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

PDP: protein domain parser.

Nickolai Alexandrov1, Ilya Shindyalov

  • 1Ceres Inc, 3007 Malibu Canyon Road, Malibu, CA 90265, USA. nicka@ceres-inc.com

Bioinformatics (Oxford, England)
|February 14, 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

HLA-DR Haplotype Diversity Revealed by Pangenome Sequence Analysis.

HLA·2026
Same author

HLA-OLI: A new MHC class I pseudogene and HLA-Y are located on a 60 kb indel in the human MHC between HLA-W and HLA-J.

HLA·2023
Same author

Characterization of the Leaf Microbiome from Whole-Genome Sequencing Data of the 3000 Rice Genomes Project.

Rice (New York, N.Y.)·2020
Same author

A platinum standard pan-genome resource that represents the population structure of Asian rice.

Scientific data·2020
Same author

Corrigendum to: Rice Galaxy: an open resource for plant science.

GigaScience·2019
Same author

Towards a deeper haplotype mining of complex traits in rice with RFGB v2.0.

Plant biotechnology journal·2019
Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·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
See all related articles

A new program, PDP, automatically identifies protein domains in 3D structures with over 80% accuracy. This computational tool aids structural biology research by reliably predicting protein domain boundaries.

Area of Science:

  • Structural biology
  • Computational biology
  • Bioinformatics

Background:

  • Protein domain identification is crucial for understanding protein function and evolution.
  • Existing methods for domain assignment can be labor-intensive or lack accuracy.
  • The Protein Data Bank (PDB) contains a vast amount of structural data requiring efficient analysis.

Purpose of the Study:

  • To develop and validate an automated program for identifying domains in protein three-dimensional structures.
  • To assess the performance of the developed program against established benchmarks.
  • To provide a reliable computational tool for structural domain assignment.

Main Methods:

  • Development of a novel program (PDP) for automatic domain identification in protein 3D structures.

Related Experiment Videos

  • Performance evaluation using three distinct benchmarks.
  • Comparison against the expert-curated SCOP database.
  • Validation against manual domain assignments and a standard set of 55 benchmark proteins.

    • Main Results:

    • The PDP program demonstrated high accuracy in identifying protein domains across all tested benchmarks.
    • Correct domain identification exceeded 80% in all three evaluation scenarios.
    • The program's performance is comparable or superior to existing automatic domain assignment methods.

    Conclusions:

    • The developed PDP program is an effective and accurate tool for automatic protein domain identification.
    • This automated approach can significantly streamline structural biology research.
    • PDP provides a reliable method for analyzing large datasets of protein structures.