Masking Resonance Artifacts in Force-Splitting Methods for Biomolecular Simulations by Extrapolative Langevin Dynamics

Numerical resonance artifacts have become recognized recently as a limiting factor to increasing the timestep in multiple-timestep (MTS) biomolecular dynamics simulations. At certain timesteps correlated to internal motions (e.g., 5 fs, around half the period of the fastest bond stretch,Tmin), visible inaccuracies or instabilities can occur. Impulse-MTS schemes are vulnerable to these resonance errors since large energy pulses are introduced to the governing dynamics equations when the slow forces are evaluated. We recently showed that such resonance artifacts can be masked significantly by applying extrapolative splitting to stochastic dynamics. Theoretical and numerical analyses of force-splitting integrators based on the Verlet discretization are reported here for linear models to explain these observations and to suggest how to construct effective integrators for biomolecular dynamics that balance stability with accuracy. Analyses for Newtonian dynamics demonstrate the severe resonance patterns of the Impulse splitting, with this severity worsening with the outer timestep, ?t; Constant Extrapolation is generally unstable, but the disturbances do not grow with ?t. Thus, the stochastic extrapolative combination can counteract generic instabilities and largely alleviate resonances with a sufficiently strong Langevin heat-bath coupling (?), estimates for which are derived here based on the fastest and slowest motion periods. These resonance results generally hold for nonlinear test systems: a water tetramer and solvated protein. Proposed related approaches such as Extrapolation/Correction and Midpoint Extrapolation work better than Constant Extrapolation only for timesteps less thanTmin/2. An effective extrapolative stochastic approach for biomolecules that balances long-timestep stability with good accuracy for the fast subsystem is then applied to a biomolecule using a three-class partitioning: the medium forces are treated byMidpoint Extrapolationvia position Verlet, and the slow forces are incorporated byConstant Extrapolation. The resulting algorithm (LN) performs well on a solvated protein system in terms of thermodynamic properties and yields an order of magnitude speedup with respect to single-timestep Langevin trajectories. Computed spectral density functions also show how the Newtonian modes can be approximated by using a small ? in the range of 5?20 ps?1.

[1]  L. Verlet Computer "Experiments" on Classical Fluids. I. Thermodynamical Properties of Lennard-Jones Molecules , 1967 .

[2]  D. Tildesley,et al.  Multiple time-step methods in molecular dynamics , 1978 .

[3]  M. Karplus,et al.  CHARMM: A program for macromolecular energy, minimization, and dynamics calculations , 1983 .

[4]  J. Haile,et al.  A multiple time-step method for molecular dynamics simulations of fluids of chain molecules , 1984 .

[5]  S. Tremaine,et al.  Symmetric Multistep Methods for the Numerical Integration of Planetary Orbits , 1990 .

[6]  Mark E. Tuckerman,et al.  Molecular dynamics algorithm for multiple time scales: Systems with long range forces , 1991 .

[7]  M. Mezei,et al.  A molecular dynamics simulation of a water droplet by the implicit-Euler/Langevin scheme , 1991 .

[8]  Klaus Schulten,et al.  Generalized Verlet Algorithm for Efficient Molecular Dynamics Simulations with Long-range Interactions , 1991 .

[9]  Tamar Schlick,et al.  TNPACK—a truncated Newton minimization package for large-scale problems: II. Implementation examples , 1992, TOMS.

[10]  Tamar Schlick,et al.  TNPACK—A truncated Newton minimization package for large-scale problems: I. Algorithm and usage , 1992, TOMS.

[11]  Mark E. Tuckerman,et al.  Reversible multiple time scale molecular dynamics , 1992 .

[12]  Jan Hermans,et al.  Multiple Time Steps: Limits on the Speedup of Molecular Dynamics Simulations of Aqueous Systems , 1993 .

[13]  Robert D. Skeel,et al.  Dangers of multiple time step methods , 1993 .

[14]  Tamar Schlick,et al.  LIN: A new algorithm to simulate the dynamics of biomolecules by combining implicit‐integration and normal mode techniques , 1993, J. Comput. Chem..

[15]  J. M. Sanz-Serna,et al.  Numerical Hamiltonian Problems , 1994 .

[16]  T. Schlick,et al.  The Langevin/implicit‐Euler/normal‐mode scheme for molecular dynamics at large time steps , 1994 .

[17]  B. Berne,et al.  A new molecular dynamics method combining the reference system propagator algorithm with a fast multipole method for simulating proteins and other complex systems , 1995 .

[18]  T. Schlick,et al.  Resonance in the dynamics of chemical systems simulated by the implicit midpoint scheme , 1995 .

[19]  M. Karplus,et al.  SIMULATIONS OF MACROMOLECULES BY MULTIPLE TIME-STEP METHODS , 1995 .

[20]  Tamar Schlick,et al.  A Family of Symplectic Integrators: Stability, Accuracy, and Molecular Dynamics Applications , 1997, SIAM J. Sci. Comput..

[21]  T Schlick,et al.  Biomolecular dynamics at long timesteps: bridging the timescale gap between simulation and experimentation. , 1997, Annual review of biophysics and biomolecular structure.

[22]  M. Karplus,et al.  Locally accessible conformations of proteins: Multiple molecular dynamics simulations of crambin , 1998, Protein science : a publication of the Protein Society.

[23]  P. Kollman,et al.  Pathways to a protein folding intermediate observed in a 1-microsecond simulation in aqueous solution. , 1998, Science.

[24]  T. Ohwada Higher Order Approximation Methods for the Boltzmann Equation , 1998 .

[25]  R. Skeel,et al.  Nonlinear Resonance Artifacts in Molecular Dynamics Simulations , 1998 .

[26]  T. Schlick,et al.  Extrapolation versus impulse in multiple-timestepping schemes. II. Linear analysis and applications to Newtonian and Langevin dynamics , 1998 .

[27]  Alexander D. MacKerell,et al.  All-atom empirical potential for molecular modeling and dynamics studies of proteins. , 1998, The journal of physical chemistry. B.

[28]  T. Schlick,et al.  Overcoming stability limitations in biomolecular dynamics. I. Combining force splitting via extrapolation with Langevin dynamics in LN , 1998 .

[29]  Robert D. Skeel,et al.  Long-Time-Step Methods for Oscillatory Differential Equations , 1998, SIAM J. Sci. Comput..

[30]  Laxmikant V. Kale,et al.  Algorithmic Challenges in Computational Molecular Biophysics , 1999 .

[31]  Tamar Schlick,et al.  Efficient Implementation of the Truncated-Newton Algorithm for Large-Scale Chemistry Applications , 1999, SIAM J. Optim..

[32]  B. Hingerty,et al.  Effective Computational Strategies for Determining Structures of Carcinogen-Damaged DNA , 1999 .

[33]  Tamar Schlick,et al.  Remark on Algorithm 702—the updated truncated Newton minimization package , 1999, TOMS.