PLIP: fully automated protein–ligand interaction profiler

By Sebastian Salentin, Sven Schreiber, V. Haupt, Melissa F. Adasme, M. Schroeder. Nucleic Acids Res., 2015

2,042 citations recorded by the source corpus.

The characterization of interactions in protein–ligand complexes is essential for research in structural bioinformatics, drug discovery and biology. However, comprehensive tools are not freely available to the research community. Here, we present the protein–ligand interaction profiler (PLIP), a novel web service for fully automated detection and visualization of relevant non-covalent protein–ligand contacts in 3D structures, freely available at projects.biotec.tu-dresden.de/plip-web. The input is either a Protein Data Bank structure, a protein or ligand name, or a custom protein–ligand complex (e.g. from docking). In contrast to other tools, the rule-based PLIP algorithm does not require any structure preparation. It returns a list of detected interactions on single atom level, covering seven interaction types (hydrogen bonds, hydrophobic contacts, pi-stacking, pi-cation interactions, salt bridges, water bridges and halogen bonds). PLIP stands out by offering publication-ready images, PyMOL session files to generate custom images and parsable result files to facilitate successive data processing. The full python source code is available for download on the website. PLIP's command-line mode allows for high-throughput interaction profiling.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/9fb2bf7b0fb083b764794b82486189b314e6e9ab

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Citations in the corpus

PLIP 2021: expanding the scope of the protein–ligand interaction profiler to DNA and RNA (2021, methodology): “After the correct detection of receptor and ligands, PLIP continues with the subsequent interaction detection and visualisation as previously described in (2).”
CB-Dock2: improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting (2022, background): “Predicting interactions between proteins and small molecules plays key roles in deciphering a wide variety of biological processes and is crucial to understanding protein functions as well as leveraging drug development (1,2).”
Ribavirin, Remdesivir, Sofosbuvir, Galidesivir, and Tenofovir against SARS-CoV-2 RNA dependent RNA polymerase (RdRp): A molecular docking study (2020, methodology): “After docking, the structures were examined using the ProteinLigand Interaction Profiler (PLIP) web server (Technical University of Dresden) [43].”
SARS-CoV-2 RNA dependent RNA polymerase (RdRp) targeting: an in silico perspective (2020, methodology): “Protein-Ligand Interaction Profiler (PLIP) web server (Technical University of Dresden) is used to mine the docking complexes (Salentin et al., 2015).”
Anti-HCV, nucleotide inhibitors, repurposing against COVID-19 (2020, neutral): “After docking, the structures are examined by the aid of the ProteinLigand Interaction Profiler (PLIP) web server (Technical University of Dresden) and tabulated for comparison [58].”
Arpeggio: A Web Server for Calculating and Visualising Interatomic Interactions in Protein Structures (2017, background): “PLIP [17] recently expanded these interactions to look at all protein–ligand interactions, but like earlier methods is limited to binary interaction fingerprints in its output.”