Title : Improved diagnostic and therapeutic strategies against SARS CoV2 by blood pH analysis of Covid19 patients in combination with molecular modeling and NMR studies
Abstract:
The global outbreak of SARS CoV2/ Covid19 is a great challenge for new concepts and strategies in different fields as glycobiology, nanomedicine or nanopharmacology. When correlating clinical data obtained from patients in intense care units with tools used in structural biology such as NMR and molecular modelling it is possible to develop new diagnostic and therapeutic strategies against SARS CoV2 and other viral infections (e.g. influenza) with a pandemic potential. At first, we have figured out in which way clinical data, in our case, pH value alterations can be directly linked to distinct structure related questions on a sub-molecular level. The effects of biophysical parameters such as temperature, pH value variation and membrane characteristics e.g. peptide solubility as well as the affinity of certain amino acids to sialic acids and sulfated carbohydrates provide helpful hints to identify a potential Achilles heel of SARS-CoV2 infections. In silico molecular modelling calculations and in vitro NMR experiments (including 31P NMR measurements) have been applied to analyse the structural behavior when potential antiviral peptides are encapsulted by dodecylphosphocholine (DPC). Since lectin-like interactions of sialic acid molecules play an important role when the blocking properties of inhibitory peptides are evaluated DPC micelles were mixed with gangliosides and analysed under physiological conditions with NMR methods. Thereby, we are able to test in which way SARS CoV fusion peptides and potential inhibitory SARS-CoV2 fusion peptides are interacting with phospholipid membranes and gangliosides in a specific way. We have found that the specific interactions of certain collagen fragments with certain SARS CoV2 structures which are involved in the stabilization of the blood brain barrier can be triggered by the application of incretin-mimetics.