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The influenza virus, also known as "the flu", is an infectious disease that has constantly affected the health of humanity. There is currently no known cure for Influenza. The Center for Innovations in Medicine at the Biodesign Institute located on campus at Arizona State University has been developing synbodies as a possible Influenza therapeutic. Specifically, at CIM, we have attempted to design these initial synbodies to target the entire Influenza virus and preliminary data leads us to believe that these synbodies target Nucleoprotein (NP). Given that the synbody targets NP, the penetration of cells via synbody should also occur. Then by Western Blot analysis we evaluated for the diminution of NP level in treated cells versus untreated cells. The focus of my honors thesis is to explore how synthetic antibodies can potentially inhibit replication of the Influenza (H1N1) A/Puerto Rico/8/34 strain so that a therapeutic can be developed. A high affinity synbody for Influenza can be utilized to test for inhibition of Influenza as shown by preliminary data. The 5-5-3819 synthetic antibody's internalization in live cells was visualized with Madin-Darby Kidney Cells under a Confocal Microscope. Then by Western Blot analysis we evaluated for the diminution of NP level in treated cells versus untreated cells. Expression of NP over 8 hours time was analyzed via Western Blot Analysis, which showed NP accumulation was retarded in synbody treated cells. The data obtained from my honors thesis and preliminary data provided suggest that the synthetic antibody penetrates live cells and targets NP. The results of my thesis presents valuable information that can be utilized by other researchers so that future experiments can be performed, eventually leading to the creation of a more effective therapeutic for influenza.
The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions remain a mystery, knowledge of these properties and interactions could have significant medical applications in terms of understanding cell signaling and immunological defenses. Furthermore, there is evidence that machine learning and peptide microarrays can be used to make reliable predictions of where proteins could interact with each other without the definitive knowledge of the interactions. In this case, a neural network was used to predict the unknown binding interactions of TNFR2 onto LT-ɑ and TRAF2, and PD-L1 onto CD80, based off of the binding data from a sampling of protein-peptide interactions on a microarray. The accuracy and reliability of these predictions would rely on future research to confirm the interactions of these proteins, but the knowledge from these methods and predictions could have a future impact with regards to rational and structure-based drug design.