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Updated: Jan 1, 2026

Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System

Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System

Published on: August 8, 2016

EFICAz: a comprehensive approach for accurate genome-scale enzyme function inference.

Weidong Tian¹, Adrian K Arakaki, Jeffrey Skolnick

¹Center of Excellence in Bioinformatics, University at Buffalo, The State University of New York,901 Washington Street, Buffalo, NY 14203-1199, USA.

Nucleic Acids Research

|December 4, 2004

Summary

EFICAz accurately infers enzyme function using four combined methods, identifying conserved residues and patterns. This approach enhances enzymatic function prediction accuracy, even for novel sequences.

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Published on: November 3, 2011

Area of Science:

Bioinformatics
Computational Biology
Enzymology

Background:

Accurate enzyme function inference is crucial for understanding biological systems and developing biotechnologies.
Existing methods often lack the accuracy or scalability required for large-scale genomic analysis.

Purpose of the Study:

To develop and validate EFICAz (Enzyme Function Inference by Combined Approach), an automated engine for high-accuracy, large-scale enzyme function inference.
To improve the prediction of Enzyme Commission (EC) numbers, particularly the four-digit classification.

Main Methods:

EFICAz integrates four prediction methods: functionally discriminating residues (FDRs) via CHIEFc, pairwise sequence comparison with a family-specific threshold, FDRs in multiple Pfam families, and Prosite patterns.
Developed the Evolutionary Footprinting method to identify FDRs using evolutionary information from multiple sequence alignments.
Validated EFICAz using a jackknife test on sequences with <40% identity to training sets.

Main Results:

EFICAz achieved high prediction accuracy (92%) and sensitivity (82%) for four-digit EC number prediction.
Identified functionally discriminating residues (FDRs) showed a significant correlation with annotated active site residues.
Application to the *Escherichia coli* genome provided more detailed enzymatic function assignments than KEGG and generated novel predictions.

Conclusions:

EFICAz is a robust and accurate tool for automated, large-scale enzyme function inference.
The integration of multiple prediction strategies and the identification of FDRs contribute to its high performance.
EFICAz has the potential to significantly advance our understanding of enzymatic functions across diverse genomes.