PDB-CAT: A user-friendly tool to classify and analyze PDB protein-ligand complexes

The Protein Data Bank (PDB) contains more than 235,000 three-dimensional biostructures and is growing at a rate of nearly 10% per year. The PDB is essential to gain knowledge on how proteins and ligands interact and how these interactions are correlated with the quantitative activity of each ligand/target pair. Unfortunately, the lack of a tool that can classify structures as apo or holo, that is by no means straightforward, and differentiate between covalent and non-covalent ligand-protein complexes makes it difficult to obtain the structures that belong to each set. To address this issue, we present PDB-CAT, a user-friendly tool that facilitates the categorization and extraction of key information from PDBx/mmCIF files through an efficient parallelized implementation. PDB-CAT uses a blacklist-based approach to automatically identify the ligand in a complex. It then classifies the PDB files based on ligand presence: structures without a ligand are classified as apo, whereas those with a ligand are classified as covalently or non-covalently bound, depending on the type of binding. As well as making this classification, the program can verify if there are any mutations in the protein sequence by comparing it to a reference sequence. An example is included to illustrate two different uses: the classification of SARS-CoV-2 Main Protease complexes depending on their variant, and the complete screening of the PDBbindv2020, achieved in <10 min. PDB-CAT is now available on GitHub (https://github.com/URV-cheminformatics/PDB-CAT) and the corresponding tutorial on GitBook (https://ariadnallopps-organization.gitbook.io/pdb-cat).