicted ranks than constituent methods and predicts all catalytic residues within the top 20 for approximately 73% of enzymes.
• Structure-aware prediction: Comparative modelled structures yield better predictions than sequence-based approaches alone.

Scientific Applications:

• Prioritization for mutational studies: Provides ranked lists of putative catalytic residues to prioritize site-directed mutagenesis experiments.
• Elucidation of enzyme mechanisms: Supports identification of mechanistic residues for studying enzymatic catalysis.
• Benchmarking of predictors: Enables comparative assessment of catalytic residue predictors using consensus rankings and ROC/PR analyses.
• Structure-informed prediction: Applies to comparative modelled structures to incorporate pocket information into catalytic residue prediction.

Methodology:

Integrates residue-level scores from four catalytic residue predictors, normalizes scores and computes the mean of normalized residue scores, and for CSmetaPred_poc combines these mean scores with predicted pocket information; performance is assessed using ROC and Precision-Recall curves and metrics including Mean Average Specificity (MAS), median predicted rank, and a binary classification treating residues ranked ≤20 as true positives.