Electrostatic pKa computations in proteins: role of internal cavities

Area of Science:

• Biochemistry
• Computational Biology
• Structural Biology

Background:

• The solvent accessible surface area (SASA) algorithm is standard for protein surface characterization in electrostatic energy computations.
• SASA's limitation in detecting narrow internal cavities leads to inaccuracies in pK(a) computations.
• Accurate pK(a) prediction is crucial for understanding protein function and designing mutants.

Purpose of the Study:

• To introduce and validate a novel cavity-detection algorithm for improved protein pK(a) calculations.
• To address the limitations of the SASA algorithm in characterizing protein interiors.
• To enhance the accuracy of electrostatic energy computations involving protein titratable groups.

Main Methods:

• Development of a new algorithm specifically designed to identify narrow cavities within protein structures.
• Application of the new algorithm to compute pK(a) values for titratable groups in SNase variants with available crystal structures.
• Comparison of computed pK(a) values with experimental data and results obtained using the conventional SASA algorithm and Karlsberg+ software.

Main Results:

• The new cavity-algorithm significantly improved pK(a) prediction accuracy for titratable residues near large cavities in SNase variants (RMSD(pKa) of 2.04 vs. 8.8).
• For residues in less hydrophobic environments, the new algorithm yielded a marginal improvement (RMSD(pKa) of 1.7 vs. 2.1 with SASA).
• Despite improvements, discrepancies remain for some residues, particularly in hydrophobic environments, suggesting SNase presents unique computational challenges.

Conclusions:

• The novel cavity-algorithm offers a substantial improvement over SASA for pK(a) computations, especially in cases involving internal protein cavities.
• The findings highlight the importance of accurate cavity detection for reliable pK(a) predictions in computational protein electrostatics.
• Further investigation is needed to fully resolve discrepancies in challenging protein environments like SNase.