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

Characterizing proteolytic cleavage site activity using bio-basis function neural networks.

Rebecca Thomson1, T Charles Hodgman, Zheng Rong Yang

  • 1Department of Structure Biology, Oxford University, UK.

Bioinformatics (Oxford, England)
|September 27, 2003
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

Global risk of selection and spread of Plasmodium falciparum histidine-rich protein 2 and 3 gene deletions.

Nature medicine·2025
Same author

Global risk of selection and spread of <i>Plasmodium falciparum</i> histidine-rich protein 2 and 3 gene deletions.

medRxiv : the preprint server for health sciences·2023
Same author

Lifestyle management in polycystic ovary syndrome - beyond diet and physical activity.

BMC endocrine disorders·2023
Same author

In silico prediction of Severe Acute Respiratory Syndrome Coronavirus 2 main protease cleavage sites.

Proteins·2021
Same author

Prevalence and factors associated with hypertension among people living with HIV/AIDS on antiretroviral therapy in Uganda.

The Pan African medical journal·2021
Same author

Prevalence of <i>Plasmodium falciparum</i> lacking histidine-rich proteins 2 and 3: a systematic review.

Bulletin of the World Health Organization·2020
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

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

A new method predicts protease cleavage site activity and efficiency using neural learning and amino acid similarity. This improves prediction accuracy and reduces computational time for applications in protein chemistry and biopharmaceutical development.

Area of Science:

  • Biochemistry
  • Proteomics
  • Computational Biology

Background:

  • Accurate prediction of protease cleavage sites is crucial in protein chemistry, proteomics, and biopharmaceutical development.
  • Existing methods often use simple regular expressions or inadequate biological data models, limiting their effectiveness.

Purpose of the Study:

  • To develop a novel methodology for characterizing proteolytic cleavage site activities.
  • To improve the accuracy and efficiency of predicting protease cleavage sites.

Main Methods:

  • Developed a methodology incorporating activity class prediction for proteolytic efficiency.
  • Utilized an amino acid similarity matrix for non-parametric neural learning to enhance prediction robustness and reduce learning time complexity.

Related Experiment Videos

Main Results:

  • Successfully predicted and characterized Trypsin cleavage sites using the new methodology.
  • Demonstrated success in predicting HIV protease cleavage sites, validating the approach.
  • Activity class prediction proved effective for characterizing cleavage site susceptibility.

Conclusions:

  • The novel methodology offers a significant advancement in predicting proteolytic cleavage site activity and efficiency.
  • The approach enhances prediction robustness and computational efficiency.
  • This method has direct applications in protein analysis and drug development.