Development and Application of Computational Modelling Techniques for Enzymatic Processes

Over the past decades there has been an ever-growing recognition of the role of small gas molecules such as O2, CO, NO and H2S as signal transductors. They act as messenger molecules in, among others, the nervous, immune and respiratory systems, and contribute to the regulation of for example metabolic networks, chemotaxis, and mammalian hypoxia responses. Impairment of the signalling network has been linked to cardiovascular, neurodegenerative and inflammatory diseases. This project will significantly enhance the insight into small gaseous molecule-mediated signal transduction by looking at the atomic level of NO triggered signalling, specifically focusing on the soluble guanylyl cyclase (sGC) – a heme protein and the only known receptor of nitric oxide in humans. Most of our knowledge on sGC has been collected using experimental techniques such as site-directed mutagenesis, spectroscopy, X-Ray crystallography, small-angle x-ray scattering, electron microscopy, and chemical cross-linking. Here, we propose to combine computational chemistry approaches to answer the challenging questions set out above that have not been answered by experiments. Such a detailed, atomistic view could significantly contribute to the development of novel drugs. Our main methodology that we will use to efficiently model large enzymatic systems will be the combined quantum mechanics molecular mechanics (QM/MM) method as implemented in the QM/MM program (QoMMMa) maintained in the Leuven group, which is one of the most efficient QM/MM programs in use today.