Influence of the Solvating Environment on Protein Dynamics and Stability

Matthias Heyden became Assistant Professor at Arizona State University in October 2017. Information on his current research is available here. This web page documents the activities of his group at the Max-Planck-Institut für Kohlenforschung (until 2017).

Proteins, the building blocks of life, and in particular enzymes, which catalyze the essential chemical and biochemical processes of life, have been optimized to their specific tasks by evolution. One of the specific adaptations is to work optimally in a specific environment, for example in aqueous solutions with high concentrations of other biopolymers and metabolites or as an integral part of a lipid bilayer.

As a part of the cluster of excellence RESOLV (EXC 1069), which focuses specifically on the effects of solvents on chemical and biochemical processes, we employ in our group molecular dynamics and Monte Carlo simulations to study the influence of the environment on solvated proteins by manipulating the solvent properties in a controlled way. In the current focus of our research are correlations of collective motion in proteins and their solvent, as well as the influence of high protein concentrations on protein folding.

Research Topics:

We analyze the mutual influence between dynamical processes in solvated biomolecules and their surrounding solvent in molecular dynamics simulations. Particularly for water-soluble proteins, vibrational motions in the far-infrared frequency range between 30 and 300 cm-1, i.e. 1 to 10 terahertz, play a special role. In this spectral range we find the intermolecular vibrations of the water hydrogen bond network, as well a multitude of vibrations of the proteins solvated therein. Using cross correlation functions resolved in time and space, we were able to show that the vibrations of the protein and water in the surrounding ... [more]
Based on existing interaction models, which are being used for Brownian dynamics simulations of complex protein solutions with up to 1000 proteins, we are developing in our group simulation methods which allow us to take into account the internal degrees of freedom of the proteins. Due to the use of Monte Carlo sampling, we have to pass on the ability to study dynamical processes in detail. However, our simulations allow us to study equilibria, for example between folded and unfolded states, as well as to analyze ... [more]

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