Can the protonation state of histidine residues be determined from molecular dynamics simulations

Histidine (His) residues in proteins can attain three different protonation states at normal pH. This constitutes a prominent problem when adding protons to a protein crystal structure, e.g. in order to perform molecular simulations. Typically, the His protonation is deduced from the hydrogen-bond pattern in crystal structures. Here, we study whether it is possible to detect erroneous His protonation state by analysing short molecular dynamics (MD) trajectories. We systematically vary the His protonation state and measure the root-mean-squared deviation (RMSD) of the His residues and nearby residues relative to the starting structure, as well as the distribution of the dihedral angle that determines the rotation of the His side chain. We study three proteins, hisactophilin with 31 solvent-exposed His residues, galectin-3, for which an experimental assignment is available for two of the His residues, and trypsin, for which the hydrogen-bond analysis is quite conclusive. The results show that improper protonation states have larger RMSD values and larger widths of the dihedral distribution, compared to the correct protonation states. Unfortunately, the variation among different His residues in the same and different proteins is so large that it is hard to define unambiguous thresholds between proper and improper protonation states. Therefore, simulations of all three protonation states are needed for conclusive results. For trypsin, we could obtain a conclusive assignment for all three His residues, which was better than the simple hydrogen-bond analysis. For galectin-3, the MD trajectories confirmed the results of hydrogen-bond analysis and experiments. They also gave additional, more uncertain information for some of the residues. However, for the solvent-exposed His residues in hisactophilin, no unambiguous conclusions regarding the protonation states could be reached. On the other hand, this indicates that protein structures are quite insensitive to the protonation state of the His residues, besides those that involve direct hydrogen bonds to the His side chain. (C) 2012 Elsevier B.V. All rights reserved. (Less)

[1]  T. Darden,et al.  Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems , 1993 .

[2]  Samuel Genheden,et al.  How to obtain statistically converged MM/GBSA results , 2009, J. Comput. Chem..

[3]  T. Holak,et al.  Structure of hisactophilin is similar to interleukin-1β and fibroblast growth factor , 1994, Nature.

[4]  Junmei Wang,et al.  Development and testing of a general amber force field , 2004, J. Comput. Chem..

[5]  Riccardo Chelli,et al.  Energetic Fitness of Histidine Protonation States in PDB Structures , 2004 .

[6]  Samuel Genheden,et al.  Starting-Condition Dependence of Order Parameters Derived from Molecular Dynamics Simulations. , 2010, Journal of chemical theory and computation.

[7]  Jan H. Jensen,et al.  PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa Predictions. , 2011, Journal of chemical theory and computation.

[8]  G. Rabinovich,et al.  Galectins: regulators of acute and chronic inflammation , 2010, Annals of the New York Academy of Sciences.

[9]  Samuel Genheden,et al.  A comparison of different initialization protocols to obtain statistically independent molecular dynamics simulations , 2011, J. Comput. Chem..

[10]  Lars Olsen,et al.  Binding affinities in the SAMPL3 trypsin and host–guest blind tests estimated with the MM/PBSA and LIE methods , 2012, Journal of Computer-Aided Molecular Design.

[11]  B. Brooks,et al.  Self-guided Langevin dynamics simulation method , 2003 .

[12]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[13]  J. Richardson,et al.  Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. , 1999, Journal of molecular biology.

[14]  Kevin J. Bowers,et al.  A maximum likelihood method for linking particle-in-cell and Monte-Carlo transport simulations , 2004, Comput. Phys. Commun..

[15]  E. Knapp,et al.  Electrostatic models for computing protonation and redox equilibria in proteins , 1999, European Biophysics Journal.

[16]  Sarah L. Williams,et al.  Progress in the prediction of pKa values in proteins , 2011, Proteins.

[17]  G. Ciccotti,et al.  Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes , 1977 .

[18]  W. L. Jorgensen,et al.  Comparison of simple potential functions for simulating liquid water , 1983 .

[19]  B. Efron.  Nonparametric estimates of standard error: The jackknife, the bootstrap and other methods , 1981 .

[20]  P. Kollman,et al.  A well-behaved electrostatic potential-based method using charge restraints for deriving atomic char , 1993 .

[21]  V. Hornak,et al.  Comparison of multiple Amber force fields and development of improved protein backbone parameters , 2006, Proteins.

[22]  Olof Engström,et al.  Protein Flexibility and Conformational Entropy in Ligand Design Targeting the Carbohydrate Recognition Domain of Galectin-3 , 2010, Journal of the American Chemical Society.

[23]  C. Sander,et al.  Errors in protein structures , 1996, Nature.

[24]  H. Berendsen,et al.  Molecular dynamics with coupling to an external bath , 1984 .

[25]  S. McSweeney,et al.  Atomic resolution structures of trypsin provide insight into structural radiation damage. , 2001, Acta crystallographica. Section D, Biological crystallography.

[26]  P. Kollman,et al.  Atomic charges derived from semiempirical methods , 1990 .

[27]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .