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Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry
Published on: October 15, 2018
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PDB-CAT: A user-friendly tool to classify and analyze PDB protein-ligand complexes.
Ariadna Llop-Peiró1, Said Trujillo-De León1, Gerard Pujadas1
1Departament de Bioquímica i Biotecnologia, Research Group in Cheminformatics & Nutrition, Universitat Rovira i Virgili, Tarragona, Spain.
Protein Science : a Publication of the Protein Society
|November 13, 2025
Summary
PDB-CAT is a new tool that classifies Protein Data Bank structures as apo or holo and differentiates covalent from non-covalent ligand-protein complexes. This facilitates research on protein-ligand interactions and drug discovery.
Area of Science:
- Structural Biology
- Bioinformatics
- Computational Chemistry
Background:
- The Protein Data Bank (PDB) is a critical resource for understanding protein-ligand interactions, but lacks automated tools for classifying structures.
- Differentiating between apo (ligand-free) and holo (ligand-bound) states, and identifying covalent versus non-covalent binding, is challenging.
- This hinders efficient retrieval and analysis of specific structural datasets for drug discovery and mechanistic studies.
Purpose of the Study:
- To develop PDB-CAT, a user-friendly tool for automated classification and information extraction from PDBx/mmCIF files.
- To accurately categorize protein structures based on ligand presence and binding type (apo, holo, covalent, non-covalent).
- To enable verification of protein sequence mutations against reference sequences.
Main Methods:
- PDB-CAT employs a parallelized implementation for efficient processing of PDBx/mmCIF files.
- It utilizes a blacklist-based approach to automatically identify ligands within protein complexes.
- Classification is based on ligand presence (apo vs. holo) and binding mode (covalent vs. non-covalent).
Main Results:
- PDB-CAT successfully classifies protein structures into apo, holo, covalent, and non-covalent categories.
- The tool can also identify mutations in protein sequences by comparison with reference data.
- Demonstrated efficiency by classifying SARS-CoV-2 Main Protease complexes and screening the PDBbindv2020 database in under 10 minutes.
Conclusions:
- PDB-CAT provides an efficient and automated solution for classifying protein structures in the PDB.
- The tool simplifies the retrieval of specific structural datasets, accelerating research in structural biology and drug discovery.
- Availability on GitHub and a tutorial on GitBook promote widespread adoption and use.
