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p K a predictions for proteins, RNA s, and DNA s with the Gaussian dielectric function using DelPhi p K a

p K a predictions for proteins, RNA s, and DNA s with the Gaussian dielectric function using... ABSTRACT We developed a Poisson‐Boltzmann based approach to calculate the pKavalues of protein ionizable residues (Glu, Asp, His, Lys and Arg), nucleotides of RNA and single stranded DNA. Two novel features were utilized: the dielectric properties of the macromolecules and water phase were modeled via the smooth Gaussian‐based dielectric function in DelPhi and the corresponding electrostatic energies were calculated without defining the molecular surface. We tested the algorithm by calculating pKa values for more than 300 residues from 32 proteins from the PPD dataset and achieved an overall RMSD of 0.77. Particularly, the RMSD of 0.55 was achieved for surface residues, while the RMSD of 1.1 for buried residues. The approach was also found capable of capturing the large pKa shifts of various single point mutations in staphylococcal nuclease (SNase) from pKa‐cooperative dataset, resulting in an overall RMSD of 1.6 for this set of pKa's. Investigations showed that predictions for most of buried mutant residues of SNase could be improved by using higher dielectric constant values. Furthermore, an option to generate different hydrogen positions also improves pKa predictions for buried carboxyl residues. Finally, the pKa calculations on two RNAs demonstrated the capability of this approach for other types of biomolecules. Proteins 2015; 83:2186–2197. © 2015 Wiley Periodicals, Inc. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proteins: Structure Function and Bioinformatics Wiley

p K a predictions for proteins, RNA s, and DNA s with the Gaussian dielectric function using DelPhi p K a

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References (79)

Publisher
Wiley
Copyright
© 2015 Wiley Periodicals, Inc.
ISSN
0887-3585
eISSN
1097-0134
DOI
10.1002/prot.24935
pmid
26408449
Publisher site
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Abstract

ABSTRACT We developed a Poisson‐Boltzmann based approach to calculate the pKavalues of protein ionizable residues (Glu, Asp, His, Lys and Arg), nucleotides of RNA and single stranded DNA. Two novel features were utilized: the dielectric properties of the macromolecules and water phase were modeled via the smooth Gaussian‐based dielectric function in DelPhi and the corresponding electrostatic energies were calculated without defining the molecular surface. We tested the algorithm by calculating pKa values for more than 300 residues from 32 proteins from the PPD dataset and achieved an overall RMSD of 0.77. Particularly, the RMSD of 0.55 was achieved for surface residues, while the RMSD of 1.1 for buried residues. The approach was also found capable of capturing the large pKa shifts of various single point mutations in staphylococcal nuclease (SNase) from pKa‐cooperative dataset, resulting in an overall RMSD of 1.6 for this set of pKa's. Investigations showed that predictions for most of buried mutant residues of SNase could be improved by using higher dielectric constant values. Furthermore, an option to generate different hydrogen positions also improves pKa predictions for buried carboxyl residues. Finally, the pKa calculations on two RNAs demonstrated the capability of this approach for other types of biomolecules. Proteins 2015; 83:2186–2197. © 2015 Wiley Periodicals, Inc.

Journal

Proteins: Structure Function and Bioinformatics – Wiley

Published: Dec 1, 2015

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