Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

EHPred: an SVM-based method for epoxide hydrolases recognition and classification.

Jia Jia1, Liang Yang, Zi-Zhang Zhang

  • 1James. D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou 310008, China.

Journal of Zhejiang University. Science. B
|December 21, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Clinical performance of 0/1 h cardiac troponin algorithm for diagnosing non-STEMI in an emergency setting.

The American journal of emergency medicine·2023
Same author

Microbial diversity and functions in saline soils: A review from a biogeochemical perspective.

Journal of advanced research·2023
Same author

Additive effect between homocysteine and low-density-lipoprotein cholesterol upon incidence of novel carotid plaque formation: data from a Chinese community-based cohort.

BMC cardiovascular disorders·2023
Same author

Levodopa Carbidopa Intestinal Gel in Advanced Parkinson's Disease: DUOGLOBE Final 3-Year Results.

Journal of Parkinson's disease·2023
Same author

Polyketides with potential bioactivities from the mangrove-derived fungus

Marine life science & technology·2023
Same author

Anti-Diabetic Drugs Inhibit Bulimia Induced Obesity.

Frontiers in bioscience (Landmark edition)·2023
Same journal

Halotolerant plant growth-promoting bacteria <i>Enterobacter</i> sp. Av16 and <i>Acinetobacter</i> sp. Av23 enhance seed germination and seedling photosynthesis of <i>Apocynum pictum</i> under salt stress.

Journal of Zhejiang University. Science. B·2026
Same journal

Integrative analytical and statistical framework for optimization of multiplex qPCR detection of <i>TGFBI</i> mutations in refractive surgery candidates.

Journal of Zhejiang University. Science. B·2026
Same journal

Regenerative potential of Schneiderian membrane-derived mesenchymal stem cells in sinus floor elevation model and calvarial defect model.

Journal of Zhejiang University. Science. B·2026
Same journal

Esculetin attenuates migraine-like pain via CGRP suppression and meningeal mast cell modulation in rat models.

Journal of Zhejiang University. Science. B·2026
Same journal

Cell proliferation and differentiation during epidermis renewal.

Journal of Zhejiang University. Science. B·2026
Same journal

HENMT1: an RNA methyltransferase in biology and disease.

Journal of Zhejiang University. Science. B·2026
See all related articles

This study introduces a two-layer machine learning method to identify epoxide hydrolases (EHs) and their subfamilies from protein sequences. The developed EHPred tool accurately classifies these enzymes, aiding in biological research.

Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Biology

Background:

  • Epoxide hydrolases (EHs) are crucial enzymes involved in various biological processes.
  • Accurate identification and classification of EHs and their subfamilies are essential for understanding their functions.
  • Existing methods may lack the precision required for large-scale genomic or proteomic analyses.

Purpose of the Study:

  • To develop a computational method for distinguishing epoxide hydrolases (EHs) from other enzymes.
  • To classify different subfamilies of EHs based on their primary protein sequences.
  • To create a user-friendly tool for EH identification and subfamily classification.

Main Methods:

  • A two-layer Support Vector Machine (SVM) classification system was designed.

Related Experiment Videos

  • Feature vectors including amino acid composition (AAC), dipeptide composition (DPC), and pseudo-amino acid composition (PAAC) were extracted from protein sequences.
  • The method was validated using k-fold cross-validation tests.

    • Main Results:

    • The first-layer SVM achieved 94.2% accuracy and a Matthew's Correlation Coefficient (MCC) of 0.84 in differentiating EHs from non-EHs.
    • The second-layer SVM, utilizing PAAC, demonstrated superior performance in classifying EH subfamilies with 90.7% accuracy and an MCC of 0.87.
    • PAAC outperformed AAC (80.0% accuracy) and DPC (84.9% accuracy) for subfamily classification.

    Conclusions:

    • The developed two-layer SVM method effectively identifies EHs and classifies their subfamilies using primary protein sequences.
    • The EHPred program provides an accurate and efficient tool for researchers to recognize and classify EHs.
    • This computational approach enhances the study of enzyme function and evolution through precise sequence-based analysis.