Sampling potential energy surface of glycyl glycine peptide: Comparison of Metropolis Monte Carlo and stochastic dynamics

A comparative study was carried out to test the efficiency with which Metropolis Monte Carlo (MC) and stochastic dynamics (SD) sample the potential energy surface of the N‐acetyl glycyl glycine methylamide peptide as defined by the united atom AMBER* force field. Boltzmann‐weighted ensembles were generated with variations of all internal degrees of freedom (i.e., stretch, bend, and torsion) for a single N‐acetyl glycyl glycine methylamide molecule at 300 K by 108‐step MC and 100‐ns SD simulations. As expected, both methods gave the same final energetic results. However, convergence was found to be ∼10 times faster with MC than with SD as measured by comparisons of the populations of all symmetrically equivalent conformers. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 1294–1299, 1998

[1]  The Detailed Balance Energy-scaled Displacement Monte Carlo Algorithm , 1987 .

[2]  M. Rao,et al.  On the force bias Monte Carlo simulation of simple liquids , 1979 .

[3]  William L. Jorgensen,et al.  Monte Carlo vs Molecular Dynamics for Conformational Sampling , 1996 .

[4]  G. Chang,et al.  Macromodel—an integrated software system for modeling organic and bioorganic molecules using molecular mechanics , 1990 .

[5]  Bruce J. Berne,et al.  On a novel Monte Carlo scheme for simulating water and aqueous solutions , 1978 .

[6]  R. Abagyan,et al.  Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins. , 1994, Journal of molecular biology.

[7]  J. Andrew McCammon,et al.  Free energy difference calculations by thermodynamic integration: Difficulties in obtaining a precise value , 1991 .

[8]  W. C. Still,et al.  A Smart Monte Carlo Technique for Free Energy Simulations of Multiconformational Molecules. Direct Calculations of the Conformational Populations of Organic Molecules , 1995 .

[9]  A simple new way to help speed up Monte Carlo convergence rates: Energy‐scaled displacement Monte Carlo , 1983 .

[10]  J. D. Doll,et al.  Brownian dynamics as smart Monte Carlo simulation , 1978 .

[11]  Peter A. Kollman,et al.  FREE ENERGY CALCULATIONS : APPLICATIONS TO CHEMICAL AND BIOCHEMICAL PHENOMENA , 1993 .

[12]  W. Clark Still,et al.  A rapidly convergent simulation method: Mixed Monte Carlo/stochastic dynamics , 1994, J. Comput. Chem..

[13]  W F van Gunsteren,et al.  On the interpretation of biochemical data by molecular dynamics computer simulation. , 1992, European journal of biochemistry.

[14]  Andy Brass,et al.  Hybrid Monte Carlo: An efficient algorithm for condensed matter simulation , 1994, J. Comput. Chem..

[15]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[16]  Wilfred F. van Gunsteren,et al.  CONVERGENCE PROPERTIES OF FREE ENERGY CALCULATIONS : ALPHA -CYCLODEXTRIN COMPLEXES AS A CASE STUDY , 1994 .

[17]  Hanoch Senderowitz,et al.  Carbohydrates: United Atom AMBER* Parameterization of Pyranoses and Simulations Yielding Anomeric Free Energies , 1996 .

[18]  B. Berne,et al.  Monte Carlo methods for accelerating barrier crossing: Anti-force-bias and variable step algorithms , 1990 .

[19]  W. Clark Still,et al.  A Quantum Mechanically Derived All-Atom Force Field for Pyranose Oligosaccharides. AMBER* Parameters and Free Energy Simulations , 1997 .

[20]  William L. Jorgensen,et al.  Theoretical studies of medium effects on conformational equilibria , 1983 .

[21]  W. Clark Still,et al.  An unbounded systematic search of conformational space , 1991 .

[22]  Ernest L. Eliel,et al.  Stereochemistry of Organic Compounds , 1962 .

[23]  U. Singh,et al.  A NEW FORCE FIELD FOR MOLECULAR MECHANICAL SIMULATION OF NUCLEIC ACIDS AND PROTEINS , 1984 .

[24]  Michael Kotelyanskii,et al.  A dynamic Monte Carlo method suitable for molecular simulations , 1992 .

[25]  N Go,et al.  Efficient monte carlo method for simulation of fluctuating conformations of native proteins , 1985, Biopolymers.

[26]  J. Mccammon,et al.  Simulation methods for protein structure fluctuations , 1980, Biopolymers.

[27]  M. Rao,et al.  On the force bias Monte Carlo simulation of water: methodology, optimization and comparison with molecular dynamics , 1979 .