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

Mining viral protease data to extract cleavage knowledge.

Ajit Narayanan1, Xikun Wu, Z Rong Yang

  • 1School of Engineering and Computer Sciences (Bioinformatics Laboratory), Old Library, University of Exeter, Exeter EX4 4PT, UK. a.narayanan@ex.ac.uk

Bioinformatics (Oxford, England)
|August 10, 2002
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

Longitudinal, Multi-Cycle Evaluation of Passive Function Improvement in People with Arm Spasticity Treated with Botulinum Toxin A.

Toxins·2026
Same author

A symptom network approach to schizophrenia in the CATIE study: processing speed as the central cognitive impairment.

BJPsych open·2026
Same author

The NEuroCOUGH Chronic Cough Registry: a protocol for a pan-European observational study.

ERJ open research·2025
Same author

The Protective Effect of Omeprazole on Vancomycin Cytotoxicity in HK-2 Cells and Renal Injury in Rats.

BioMed research international·2025
Same author

A Proof-of-Concept Development on Speech Analysis for Concussion Detection.

Studies in health technology and informatics·2025
Same author

Therapeutic Drug Monitoring of Voriconazole: Unbound Concentration With Clinical Efficacy and Adverse Events.

The Annals of pharmacotherapy·2025
Same journal

OmicsTransformer: Self-Supervised Masked Consistency and Uncertainty-Aware Fusion for Robust Multi-Omics Prediction.

Bioinformatics (Oxford, England)·2026
Same journal

Computational Tool Choice Impacts CRISPR Spacer-Proto spacer Detection.

Bioinformatics (Oxford, England)·2026
Same journal

ARISE: RNA-Anchored Shared-Edge Topology and Hierarchical Fusion for Spatial Multi-Omics Integration.

Bioinformatics (Oxford, England)·2026
Same journal

Interactive exploration of biobank-scale ancestral recombination graphs with Lorax.

Bioinformatics (Oxford, England)·2026
Same journal

PepMCP: A Graph-Based Membrane Contact Probability Predictor for Membrane-Lytic Antimicrobial Peptides.

Bioinformatics (Oxford, England)·2026
Same journal

ARGscape: A modular, interactive tool for manipulation of spatiotemporal ancestral recombination graphs.

Bioinformatics (Oxford, England)·2026
See all related articles

Machine learning models identified patterns in HIV and HCV polyprotein cleavage sites. Artificial neural networks proved more effective than symbolic learning for predicting protease activity, aiding antiviral drug development.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning in Virology

Background:

  • Viral polyproteins require precise cleavage by proteases for function.
  • Understanding protease specificity in Human Immunodeficiency Virus (HIV) and Hepatitis C Virus (HCV) is crucial for antiviral drug development.
  • Current knowledge of viral polyprotein processing remains limited despite rapid sequence data growth.

Purpose of the Study:

  • To identify specific patterns in HIV and HCV viral polyprotein cleavage sites using machine learning.
  • To investigate the generalizability of known cleavage sites to predict unknown sites.
  • To contribute to a broader understanding of viral protease function and substrate characteristics.

Main Methods:

  • Application of machine learning techniques, including artificial neural networks and symbolic learning (See5).

Related Experiment Videos

  • Analysis of amino acid residues at viral protease cleavage sites.
  • Comparative evaluation of different machine learning models for predicting cleavage events.

    • Main Results:

    • Both artificial neural networks and symbolic learning identified novel substrate attributes.
    • Artificial neural networks demonstrated superior performance compared to symbolic learning methods.
    • The study provides insights into the features governing viral protease specificity.

    Conclusions:

    • Machine learning, particularly artificial neural networks, can effectively model viral protease cleavage site attributes.
    • Findings support the potential for developing more targeted antiviral therapies based on protease inhibitor strategies.
    • Further experimental investigation can be guided by these computational predictions.