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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Published on: July 25, 2013

Rapid calculation of protein pKa values using Rosetta.

Krishna Praneeth Kilambi¹, Jeffrey J Gray²

¹Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland.

Biophysical Journal

|September 6, 2012

Summary

We developed a fast Monte Carlo method using Rosetta to predict protein residue pK(a) values. This approach accurately estimates protonation states, aiding protein design and function studies.

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Area of Science:

Computational Biology
Biophysics
Protein Chemistry

Background:

Accurate prediction of protein residue pK(a) values is crucial for understanding protein function and interactions.
Standard computational methods often struggle to capture the environmental influences on residue protonation states.

Purpose of the Study:

To develop and validate a Rosetta-based Monte Carlo method for calculating pK(a) values of key protein residues.
To assess the impact of side-chain and backbone flexibility on pK(a) prediction accuracy.

Main Methods:

A Rosetta-based Monte Carlo simulation was employed.
The method incorporated Coulomb electrostatic potential and optimized solvation energies.
Side-chain flexibility was included, with further analysis of proximal residue conformational freedom and backbone flexibility.

Main Results:

The refined method achieved a root mean-square deviation (RMSD) of 0.83 from experimental pK(a) values.
Accounting for side-chain flexibility yielded an RMSD of 0.85, while adding backbone flexibility resulted in an RMSD of 0.93.
The method successfully predicted large pK(a) shifts in specific mutations, like those in staphylococcal nuclease.

Conclusions:

A simple, fast Rosetta-based method with limited conformational sampling accurately predicts protein residue pK(a) values.
This computational approach is valuable for rapid pK(a) estimation in applications like protein docking, design, and folding studies.