Concerted simulations reveal how peroxidase compound III formation results in cellular oscillations.

A major problem in mathematical modeling of the dynamics of complex biological systems is the frequent lack of knowledge of kinetic parameters. Here, we apply Brownian dynamics simulations, based on protein three-dimensional structures, to estimate a previously undetermined kinetic parameter, which is then used in biochemical network simulations. The peroxidase-oxidase reaction involves many elementary steps and displays oscillatory dynamics important for immune response. Brownian dynamics simulations were performed for three different peroxidases to estimate the rate constant for one of the elementary steps crucial for oscillations in the peroxidase-oxidase reaction, the association of superoxide with peroxidase. Computed second-order rate constants agree well with available experimental data and permit prediction of rate constants at physiological conditions. The simulations show that electrostatic interactions depress the rate of superoxide association with myeloperoxidase, bringing it into the range necessary for oscillatory behavior in activated neutrophils. Such negative electrostatic steering of enzyme-substrate association presents a novel control mechanism and lies in sharp contrast to the electrostatically-steered fast association of superoxide and Cu/Zn superoxide dismutase, which is also simulated here. The results demonstrate the potential of an integrated and concerted application of structure-based simulations and biochemical network simulations in cellular systems biology.

[1]  John A. Tainer,et al.  Structure and mechanism of copper, zinc superoxide dismutase , 1983, Nature.

[2]  J. Mccammon,et al.  Point charge distributions and electrostatic steering in enzyme/substrate encounter: Brownian dynamics of modified copper/zinc superoxide dismutases. , 1990, Biochemistry.

[3]  Alexander Scheeline,et al.  The Peroxidase−Oxidase Oscillator and Its Constituent Chemistries , 1997 .

[4]  E. Getzoff,et al.  Faster superoxide dismutase mutants designed by enhancing electrostatic guidance , 1992, Nature.

[5]  Rebecca C. Wade,et al.  Improving the Continuum Dielectric Approach to Calculating pKas of Ionizable Groups in Proteins , 1996 .

[6]  J. Mccammon,et al.  Brownian dynamics simulation of diffusion‐influenced bimolecular reactions , 1984 .

[7]  J. Hajdu,et al.  The catalytic pathway of horseradish peroxidase at high resolution , 2002, Nature.

[8]  Ursula Kummer,et al.  A model of the oscillatory metabolism of activated neutrophils. , 2003, Biophysical journal.

[9]  A. Desideri,et al.  Simulation of superoxide-superoxide dismutase association rate for six natural variants. Comparison with the experimental catalytic rate , 1994 .

[10]  M Karplus,et al.  Temperature dependence of the structure and dynamics of myoglobin. A simulation approach. , 1990, Journal of molecular biology.

[11]  J. Tainer,et al.  The role of arginine 143 in the electrostatics and mechanism of Cu, Zn superoxide dismutase: Computational and experimental evaluation by mutational analysis , 1994, Proteins.

[12]  J A McCammon,et al.  Computer simulation of protein-protein association kinetics: acetylcholinesterase-fasciculin. , 1999, Journal of molecular biology.

[13]  D. Ermak,et al.  Brownian dynamics with hydrodynamic interactions , 1978 .

[14]  L. Olsen,et al.  Oscillations in the peroxidase-oxidase reaction: a comparison of different peroxidases. , 1996, Biochimica et biophysica acta.

[15]  H. Petty,et al.  Apparent role of traveling metabolic waves in oxidant release by living neutrophils , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[16]  H. Petty,et al.  Complement deposition on immune complexes reduces the frequencies of metabolic, proteolytic, and superoxide oscillations of migrating neutrophils. , 1999, Cellular immunology.

[17]  R. Wade,et al.  pKa calculations for class A beta-lactamases: methodological and mechanistic implications. , 1997, Biophysical journal.

[18]  R C Wade,et al.  Protein-protein association: investigation of factors influencing association rates by brownian dynamics simulations. , 2001, Journal of molecular biology.

[19]  A. Kettle,et al.  A pulse radiolysis investigation of the reactions of myeloperoxidase with superoxide and hydrogen peroxide. , 1988, Biochimica et biophysica acta.

[20]  R C Wade,et al.  Brownian dynamics simulation of protein-protein diffusional encounter. , 1998, Methods.

[21]  A. Desideri,et al.  Toward the engineering of a super efficient enzyme. , 1999, Biochemical and biophysical research communications.

[22]  J. Tainer,et al.  Novel dimeric interface and electrostatic recognition in bacterial Cu,Zn superoxide dismutase. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[23]  C. Sander,et al.  Positioning hydrogen atoms by optimizing hydrogen‐bond networks in protein structures , 1996, Proteins.

[24]  Chung F. Wong,et al.  Partial electrostatic charges for the active center of Cu, Zn superoxide dismutase , 1990 .

[25]  L. Olsen,et al.  Melatonin activates the peroxidase-oxidase reaction and promotes oscillations. , 2001, Biochemical and biophysical research communications.

[26]  H. Hayasawa,et al.  Mutations affecting the calcium-binding site of myeloperoxidase and lactoperoxidase. , 2001, Biochemical and biophysical research communications.

[27]  I. Morishima,et al.  Presence of endogenous calcium ion and its functional and structural regulation in horseradish peroxidase. , 1986, The Journal of biological chemistry.

[28]  R C Wade,et al.  Electrostatic steering and ionic tethering in enzyme-ligand binding: insights from simulations. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[29]  I. Yamazaki,et al.  Sustained Oscillations in a Lactoperoxidase, NADPH and O2 System , 1969, Nature.

[30]  M. Smoluchowski.  Versuch einer mathematischen Theorie der Koagulationskinetik kolloider Lösungen , 1918 .

[31]  W. L. Jorgensen,et al.  The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. , 1988, Journal of the American Chemical Society.

[32]  U Kummer,et al.  Oscillatory dynamics protect enzymes and possibly cells against toxic substances. , 2002, Faraday discussions.