Open Access

Max Linke

• The diffusive behavior of macromolecules in solution is a key factor in the kinetics of macromolecular binding and assembly, and in the theoretical description of many experiments. Experiments on high-density protein solutions have found that a slow down of the diffusion dynamics is larger than expected from colloidal theory for non-interaction hard-spheres. It has also been shown that the rotational diffusion anisotropy in high-density protein solutions is larger than in dilute ones. High-density protein solutions are a complex fluid that is different from the neat fluid assumption used in the hydrodynamic theory. It is therefore important to have methods to accurately calculate the translational and rotational diffusion tensor from simulations as well as simulation algorithms to explore high-density solutions. Simulations provide a powerful tool to study diffusion in complex fluids. They can be used to study the macroscopic and microscopic effects of complex fluids on the diffusive behavior. There has been already a lot of work done to accurately simulate diffusion and to determine the diffusion coefficients from simulations. The translational diffusion of molecules in simple and complex liquids can be determined with high accuracy from simulations. This is not yet the case for rotational diffusion. Existing algorithms to calculate the rotational diffusion coefficients from simulations make assumptions about the shape of the protein or only work at short times. For the simulation of diffusive behavior of macromolecules two options exist today. An all-atom integrator with explicit solvent molecules or coarse-grained (CG) simulations with an implicit solvent. CG simulations of dynamic behavior with implicit solvent are also called Brownian dynamics (BD) simulations. For the CG simulations the Ermak-McCammon algorithm is often used to solve the underlying Langevin equation. The algorithm is an extension of the Euler-Maruyama integrator to include translation and rotation in three dimensions. This algorithm only correctly reproduces the equilibrium probability for short time-steps and the error depends linearly on the time-step. It has been shown that Monte Carlo based algorithms can produce BD for translational dynamics, when appropriately parametrized. The advantage of Monte Carlo based algorithm is that they will reproduce the correct equilibrium distribution independent of the chosen time-step. This in return allows choosing larger time-steps in simulations. The aim of this thesis is to develop novel´methods to accurately determine the rotational diffusion coefficient from simulations and extend existing Monte Carlo algorithms to include rotational dynamics. The first project addresses the question of how to accurately determine the rotational diffusion coefficients from simulations. We develop a quaternion based method to calculate the rotational diffusion tensor from simulations and a theory for the effects of periodic boundary conditions (PBC) on the rotational diffusion coefficient in simulations. Our method for calculating rotational diffusion coefficients is based on the quaternion covariances from Favro for a freely rotating rigid molecule. The covariances as formulated by Favro are only valid in the principal coordinate system (PCS) of the rotation diffusion tensor. The covariances can be generalized for an arbitrary reference coordinate system (RCS), i.e., a simulation, given the principle axes of the rotational diffusion tensor in the RCS. We show that no prior knowledge of the diffusion tensor and its principal axes is required to calculate the generalized covariances from simulations using common root-mean-square distance (RMSD) procedures. We develop two methods to fit the covariances calculated from simulations to our generalized equations to fit the rotational diffusion tensor. In the first method we minimize the sum of the squared error deviations between model and simulation data. For this six dimensional optimization we use a simulated annealing algorithm. Alternatively the rotational diffusion tensor can also be determined from a eigenvalue decomposition of covariance after integration. To minimize the effects of sampling noise in the integration we first apply a Laplace-transformation to smooth the covariances at large times. For ideal sampling the resulting rotational diffusion coefficient should be independent of the value of the Laplace variable. In practice, however, the best results are achieved using a value close to the inverse autocorrelation time of the rotational motion. ...