Proteins and enzymes have evolved for optimal function in specific environments. These environments can be dominated by water, i.e. in the cytosol or in extracellular fluids, but also by lipids, i.e. for integral membrane proteins. Further, proteins are rarely found in dilute solution, instead the biomolecular packing in biologically relevant systems often reaches 40% w/w. We employ two types of simulations to study the interactions of biomolecules with their respective solvating environment. 

In atomistic molecular dynamics simulations, we analyze the interactions of biomolecular solutes with water. We identify the three-dimensional fingerprint that a protein creates in its immediate solvent environment by affecting the local thermodynam-ic properties of water and by slowing down dynamical processes in the solvent, such as diffusion and rotational relaxation. Increased orientational ordering of water can be observed extending several hydration layers from the protein-water inter-face, reminiscent of the average electrostatic field. 

In addition, we analyze frequency-dependent intermolecular vibrations of the protein solute and its solvent via spatially resolved velocity cross correlation functions. These allow us to visualize propagating collective motions in the water HB network, that are dynamically coupled to fluctuations on the protein surface. 

To study effects of protein-protein interactions in crowded environments, such as the interior of a cell, we employ implicit solvent Monte Carlo simulations. Using potentials developed for rigid-body Brownian dynamics simulations, we implemented a conformational ensemble Monte Carlo approach, which allows for conformational flexibility during the simulation at no additional computational cost. The approach can be used to study the aggregation of stable proteins, as well as the relative stability of folded and unfolded states as a function of the environment, e.g. the degree of biomolecular crowding.