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Calculating pH-dependent free energy of proteins by using Monte Carlo protonation probabilities of ionizable residues
Protein & Cell ; (12): 230-238, 2012.
Article in English | WPRIM | ID: wpr-757283
ABSTRACT
Protein folding, stability, and function are usually influenced by pH. And free energy plays a fundamental role in analysis of such pH-dependent properties. Electrostatics-based theoretical framework using dielectric solvent continuum model and solving Poisson-Boltzmann equation numerically has been shown to be very successful in understanding the pH-dependent properties. However, in this approach the exact computation of pH-dependent free energy becomes impractical for proteins possessing more than several tens of ionizable sites (e.g. > 30), because exact evaluation of the partition function requires a summation over a vast number of possible protonation microstates. Here we present a method which computes the free energy using the average energy and the protonation probabilities of ionizable sites obtained by the well-established Monte Carlo sampling procedure. The key feature is to calculate the entropy by using the protonation probabilities. We used this method to examine a well-studied protein (lysozyme) and produced results which agree very well with the exact calculations. Applications to the optimum pH of maximal stability of proteins and protein-DNA interactions have also resulted in good agreement with experimental data. These examples recommend our method for application to the elucidation of the pH-dependent properties of proteins.
Subject(s)
Full text: Available Index: WPRIM (Western Pacific) Main subject: Protein Binding / Protons / Thermodynamics / DNA / Cathepsin B / Proteins / Muramidase / Chemistry / Monte Carlo Method / Probability Type of study: Health economic evaluation Language: English Journal: Protein & Cell Year: 2012 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Main subject: Protein Binding / Protons / Thermodynamics / DNA / Cathepsin B / Proteins / Muramidase / Chemistry / Monte Carlo Method / Probability Type of study: Health economic evaluation Language: English Journal: Protein & Cell Year: 2012 Type: Article