PKA17-A Coarse-Grain Grid-Based Methodology and Web-Based Software for Predicting Protein pK _a Shifts.

Area of Science:

• Biochemistry
• Computational Biology
• Structural Biology

Background:

• Accurate prediction of protein pK_a values is crucial for understanding protein function and interactions.
• Existing computational models require refinement for enhanced predictive power.

Purpose of the Study:

• To develop and validate PKA17, a novel coarse-grain grid-based model for predicting protein pK_a shifts.
• To compare the performance of PKA17 against the established PROPKA software.

Main Methods:

• Developed a coarse-grain grid-based model (PKA17) for pK_a prediction.
• Performed parameter fitting using experimental data for 442 Asp, Glu, His, Lys, and Arg residues.
• Benchmarked PKA17 against PROPKA using average unsigned error and root-mean-square deviation (RMSD).

Main Results:

• PKA17 achieved an average unsigned error of 0.628 pH units and an RMSD of 0.831 pH units.
• PKA17 outperformed PROPKA, which yielded an average unsigned error of 0.761 pH units and an RMSD of 1.063 pH units.
• Confirmed that local protein environment significantly influences ionizable residue pK_a values.

Conclusions:

• PKA17 demonstrates superior accuracy in predicting protein pK_a shifts compared to PROPKA.
• The PKA17 model provides a valuable tool for researchers studying protein electrostatics and function.
• Further exploration of combining PKA17 and PROPKA may enhance prediction accuracy.