We present the latest version of the Groningen Molecular Simulation program package, GROMOS05. It has been developed for the dynamical modelling of (bio)molecules using the methods of molecular dynamics, stochastic dynamics, and energy minimization. An overview of GROMOS05 is given, highlighting features not present in the last major release, GROMOS96. The organization of the program package is outlined and the included analysis package GROMOS++ is described. Finally, some applications illustrating the various available functionalities are presented.

Computation based on molecular models is playing an increasingly important role in biology, biological chemistry, and biophysics. Since only a very limited number of properties of biomolecular systems is actually accessible to measurement by experimental means, computer simulation can complement experiment by providing not only averages, but also distributions and time series of any definable quantity, for example, conformational distributions or interactions between parts of systems. Present day biomolecular modeling is limited in its application by four main problems: 1) the force-field problem, 2) the search (sampling) problem, 3) the ensemble (sampling) problem, and 4) the experimental problem. These four problems are discussed and illustrated by practical examples. Perspectives are also outlined for pushing forward the limitations of biomolecular modeling.

Most processes occurring in a system are determined by the relative free energy between two or more states because the free energy is a measure of the probability of finding the system in a given state. When the two states of interest are connected by a pathway, usually called reaction coordinate, along which the free-energy profile is determined, this profile or potential of mean force (PMF) will also yield the relative free energy of the two states. Twelve different methods to compute a PMF are reviewed and compared, with regard to their precision, for a system consisting of a pair of methane molecules in aqueous solution. We analyze all combinations of the type of sampling (unbiased, umbrella-biased or constraint-biased), how to compute free energies (from density of states or force averaging) and the type of coordinate system (internal or Cartesian) used for the PMF degree of freedom. The method of choice is constraint-bias simulation combined with force averaging for either an internal or a Cartesian PMF degree of freedom.

It is proposed to convert nuclear Overhauser effects (NOEs) into relatively precise distances for detailed structural studies of proteins. To this purpose, it is demonstrated that the measurement of NOE buildups between amide protons in perdeuterated human ubiquitin using a designed (15)N-resolved HMQC-NOESY experiment enables the determination of (1)H(N)-(1)H(N) distances up to 5 A with high accuracy and precision. These NOE-derived distances have an experimental random error of approximately 0.07 A, which is smaller than the pairwise rmsd (root-mean-square deviation) of 0.24 A obtained with corresponding distances extracted from either an NMR or an X-ray structure (pdb codes: 1D3Z and 1UBQ), and also smaller than the pairwise rmsd between distances from X-ray and NMR structures (0.15 A). Because the NOE contains both structural and dynamical information, a comparison between the 3D structures and NOE-derived distances may also give insights into through-space dynamics. It appears that the extraction of motional information from NOEs by comparison to the X-ray structure or the NMR structure is challenging because the motion may be masked by the quality of the structures. Nonetheless, a detailed analysis thereof suggests motions between beta-strands and large complex motions in the alpha-helix of ubiquitin. The NOE-derived motions are, however, of smaller amplitude and possibly of a different character than those present in a 20 ns molecular dynamic simulation of ubiquitin in water using the GROMOS force field. Furthermore, a recently published set of structures representing the conformational distribution over time scales up to milliseconds (pdb: 2K39) does not satisfy the NOEs better than the single X-ray structure. Hence, the measurement of possibly thousands of exact NOEs throughout the protein may serve as an excellent probe toward a correct representation of both structure and dynamics of proteins.

Thermodynamic data are often used to calibrate or test atomic‐level (AL) force fields for molecular dynamics (MD) simulations. In contrast, the majority of coarse‐grained (CG) force fields do not rely extensively on thermodynamic quantities. Recently, a CG force field for lipids, hydrocarbons, ions, and water,1 in which approximately four non‐hydrogen atoms are mapped onto one interaction site, has been proposed and applied to study various aspects of lipid systems. To date, no extensive investigation of its capability to describe solvation thermodynamics has been undertaken. In the present study, a detailed picture of vaporization, solvation, and phase‐partitioning thermodynamics for liquid hydrocarbons and water was obtained at CG and AL resolutions, in order to compare the two types of models and evaluate their ability to describe thermodynamic properties in the temperature range between 263 and 343 K. Both CG and AL models capture the experimental dependence of the thermodynamic properties on the temperature, albeit a systematically weaker dependence is found for the CG model. Moreover, deviations are found for solvation thermodynamics and for the corresponding enthalpy–entropy compensation for the CG model. Particularly water/oil repulsion seems to be overestimated. However, the results suggest that the thermodynamic properties considered should be reproducible by a CG model provided it is reparametrized on the basis of these liquid‐phase properties.

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