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.

Redox homeostasis is described as the net physiologic balance between inter-convertible oxidized and reduced equivalents within subcellular compartments that remain in a dynamic equilibrium. This equilibrium is impacted by reactive oxygen species (ROS), which are natural by-products of normal cellular activity. Studies have shown that cancer cells have high ROS levels and altered redox homeostasis due to increased basal metabolic activity, mitochondrial dysfunction, peroxisome activity, as well as the enhanced activity of NADPH oxidase, cyclooxygenases, and lipoxygenases. Glioblastoma (GBM) is the most prevalent primary brain tumor in adults with a median survival of 15 months. GBM is characterized by its extreme resistance to therapeutic interventions as well as an elevated metabolic rate that results in the exacerbated production of ROS. Therefore, many agents with either antioxidant or pro-oxidant mechanisms of action have been rigorously employed in preclinical as well as clinical settings for treating GBM by inducing oxidative stress within the tumor. Among those agents are well-known antioxidant vitamin C and small molecular weight SOD mimic BMX-001, both of which are presently in clinical trials on GBM patients. Despite the wealth of investigations, limited data is available on the response of normal brain vs glioblastoma tissue to these therapeutic interventions. Currently, a sensitive and rapid liquid chromatography tandem mass spectrometry (LC-MS/MS) method was established for the quantification of a panel of oxidative stress biomarkers: glutathione (GSH), cysteine (Cys), glutathione disulfide (GSSG), and cysteine disulfide in human-derived brain tumor and mouse brain samples; this method will be enriched with additional oxidative stress biomarkers homocysteine (Hcy), methionine (Met), and cystathionine (Cyst). Using this enriched method, we propose to evaluate the thiol homeostasis and the redox state of both normal brain and GBM in mice after exposure with redox-active therapeutics. Our results showed that, compared to normal brain (in intact mice), GBM tissue has significantly lower GSH/GSSG and Cys/CySS ratios indicating much higher oxidative stress levels. Contralateral “normal” brain tissue collected from the mice with intracranial GBM were also under significant oxidative stress compared to normal brains collected from the intact mice. Importantly, normal brain tissue in both studies retained the ability to restore redox homeostasis after treatment with a redox-active therapeutic within 24 hours while glioblastoma tissue does not. Ultimately, elucidating the differential redox response of normal vs tumor tissue will allow for the development of more redox-active agents with therapeutic benefit.