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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.
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The experiments in this study observed a correlation between the growth rate of uninfected cultures and the decay rate of infected cultures, meaning that temperatures that cultures that displayed a higher growth rate for uninfected M. vaginatus would die faster when infected with the predatory bacterium. Infected cultures that were incubated at temperatures 4 and 10 °C did not display death and this could have been due to lower activity of M. vaginatus at lower temperatures or the inability for the predatory bacterium to attack at lower temperatures.