Introduction to Molecular Dynamics with GROMACS

SummaryMolecular simulation is a very powerful toolbox in modern molecular modeling, and enables us tofollow and understand structure and dynamics with extreme detail – literally on scales where motion ofindividual atoms can be tracked. This task will focus on the two most commonly used methods, namelyenergy minimization and molecular dynamics that respectively optimize structure and simulate thenatural motion of biological macromolecules. we are first going to set up your Gromacs environments,have a look at the structure, prepare the input files necessary for simulation, solvate the structure in water,minimize & equilibrate it, and finally perform a short production simulation. After this we’ll testsome simple analysis programs that are part of Gromacs.You should write a brief report about your work and describe your findings. In particular, try tothink about why you are using particular algorithms/choices, and whether there are alternatives.Molecular motions occur over a wide range of scales in both time and space, and the choice of approachto study them depends on the question asked. Molecular simulation is far from the only availablemethod, and when the aim e.g. is to predict the structure of a protein it is often more efficient touse bioinformatics instead of spending thousands or millions of CPU hours. Ideally, the timedependentSchrödinger equation should be able to predict all properties of any molecule with arbitraryprecision ab initio. However, as soon as more than a handful of particles are involved it is necessary tointroduce approximations. For most biomolecular systems we therefore choose to work with empiricalparameterizations of models instead, for instance classical Coulomb interactions between pointlikeatomic charges rather than a quantum description of the electrons. These models are not only orders ofmagnitude faster, but since they have been parameterized from experiments they also perform betterwhen it comes to reproducing observations on microsecond scale (Fig. 1), rather than extrapolatingquantum models 10 orders of magnitude. The first molecular dynamics simulation was performed aslate as 1957, although it was not until the 1970’s that it was possible to simulate water and biomolecules.Background & TheoryMacroscopic properties measured in an experiment are not direct observations, but averages over billionsof molecules representing a statistical mechanics ensemble. This has deep theoretical implicationsthat are covered in great detail in the literature, but even from a practical point of view there are importantconsequences: (i) It is not sufficient to work with individual structures, but systems have to be expandedto generate a representative ensemble of structures at the given experimental conditions, e.g.temperature and pressure. (ii) Thermodynamic equilibrium properties related to free energy, such asbinding constant, solubilities, and relative stability cannot be calculated directly from individual simulations,but require more elaborate techniques. (iii) For equilibrium properties (in contrast to kinetic) theaim is to examine structure ensembles, and not necessarily to reproduce individual atomic trajectories!The two most common ways to generate statistically faithful equilibrium ensembles are Monte Carloand Molecular Dynamics simulations. Monte Carlo simulations rely on designing intelligent moves togenerate new conformations, but since these are fairly difficult to invent most simulations tend to doclassical dynamics with Newton’s equations of motion since this also has the advantage of accuratelyreproducing kinetics of non-equilibrium properties such as diffusion or folding times. When a startingconfiguration is very far from equilibrium, large forces can cause the simulation to crash or distort thesystem, and for this reason it is usually necessary to start with an Energy Minimization of the systemprior to the molecular dynamics simulation. In addition, energy minimizations are commonly used torefine low-resolution experimental structures.All classical simulation methods rely on more or less empirical approximations called Force fields tocalculate interactions and evaluate the potential energy of the system as a function of point-like atomiccoordinates. A force field consists of both the set of equations used to calculate the potential energy andforces from particle coordinates, as well as a collection of parameters used in the equations.