Human infection with novel SARS-CoV-2 virus was first reported in December of 2019, with the subsequent rapid spread of the virus all over the world. SARS-CoV-2 differs greatly from the rest of coronaviruses in the accessory protein ORF8, which is encoded by one of the most variable parts of the viral genome. With the spread of infection, new variants of the virus and ORF8 emerged that could be found all over the world and also in Slovenia. New variants often result in an altered course of viral infection or COVID-19 disease development, which can be utilised in the research of molecular mechanisms of viral proteins. Many ORF8 interactors have been identified, among others also ADAM9, PLOD2, ITGB1, TGFB1 and IL17RA, which all take part in physiological processes that symptoms of infection with SARS-CoV-2 are attributed to. This research focused on ORF8 modifications S24L, Y73C and L84S, which appeared the most frequently in the earliest variants. Protein interaction analysis consisted of experimental yeast-two-hybrid screening, followed by current computational molecular docking methods, which were the focal point of this thesis. Algorithms I-TASSER and AlphaFold were used for protein structure prediction of unknown structures of target proteins together with experimentally solved structures of the rest of the target proteins, which were then used for protein docking on servers HADDOCK and ClusPro in order to successfully complete our bioinformatic analysis. In the experimental part of this research, certain mutations resulted in deviation from the referential result, which was used as a basis for the hypothesis that the modified amino acids which result in a deviated experimental result, are part of the interaction surface of the protein complex. Our hypothesis has been confirmed by successful modelling of mentioned protein-protein interactions and analysis of the produced models. We therefore showed complementarity of experimental and bioinformatic methods in research of molecular mechanisms of disease.
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