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

Three-dimensional shape-structure comparison method for protein classification.

Petros Daras1, Dimitrios Zarpalas, Apostolos Axenopoulos

  • 1Informatics and Telematics Institute (ITI), 1st Km Thermi-Panorama Road, Thermi-Thessaloniki, PO Box 361, Greece. daras@iti.gr

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|October 20, 2006
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

Large Language Models for Cardiovascular Disease, Cancer, and Mental Disorders: A Review of Systematic Reviews.

Healthcare (Basel, Switzerland)·2026
Same author

Services for Connected, Cooperated, and Automated Mobility based on Big Data and Artificial Intelligence: The SHOW project paradigm.

Open research Europe·2025
Same author

PDualNet: a deep learning framework for joint prediction of Parkinson's disease progression subtype and MDS-UPDRS scores.

Scientific reports·2025
Same author

Manufacturing data spaces applications in europe - A survey.

Data in brief·2025
Same author

Interactive digital twins enabling responsible extended reality applications.

Scientific reports·2025
Same author

Chemical Targeting of the ATXN1 aa99-163 Interaction Site Suppresses polyQ-Expanded Protein Dimerization.

ACS omega·2025
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
See all related articles

This study introduces a novel 3D shape-based method for protein classification, achieving over 99% accuracy. The approach efficiently searches and retrieves protein molecules using geometric and structural data, outperforming existing methods like DALI.

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Biophysics

Background:

  • Protein structure analysis is crucial for understanding function.
  • Existing methods for protein classification can be computationally intensive.
  • Efficient search and retrieval of protein molecules are essential in bioinformatics.

Purpose of the Study:

  • To develop an efficient 3D shape-based approach for protein molecule search, retrieval, and classification.
  • To create a method that utilizes both geometric and structural features of proteins.
  • To evaluate the performance and accuracy of the proposed method against established databases and algorithms.

Main Methods:

  • A 3D shape-based approach using protein data bank (PDB) files.
  • Application of the Spherical Trace Transform for rotation-invariant descriptor vectors.

Related Experiment Videos

  • Integration of geometry-based and attribute-based descriptor vectors.
  • Testing three classification methods using a subset of the FSSP/DALI database for ground truth.

    • Main Results:

    • The proposed method achieves over 99% classification accuracy.
    • The approach is significantly simpler and faster than the DALI method.
    • The method effectively describes protein 3D shape using rotation-invariant descriptors.

    Conclusions:

    • The 3D shape-based approach offers a highly accurate and efficient solution for protein classification.
    • This method provides a valuable tool for structural bioinformatics and drug discovery.
    • The Spherical Trace Transform is effective for generating protein shape descriptors.