Relating kinetic rates and local energetic roughness by accelerated molecular-dynamics simulations.

We show that our accelerated molecular-dynamics (MD) approach can extend the time scale in all-atom MD simulations of biopolymers. We also show that this technique allows for the kinetic rate information to be recaptured. In deducing the kinetic rates, the relationship between the local energetic roughness of the potential-energy landscape and the effective diffusion coefficient is established. These are demonstrated on a very slow but important biomolecular process: the dynamics of cis-trans-isomerization of Ser-Pro motifs. We do not only recapture the slow kinetic rates, which is difficult in traditional MD, but also obtain the underlying roughness of the energy landscape of proteins at atomistic resolution.

[1]  A. Szabó,et al.  Electron transfer reaction dynamics in non-Debye solvents , 1998 .

[2]  J. Mccammon,et al.  Dynamics of Proteins and Nucleic Acids , 2018 .

[3]  A. Voter.  A method for accelerating the molecular dynamics simulation of infrequent events , 1997 .

[4]  J. Tully,et al.  Puddle-skimming: An efficient sampling of multidimensional configuration space , 2002 .

[5]  R. Zwanzig,et al.  Diffusion in a rough potential. , 1988, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Donald Hamelberg,et al.  Phosphorylation effects on cis/trans isomerization and the backbone conformation of serine-proline motifs: accelerated molecular dynamics analysis. , 2005, Journal of the American Chemical Society.

[7]  B. Brooks,et al.  Langevin dynamics of peptides: The frictional dependence of isomerization rates of N‐acetylalanyl‐N′‐methylamide , 1992, Biopolymers.

[8]  K. Wüthrich,et al.  Nmr studies of the rates of proline cis–trans isomerization in oligopeptides , 1981 .

[9]  F. Bovey,et al.  Cis‐Trans equilibrium and kinetic studies of acetyl‐L‐proline and glycyl‐L‐proline , 1977, Biopolymers.

[10]  P. Kollman,et al.  A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules , 1995 .

[11]  A. Voter.  Hyperdynamics: Accelerated Molecular Dynamics of Infrequent Events , 1997 .

[12]  J. Mongan,et al.  Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. , 2004, The Journal of chemical physics.

[13]  John E. Straub,et al.  Time scales and pathways for kinetic energy relaxation in solvated proteins: Application to carbonmonoxy myoglobin , 2000 .

[14]  B. Berne,et al.  Novel methods of sampling phase space in the simulation of biological systems. , 1997, Current opinion in structural biology.

[15]  David A. Case,et al.  Effective Born radii in the generalized Born approximation: The importance of being perfect , 2002, J. Comput. Chem..

[16]  K. Fichthorn,et al.  Accelerated molecular dynamics of infrequent events , 1999 .

[17]  Peter A. Kollman,et al.  AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules , 1995 .

[18]  John W. Wilkins,et al.  Simple bias potential for boosting molecular dynamics with the hyperdynamics scheme , 1998 .

[19]  Grubmüller,et al.  Predicting slow structural transitions in macromolecular systems: Conformational flooding. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.