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Related Concept Videos

Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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Turnover Number and Catalytic Efficiency

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Related Experiment Video

Updated: May 16, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

ECOH: an enzyme commission number predictor using mutual information and a support vector machine.

Yoshihiko Matsuta1, Masahiro Ito, Yukako Tohsato

  • 1Department of Bioinformatics, College of Life Sciences, Ritsumeikan University, Shiga, Kusatsu 525-8577, Japan.

Bioinformatics (Oxford, England)
|December 11, 2012
PubMed
Summary

A new method, Enzyme COmmission numbers Handler (ECOH), predicts enzyme commission (EC) numbers for unclassified reactions by analyzing substrate and product structures. This tool achieves high accuracy, aiding in enzyme function prediction.

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Enzymology

Background:

  • The enzyme commission (EC) number system classifies enzymatic reactions, but many lack official numbers due to insufficient assay data.
  • Predicting EC numbers for unclassified reactions is crucial for understanding enzyme function and metabolic pathways.

Purpose of the Study:

  • To develop a computational method for assigning EC numbers to enzymatic reactions lacking official classification.
  • To leverage structural similarities between substrates and products of classified and unclassified reactions.

Main Methods:

  • A novel method, Enzyme COmmission numbers Handler (ECOH), was developed.
  • ECOH utilizes a maximum common substructure algorithm, mutual information, and a support vector machine.
  • The method analyzes structural similarities of substrates and products to predict EC numbers.

Main Results:

  • ECOH achieved high prediction accuracy for the first three EC number digits: 86.1% sensitivity, 87.4% precision, and 99.8% accuracy.
  • The method successfully predicted classifications for 85 enzymatic reactions with multiple EC sub-subclasses.
  • ECOH demonstrated superior performance and flexibility compared to existing methods.

Conclusions:

  • ECOH is an effective tool for predicting EC numbers for unclassified enzymatic reactions.
  • The method's accuracy and flexibility make it valuable for predicting enzyme function and annotating biochemical pathways.