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

Determining functional specificity from protein sequences.

Jason E Donald1, Eugene I Shakhnovich

  • 1Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA 02138, USA.

Bioinformatics (Oxford, England)
|March 31, 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

Complete enzyme clustering enhances coenzyme Q biosynthesis via substrate channeling.

Nature communications·2026
Same author

Biophysical fitness landscape design traps viral evolution.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Chaperonin recognition of protein dynamics drives drug resistance.

bioRxiv : the preprint server for biology·2026
Same author

Co-targeting Metabolic Neighbours Constraints Bacterial Adaptive Evolution.

bioRxiv : the preprint server for biology·2026
Same author

CASPULE: A computational tool to study sticker spacer polymer condensates.

PLoS computational biology·2026
Same author

Evolutionary dynamics under phenotypic uncertainty.

bioRxiv : the preprint server for biology·2026
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 consistently selects protein family group sizes, independent of clustering techniques. This approach identifies significant protein groups corresponding to distinct DNA-binding motifs, aiding in protein interaction studies.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Protein families contain homologous sequences with diverse functions.
  • A key challenge is determining the optimal granularity for functional grouping.
  • Existing methods lack a general approach for selecting group size.

Purpose of the Study:

  • To propose a consistent, generalizable method for selecting protein family granularity.
  • To validate the method against known functional and binding specificity data.
  • To compare the proposed method with existing protein clustering algorithms.

Main Methods:

  • Developed a novel method for selecting granularity, independent of sequence similarity and clustering algorithms.
  • Applied the method to three well-studied protein families: basic leucine zippers, nuclear receptors, and C2H2 zinc finger proteins.

Related Experiment Videos

  • Validated group significance using functional information, experimentally determined binding specificities, and data randomization.

    • Main Results:

    • The proposed method successfully identified significant and functionally relevant protein groups.
    • These groups accurately correspond to known DNA-binding motifs within the studied protein families.
    • The method demonstrated superior or comparable performance to the TRIBE-MCL algorithm.

    Conclusions:

    • The developed method provides a robust and consistent approach to defining protein family granularity.
    • The identified protein groupings are highly significant and useful for understanding DNA-binding specificities.
    • This approach is expected to advance the study of protein interactions beyond DNA binding.