pKD

pKD identifies sets of point mutations that induce desired changes in the pKa values of specific target residues to enable re-engineering of pH-dependent characteristics of proteins, including stability and enzymatic activity.

Key Features:

• Mutation set identification: Identifies sets of point mutations that can shift pKa values of specified target residues.
• DeltapKa prediction: Predicts DeltapKa values for all feasible point mutations.
• FDPB solver: Employs finite difference solutions to the Poisson-Boltzmann equation (FDPB) as the core computational approach.
• Protonation-state energy calculations: Calculates energy differences across protonation states by altering atomic charges or adding/removing hydrogens.
• Hydrogen-bond network optimization: Globally optimizes the hydrogen-bond network for each protonation state to improve pKa accuracy, particularly for buried residues.
• Structural artifact consideration: Accounts for potential pK(a) errors arising from crystal-packing artifacts in X-ray structures and supports use of corrected structures for parameter optimization.

Scientific Applications:

• Re-engineering pH-dependent properties: Design mutations to modulate protein stability and enzymatic activity via targeted pKa changes.
• Protein redesign: Tune protonation behavior and pH-dependent characteristics of proteins through systematic point-mutation analysis.

Methodology:

pKD uses finite difference solutions to the Poisson-Boltzmann equation (FDPB) to compute energy differences across protonation states by altering atomic charges or adding/removing hydrogens, globally optimizes hydrogen-bond networks for each protonation state, and predicts DeltapKa values for feasible point mutations.