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

SnapDRAGON: a method to delineate protein structural domains from sequence data.

Richard A George1, Jaap Heringa

  • 1Division of Mathematical Biology, National Institute for Medical Research, The Ridgeway, Mill Hill, London NW7 1AA, UK.

Journal of Molecular Biology
|February 28, 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

Tumor break load quantitates structural variant-associated genomic instability with biological and clinical relevance across cancers.

NPJ precision oncology·2025
Same author

High Prevalence of Chromosomal Rearrangements and LINE Retrotranspositions Detected in Formalin-Fixed, Paraffin-Embedded Colorectal Cancer Tissue.

The Journal of molecular diagnostics : JMD·2024
Same author

CIBRA identifies genomic alterations with a system-wide impact on tumor biology.

Bioinformatics (Oxford, England)·2024
Same author

Mining literature and pathway data to explore the relations of ketamine with neurotransmitters and gut microbiota using a knowledge-graph.

Bioinformatics (Oxford, England)·2023
Same author

Author Correction: Applying federated learning to combat food fraud in food supply chains.

NPJ science of food·2023
Same author

Applying federated learning to combat food fraud in food supply chains.

NPJ science of food·2023
Same journal

Biologically Relevant, Cationic Residues in Human Rhinovirus Stabilize Capsid-Bound RNA Duplexes, and Restrict Capsid Flexibility.

Journal of molecular biology·2026
Same journal

Cryo-EM structures of phage T4 infection intermediate.

Journal of molecular biology·2026
Same journal

A classic fold with a twist: Structural architecture of Dhillonvirus phage Bas18.

Journal of molecular biology·2026
Same journal

Tesorai Search: cloud-based database search engine boosts identifications for mass spectrometry proteomics with a pretrained peptide-spectrum deep-learning model.

Journal of molecular biology·2026
Same journal

Characterization of diverse functions of NRF1 nuclear localization sequence.

Journal of molecular biology·2026
Same journal

UPF3A and UPF3B shape the transcriptome cooperatively yet oppose cell function.

Journal of molecular biology·2026
See all related articles

SnapDRAGON predicts protein domain boundaries using sequence data and 3D models. This computational method accurately identifies domain numbers and locations, aiding protein structure analysis.

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Science

Background:

  • Protein domains are fundamental units of structure and function.
  • Identifying protein domain boundaries from sequence alone is a significant challenge in bioinformatics.
  • Existing methods often require experimental structural data.

Purpose of the Study:

  • To present SnapDRAGON, a novel computational method for predicting protein domain boundaries solely from sequence information.
  • To evaluate the accuracy of SnapDRAGON in predicting both the number and precise locations of protein domains.

Main Methods:

  • Utilizing hydrophobic residue clustering as a biophysical principle for domain identification.
  • Generating multiple *ab initio* three-dimensional (3D) models for protein multiple sequence alignments.

Related Experiment Videos

  • Employing a distance geometry-based folding technique coupled with a 3D-domain assignment algorithm.

    • Main Results:

    • Achieved 72.4% overall accuracy in predicting the number of domains across a dataset of 414 protein alignments.
    • Delineated inter-domain boundary positions with 63.9% accuracy for continuous domains and 35.4% for discontinuous domains.
    • Overall domain boundary delineation accuracy reached 51.8%, independent of sequence similarity.

    Conclusions:

    • SnapDRAGON effectively predicts protein domain organization using only sequence data.
    • The method demonstrates robustness across diverse protein domain architectures.
    • This approach offers a valuable tool for structural and functional annotation of proteins.