PROPKA 3

What is PROPKA?

PROPKA is an empirical method for predicting the pKa values of ionizable amino acid residues in proteins based on their three-dimensional structure. Developed by Jan H. Jensen and colleagues at the University of Copenhagen, PROPKA calculates how the protein environment shifts the pKa of each ionizable group away from its intrinsic (model) value.

The pKa of an ionizable group determines at what pH that group gains or loses a proton. In solution, amino acids have characteristic pKa values, but within a folded protein, factors such as burial from solvent, hydrogen bonding, and electrostatic interactions with nearby charged residues can shift these values substantially. PROPKA quantifies these perturbations using structure-based empirical energy functions, enabling rapid prediction of protonation states at any given pH.

PROPKA 3 introduced consistent treatment of internal and surface residues through improved desolvation and dielectric response modeling. The method handles ASP, GLU, HIS, CYS, TYR, LYS, ARG residues and both N- and C-terminal groups. Benchmarks show root-mean-square deviations of 0.79 pKa units for Asp/Glu, 0.75 for Tyr, 0.65 for Lys, and 1.00 for His residues compared to experimental measurements.

How to use PROPKA online

ProteinIQ provides a web-based interface for running PROPKA without command-line installation. Upload a protein structure file or fetch one from RCSB, and receive predicted pKa values for all ionizable residues.

Inputs

Results

The output is a spreadsheet listing all ionizable residues with their predicted and model pKa values.

Model pKa reference values

PROPKA uses the following intrinsic pKa values as baselines:

Interpreting pKa shifts

The Shift column indicates how the protein environment alters the intrinsic pKa:

• Positive shift — The residue is more difficult to ionize (stabilized neutral form). Common causes include burial from solvent, loss of favorable interactions in the charged state, or proximity to like-charged residues.
• Negative shift — The residue ionizes more readily (stabilized charged form). Typically results from hydrogen bonds that stabilize the charged state or proximity to oppositely charged groups.
• Large shifts (> 2 pKa units) — May indicate functionally important residues, catalytic sites, or unusual structural environments. Active site residues often display perturbed pKa values that enable catalysis at physiological pH.

Determining protonation states

To determine if a residue is protonated at a given pH:

• Acidic residues (ASP, GLU, CYS, TYR, C-terminus) — Protonated (neutral) when pH < pKa; deprotonated (charged) when pH > pKa
• Basic residues (HIS, LYS, ARG, N-terminus) — Protonated (charged) when pH < pKa; deprotonated (neutral) when pH > pKa

At physiological pH (~7.4), residues with predicted pKa values near 7.4 may exist in mixed protonation states.

How does PROPKA work?

PROPKA computes pKa values by calculating a perturbation to the model (intrinsic) pKa caused by the protein environment. The predicted pKa is expressed as:

$\text{pKa} = \text{pKa}_{\text{model}} + \Delta\text{pKa}_{\text{DS}} + \Delta\text{pKa}_{\text{HB}} + \Delta\text{pKa}_{\text{CC}}$

where DS represents desolvation effects, HB represents hydrogen bonding interactions, and CC represents Coulombic charge-charge interactions.

Desolvation effects

When an ionizable group is buried within a protein, it loses favorable solvation interactions with water. Desolvation destabilizes the charged form relative to the neutral form, shifting carboxylate pKa values upward (making them harder to deprotonate) and amine pKa values downward (making them harder to protonate).

PROPKA calculates the desolvation penalty from two factors:

• Solvent accessible surface area — Groups with less exposed surface experience greater desolvation penalties
• Depth of burial — Distance from the protein surface, with deeply buried groups experiencing the largest effects

The desolvation contribution scales continuously between surface and internal residues, avoiding the discontinuous behavior of earlier PROPKA versions that treated these as discrete categories.

Hydrogen bonding

Hydrogen bonds can stabilize either the charged or neutral form of an ionizable group, depending on the geometry and donor/acceptor relationship:

• Hydrogen bonds to charged carboxylates — Stabilize the charged form and shift pKa downward
• Hydrogen bonds from protonated groups — Can stabilize the neutral form of acids

The hydrogen bonding contribution depends on both distance and angle, with optimal geometry producing the largest pKa shifts. PROPKA evaluates both side-chain and backbone hydrogen bond donors and acceptors.

Coulombic interactions

Electrostatic interactions between ionizable groups affect their pKa values:

• Like charges — Repulsion destabilizes simultaneous ionization, shifting pKa values to reduce the probability of both groups being charged
• Opposite charges — Attraction stabilizes the charged forms, shifting pKa values toward greater ionization

For buried residue pairs, PROPKA applies full Coulombic interactions. For surface-exposed pairs, the high dielectric of water screens these interactions substantially. The transition between these regimes uses the same smooth interpolation applied to desolvation effects.

Coupled residues

When two ionizable groups interact strongly, their protonation states become coupled. Neither group can be assigned a single pKa value because the ionization of one depends on the protonation state of the other. PROPKA identifies these cases and marks them with an asterisk in detailed output.

Limitations

• Static structure assumption — PROPKA analyzes a single conformation and does not account for conformational flexibility or dynamics that may affect pKa values
• Empirical parameterization — Accuracy depends on the training set; unusual structural environments not represented in the parameterization may yield less reliable predictions
• Coupled residues — Strongly interacting ionizable groups require special treatment; predicted pKa values for coupled pairs should be interpreted with caution
• Ligand handling — PROPKA 3.1+ supports protein-ligand complexes, but ligand ionizable groups may be less accurately predicted than protein residues
• Crystal packing artifacts — Structures from X-ray crystallography may contain crystal contacts that perturb pKa values in non-physiological ways
• Arginine predictions — ARG predictions are less validated due to limited experimental data at high pH

Structures with missing atoms or poor resolution may produce unreliable results. Using PDB Fixer to repair structures before running PROPKA is recommended.

Related tools

• PDB2PQR — Structure preparation tool that uses PROPKA internally to assign protonation states before electrostatics calculations
• Isoelectric Point Calculator — Calculates the overall pI of a protein from sequence, complementing residue-level pKa predictions
• Protein Parameters — Computes physicochemical properties from sequence including charge at different pH values

Applications

• Electrostatics calculations — Determining correct protonation states for Poisson-Boltzmann solvers and molecular dynamics simulations
• Enzyme mechanism studies — Identifying residues with perturbed pKa values that may participate in catalysis
• pH-dependent binding — Predicting how ligand or protein binding changes with pH based on ionization states at the interface
• Protein stability analysis — Understanding how ionizable groups contribute to the pH-dependence of protein folding and stability
• Molecular dynamics preparation — Assigning appropriate protonation states before running MD simulations at a specific pH