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Conservation of Protein Domains Over Different Proteins02:26

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Related Experiment Video

Updated: Jan 22, 2026

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
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3D-PP: A Tool for Discovering Conserved Three-Dimensional Protein Patterns.

Alejandro Valdés-Jiménez1,2, Josep-L Larriba-Pey3, Gabriel Núñez-Vivanco4

  • 1Center for Bioinformatics, Simulations and Modelling, Universidad de Talca, 3460000 Talca, Chile.

International Journal of Molecular Sciences
|July 3, 2019
PubMed
Summary
This summary is machine-generated.

3D-PP is a new web server that identifies similar 3D amino acid patterns in protein structures, regardless of sequence similarity. This tool aids in protein classification and drug design by discovering novel structural motifs.

Keywords:
3D-patternsconserved patternssimilarity

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Area of Science:

  • Structural bioinformatics
  • Computational biology
  • Drug discovery

Background:

  • Conserved 3D protein patterns offer insights into protein classification, function, and drug design.
  • Existing computational tools often focus on known patterns, necessitating methods for discovering all possible 3D patterns.

Purpose of the Study:

  • To introduce 3D-PP, a novel, free web server for discovering and recognizing similar 3D amino acid patterns across protein structures.
  • To enable the identification of diverse structural patterns, including allosteric sites and enzyme-cofactor interactions, without prior ligand knowledge.

Main Methods:

  • Developed 3D-PP, a web server utilizing a high-performance graph database for storing protein structural data.
  • Input accepts PDB access codes or zip files of PDB coordinates from various experimental or in silico sources.
  • Identified patterns are presented as sequence patterns for further analysis within the web interface.

Main Results:

  • Validated 3D-PP's accuracy and suitability using Zinc finger and Serotonin target proteins from the Protein Data Bank.
  • Demonstrated 3D-PP's utility in discovering novel 3D patterns from in silico homology models of cancer-related proteins.
  • Confirmed 3D-PP as a reliable, flexible, and user-friendly tool for identifying conserved structural motifs.

Conclusions:

  • 3D-PP effectively identifies conserved structural motifs, enhancing knowledge of protein function and classification.
  • The tool's ability to discover novel patterns has implications for rational drug design and understanding protein roles in diseases like cancer.
  • 3D-PP provides a valuable resource for the scientific community, accessible at https://appsbio.utalca.cl/3d-pp/.