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We present the newest version of the GROningen MOlecular Simulation program package, GROMOS96. GROMOS96 has been developed for the dynamic modelling of (bio)molecules using the methods of molecular dynamics, stochastic dynamics, and energy minimization as well as the path-integral formalism. An overview of its functionality is given, highlighting methodology not present in the last major release, GROMOS87. The organization of the code is outlined, and reliability, testing, and efficiency issues involved in the design of this large (73 000 lines of FORTRAN77 code) and complex package are discussed. Finally, we present two applications illustrating new functionality: local elevation simulation and molecular dynamics in four spatial dimensions.

We have investigated the feasibility of predicting free energy differences between a manifold of molecular states from a single simulation or ensemble representing one reference state. Two formulas that are based on the so-called λ- coupling parameter approach are analyzed and compared:  (i) expansion of the free energy F(λ) into a Taylor series around a reference state (λ = 0), and (ii) the so-called free energy perturbation formula. The results obtained by these extrapolation methods are compared to exact (target) values calculated by thermodynamic integration for mutations in two molecular systems:  a model dipolar diatomic molecule in water, and a series of para-substituted phenols in water. For moderate charge redistribution (≈0.5 e), both extrapolation methods reproduce the exact free energy differences. For free energy changes due to a change of atom type or size, the Taylor expansion method fails completely, while the perturbation formula yields moderately accurate predictions. Both extrapolation methods fail when a mutation involves the creation or deletion of atoms, due to the poor sampling in the reference state simulation of the configurations that are important in the end states of interest. To overcome this sampling difficulty, a procedure based on the perturbation formula and on biasing the sampling in the reference state is proposed, in which soft-core interaction sites are incorporated into the Hamiltonian of the reference state at positions where atoms are to be created or deleted. For mutations going from p-methylphenol to the other five differently para-substituted phenols, the differences in free energy are correctly predicted using extrapolation based on a single simulation of a biased, non-physical reference state. Since a large number of mutations can be investigated using a recorded trajectory of a single simulation, the proposed method is potentially viable in practical applications such as drug design.

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Azurin from Pseudomonas aeruginosa is a small 128-residue, copper-containing protein. Its redox potential can be modified by mutating the protein. Free-energy calculations based on classical molecular-dynamics simulations of the protein and from mutants in aqueous solution at different pH values were used to compute relative redox potentials. The precision of the free-energy calculations with the lambda coupling-parameter approach is evaluated as function of the number and sequence of lambda values, the sampling time and initial conditions. It is found that the precision is critically dependent on the relaxation of hydrogen-bonding networks when changing the atomic-charge distribution due to a change of redox state or pH value. The errors in the free energies range from 1 to 10 k(B)T, depending on the type of process. Only qualitative estimates of the change in redox potential by protein mutation can be obtained.