Taboo-based Monte Carlo Search as a Method to Improve Sampling Efficiency

Abstract Taboo-based Monte Carlo search which restricts the sampling of the region near an old configuration, is developed. In this procedure, Monte Carlo simulation and random search method are combined to improve the sampling efficiency. The feasibility of this method is tested on global optimization of a continuous model function, melting of the 256 Lennard-Jones particles at T∗ = 0.680 and ρ∗ = 0.850 and polypeptides (alanine dipeptide and Metenkephalin). From the comparison of results for the model function between our method and other methods, we find the increase of convergence rate and the high possibility of escaping from the local energy minima. The results of the Lennard-Jones solids and polypeptides show that the convergence property to reach the equilibrium state is better than that of others. It is also found that no significant bias in ensemble distribution is detected, though taboo-based Monte Carlo search does not sample the correct ensemble distribution owing to the restriction of the sa...

[1]  R. Hicks,et al.  Conformational analysis of met‐enkephalin in both aqueous solution and in the presence of sodium dodecyl sulfate micelles using multidimensional NMR and molecular modeling , 1992, Biopolymers.

[2]  Mu Shik Jhon,et al.  Acceleration of convergence rates by high directional Monte Carlo sampling procedure , 1992 .

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

[4]  Mu Shik Jhon,et al.  Valley Restrained Monte Carlo Procedure as a Method to Improve Sampling Efficiency , 1999 .

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

[6]  Jae Kwang Shin,et al.  High directional Monte Carlo procedure coupled with the temperature heating and annealing as a method to obtain the global energy minimum structure of polypeptides and proteins , 1991, Biopolymers.

[7]  Seung Do Hong,et al.  Restricted random search method based on taboo search in the multiple minima problem , 1997 .

[8]  H. Scheraga,et al.  Application of the diffusion equation method for global optimization to oligopeptides , 1992 .

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

[10]  H. Scheraga,et al.  On the multiple‐minima problem in the conformational analysis of polypeptides. II. An electrostatically driven Monte Carlo method—tests on poly(L‐alanine) , 1988, Biopolymers.

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

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

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

[14]  Arnold T. Hagler,et al.  COMPUTER SIMULATION OF THE CONFORMATIONAL PROPERTIES OF OLIGOPEPTIDES. COMPARISON OF THEORETICAL METHODS AND ANALYSIS OF EXPERIMENTAL RESULTS , 1979 .

[15]  J. D. Doll,et al.  Extending J walking to quantum systems: Applications to atomic clusters , 1992 .

[16]  Timothy F. Havel.  An evaluation of computational strategies for use in the determination of protein structure from distance constraints obtained by nuclear magnetic resonance. , 1991, Progress in biophysics and molecular biology.

[17]  H. A. Scheraga,et al.  Application of the diffusion equation method of global optimization to water clusters , 1992 .

[18]  A. K. Rattan,et al.  Met-enkephalin alteration in the rat during chronic injection of morphine and/or midazolam , 1997, Brain Research.

[19]  M. Karplus,et al.  Crystallographic R Factor Refinement by Molecular Dynamics , 1987, Science.

[20]  C. F. Curtiss,et al.  Molecular Theory Of Gases And Liquids , 1954 .

[21]  Jacek Klinowski,et al.  Taboo Search: An Approach to the Multiple Minima Problem , 1995, Science.

[22]  Mu Shik Jhon,et al.  Determination of C‐terminal structure of human CHaras oncogenic protein , 1995, J. Comput. Chem..

[23]  Mu Shik Jhon,et al.  Use of the High Directional Monte Carlo Method to Predict the Low Energy Structures of Melittin , 1995 .