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Analyzing and Building Nucleic Acid Structures with 3DNA
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Databases and web-based tools for studying structures of protein-nucleic acid complexes.

Justas Dapkūnas1, Česlovas Venclovas1

  • 1Institute of Biotechnology, Life Sciences Center, Vilnius University, Saulėtekio 7, LT-10257 Vilnius, Lithuania.

Current Opinion in Structural Biology
|June 12, 2025
PubMed
Summary
This summary is machine-generated.

This review covers essential databases and web tools for 3D protein-nucleic acid complex structures. Emerging AI methods promise to enhance the scarcity of tools for analyzing these vital molecular interactions.

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Area of Science:

  • Molecular Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Structural data on protein-DNA and protein-RNA interactions are crucial for understanding molecular biology.
  • Access to comprehensive databases and web resources for these complex structures is vital for researchers.
  • Current tools for analyzing protein-nucleic acid complexes are limited.

Purpose of the Study:

  • To review available databases and web-based resources for 3D structures of protein-nucleic acid complexes.
  • To highlight core databases for experimental data and derivative databases for interaction-specific structures.
  • To provide an overview of web servers for structure prediction, analysis, and comparison.

Main Methods:

  • Literature review of existing databases and web resources.
  • Categorization of resources into core, derivative, and analysis tools.
  • Discussion of current limitations and future prospects, including AI-driven techniques.

Main Results:

  • Identification and description of key databases for protein-nucleic acid structural data.
  • Overview of specialized resources focusing on protein-nucleic acid interactions.
  • Summary of available web servers for structure prediction and analysis.

Conclusions:

  • A significant number of resources exist for protein-nucleic acid structural data, but tools for integrated analysis are scarce.
  • The development of AI-driven structure prediction techniques is expected to significantly advance the field.
  • Further development of integrated analysis tools is needed to fully leverage available structural data.