---
id: propka
version: "3.5.1"
license: LGPL v2.1
license_treatment: copyleft
maintenance: dormant
---

# propka — Heuristic pKa calculations with ligands

License: copyleft · Maintenance: dormant · Downloads: 436.8K/mo

## What it is and what it does

PROPKA is a mature bioinformatics library that calculates pKa values—the pH at which ionizable amino acid residues protonate or deprotonate—directly 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.

The 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.

Use it for:

- 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.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

PROPKA predicts pKa values of ionizable groups in proteins and protein-ligand complexes from 3D structure using empirical heuristics.

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.

## Install

pip install propka
uv add propka
poetry add propka

## Installing propka

Before you install: Low install friction with no runtime dependencies. Dormant maintenance status (955 days since last release) but marked mature and supporting current Python versions through 3.12.

License in practice: Licensed under LGPL v2.1 (copyleft). Derivative works and modifications must be distributed under the same license; static linking or bundling requires careful compliance review.

Quickstart:

pip install propka

from propka.molecular_container import MolecularContainer
mol = MolecularContainer('protein.pdb')
mol.calculate_pka()

Requires a valid PDB structure file as input; PROPKA is a command-line and library tool for offline calculation, not a web service.

Verify before relying:

- Whether the dormant status (no commits tracked) reflects genuine maintenance or only PyPI release infrequency.
- Performance characteristics and accuracy benchmarks for modern protein structures or large complexes.
- Whether ligand handling in version 3.1+ is production-ready or still experimental.

## Package facts

- License: LGPL v2.1 (copyleft)
- Python support: supports_current
- Install friction: low
- Maintenance: dormant
- Downloads: 436.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

pka prediction protein, ionizable groups pka, protein ligand pka, structural pka calculation, protein protonation state, empirical pka estimation, 3d structure pka, structural-biology, computational-chemistry, pka-prediction

[View on SkillFed](https://skillfed.io/packages/propka) · [View on PyPI](https://pypi.org/project/propka/)