The application of molecular dynamics simulation techniques and free energy calculations to predict protein-protein and protein-ligand interactions

Proteins are the media through which genetic information is expressed. They are involved in most if not all biological processes, from receptor activation to cellular process regulation or even chemical reaction catalysis. This diversity of activities is made possible because proteins come in all sizes and shapes (their structures). In most cases, each protein has one unique structure (the native structure) which is directly related to its function. Knowing the structure of a proteins is therefore of great importance for the understanding of its function or of the mechanism by which the function is carried out. However the structure of a protein cannot be determined solely from its sequence. Instead, the structure can be obtained experimentally for certain proteins either by x-ray crystallography or by NMR spectroscopy. These experimental methods cannot provide detailed information on the dynamical properties of a protein and therefore only provide very limited insights on the folding process itself. Molecular dynamics (MD) simulations can be used to study the dynamical properties of a system in full atomic details. Thus MD simulations can be used to gain a better understanding of the interactions between proteins and between proteins and ligands in order to predict how proteins or some of their elements associate with one another to achieve their lowest free energy conformation. The ability to accurately determine differences in free energy is therefore of great practical interest in biophysics and structural biology as it would allow the prediction of phenomena such as conformational changes or protein-ligand interactions. Free energy differences can be calculated from numerical simulations using a variety of statistical mechanical approaches. The accuracy of such calculations is primarily limited by two factors, the nature of the underlying model or force field and the extent of the sampling during the simulation ... Zie: Summar