Framework‐based design of a new all‐purpose molecular simulation application: The Adun simulator

Here we present Adun, a new molecular simulator that represents a paradigm shift in the way scientific programs are developed. The traditional algorithm centric methods of scientific programming can lead to major maintainability and productivity problems when developing large complex programs. These problems have long been recognized by computer scientists; however, the ideas and techniques developed to deal with them have not achieved widespread adoption in the scientific community. Adun is the result of the application of these ideas, including pervasive polymorphism, evolutionary frameworks, and refactoring, to the molecular simulation domain. The simulator itself is underpinned by the Adun Framework, which separates the structure of the program from any underlying algorithms, thus giving a completely reusable design. The aims are twofold. The first is to provide a platform for rapid development and implementation of different simulation types and algorithms. The second is to decrease the learning barrier for new developers by providing a rigorous and well‐defined structure. We present some examples on the use of Adun by performing simple free‐energy simulations for the adiabatic charging of a single ion, using both free‐energy perturbation and the Bennett's method. We also illustrate the power of the design by detailing the ease with which ASEP/MD, an elaborated mean field QM/MM method originally written in FORTRAN 90, was implemented into Adun. © 2005 Wiley Periodicals, Inc. J Comput Chem 26: 1647–1659, 2005

[1]  Ralph E. Christoffersen,et al.  Algorithms for Chemical Computations , 1977 .

[2]  G. Torrie,et al.  Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling , 1977 .

[3]  M. A. Aguilar,et al.  Study of solvent effects by means of averaged solvent electrostatic potentials obtained from molecular dynamics data , 1997 .

[4]  Jacopo Tomasi,et al.  Molecular Interactions in Solution: An Overview of Methods Based on Continuous Distributions of the Solvent , 1994 .

[5]  A. Warshel,et al.  Examining methods for calculations of binding free energies: LRA, LIE, PDLD‐LRA, and PDLD/S‐LRA calculations of ligands binding to an HIV protease , 2000, Proteins.

[6]  David A. Kofke,et al.  Appropriate methods to combine forward and reverse free-energy perturbation averages , 2003 .

[7]  F.J.Olivares del Valle,et al.  ASEP/MD: A program for the calculation of solvent effects combining QM/MM methods and the mean field approximation ☆ , 2003 .

[8]  G. Crooks Path-ensemble averages in systems driven far from equilibrium , 1999, cond-mat/9908420.

[9]  Arieh Warshel,et al.  Microscopic and semimicroscopic calculations of electrostatic energies in proteins by the POLARIS and ENZYMIX programs , 1993, J. Comput. Chem..

[10]  J. Aqvist,et al.  A new method for predicting binding affinity in computer-aided drug design. , 1994, Protein engineering.

[11]  T. Schlick Molecular modeling and simulation , 2002 .

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

[13]  Kent L. Beck,et al.  Extreme programming explained - embrace change , 1990 .

[14]  Ralph Johnson,et al.  design patterns elements of reusable object oriented software , 2019 .

[15]  Charles H. Bennett,et al.  Efficient estimation of free energy differences from Monte Carlo data , 1976 .

[16]  Arieh Warshel,et al.  Computer Modeling of Chemical Reactions in Enzymes and Solutions , 1991 .

[17]  Arieh Warshel,et al.  A surface constrained all‐atom solvent model for effective simulations of polar solutions , 1989 .

[18]  C. Jarzynski Nonequilibrium Equality for Free Energy Differences , 1996, cond-mat/9610209.

[19]  Arieh Warshel,et al.  A local reaction field method for fast evaluation of long‐range electrostatic interactions in molecular simulations , 1992 .