{"categories":[{"label":"Bio-Informatics","url":"https://skillfed.io/packages/category/scientific-engineering-bio-informatics"},{"label":"Chemistry","url":"https://skillfed.io/packages/category/scientific-engineering-chemistry"}],"enrichment":{"capability":"PROPKA predicts pKa values of ionizable groups in proteins and protein-ligand complexes from 3D structure using empirical heuristics.","skillfed_tags":["structural-biology","computational-chemistry","pka-prediction"],"use_cases":["Calculate protonation states of proteins at physiological pH for molecular dynamics simulations or docking studies.","Predict ionizable residue pKa shifts caused by protein-ligand binding to understand binding thermodynamics.","Batch-process PDB structures to generate pKa tables for structural biology databases or annotation pipelines.","Rationalize experimental pH-dependent protein behavior by mapping predicted pKa values to observed titration curves.","Prepare protein structures for pH-dependent computational studies by assigning appropriate protonation states."],"what_it_does":"PROPKA is a mature bioinformatics library that calculates pKa values\u2014the pH at which ionizable amino acid residues protonate or deprotonate\u2014directly from protein 3D structures. It uses empirical heuristics calibrated on experimental data and handles both standalone proteins and protein-ligand complexes. The method is described in peer-reviewed literature and has been in use since at least 2011.\n\nThe package provides both a command-line interface and a Python API for integration into computational workflows. It requires only a PDB structure file as input and has no external runtime dependencies, making it straightforward to install and use in analysis pipelines. However, the project shows dormant maintenance (last release January 2024, no recent commits tracked), so users should verify that its predictions meet their accuracy requirements before relying on it for new research.","worth_installing":"Yes, if you need offline pKa prediction from PDB structures and can verify the method's accuracy for your use case. The package is mature, has no dependencies, and installs easily. However, dormant maintenance and lack of recent commits mean you should test predictions against your own data or literature benchmarks before using results in new publications. The LGPL copyleft license requires derivative works to remain open-source."},"id":"propka","links":{"html":"https://skillfed.io/packages/propka","md":"https://skillfed.io/packages/propka.md","pypi":"https://pypi.org/project/propka/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-01-02","license_spdx":null,"license_treatment":"copyleft","name":"propka","python_support":"supports_current","summary":"Heuristic pKa calculations with ligands"},"popularity":{"monthly_downloads":436779,"position":6673,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.5.1"}