Improving pKa calculations with consideration of hydration entropy

Area of Science:

• Computational Biochemistry
• Protein Electrostatics
• Macromolecular Structure-Function Relationships

Background:

• Continuum dielectric modeling is crucial for studying protein electrostatics and structure-function relationships.
• Current models often struggle to accurately predict the pKa values of ionizable residues.
• Accurate pKa prediction is vital for understanding protein function and interactions.

Purpose of the Study:

• To improve the accuracy of continuum dielectric models for calculating protein pKas.
• To incorporate the entropic contribution of first hydration shell solvent ordering into electrostatic models.
• To refine pKa calculations for specific ionizable groups like cysteines, aspartic acids, and glutamic acids.

Main Methods:

• Developed a modified continuum dielectric model.
• Included a new term estimating the entropy of first hydration shell solvent ordering.
• Applied the model to compute pKas for cysteines in DsbA and thioredoxin, and for aspartic/glutamic acids in various proteins.

Main Results:

• The enhanced model shows improved accuracy in calculated pKas compared to standard models.
• The modification, based on charge burial geometry and adjustable hydration numbers, refines electrostatic predictions.
• Demonstrated the model's effectiveness on specific protein examples and amino acid types.

Conclusions:

• Incorporating hydration shell entropy significantly enhances the accuracy of pKa calculations in continuum dielectric models.
• The proposed modification offers a promising framework for more precise electrostatic modeling of macromolecules.
• Future work can further refine the model using experimentally determined, group-specific hydration numbers.