You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["Protein", "Chemical"].

Please return the output as a JSON object of the format: {"Chemical" : ["Dexamethasone", ...], "Protein" : ["BRCA-1", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Live-cell imaging of p53 interactions using a novel Venus-based bimolecular fluorescence complementation system.
p53 plays an important role in regulating a wide variety of cellular processes, such as cell cycle arrest and/or apoptosis. Dysfunction of p53 is frequently associated with several pathologies, such as cancer and neurodegenerative diseases. In recent years substantial progress has been made in developing novel p53-activating molecules. Importantly, modulation of p53 interaction with its main inhibitor, Mdm2, has been highlighted as a promising therapeutic target. In this regard, bimolecular fluorescence complementation (BiFC) analysis, by providing direct visualization of protein interactions in living cells, offers a straightforward method to identify potential modulators of protein interactions. In this study, we developed a simple and robust Venus-based BiFC system to screen for modulators of p53-p53 and p53-Mdm2 interactions in live mammalian cells. We used nutlin-3, a well-known disruptor of p53-Mdm2 interaction, to validate the specificity of the assay. The reduction of BiFC signal mediated by nutlin-3 was correlated with an increase in Puma transactivation, PARP cleavage, and cell death. Finally, this novel BiFC approach was exploited to identify potential modulators of p53-Mdm2 complex formation among a commercially available chemical library of 33 protein phosphatase inhibitors. Our results constitute "proof-of-concept" that this model has strong potential as an alternative to traditional target-based drug discovery strategies. Identification of new modulators of p53-p53 and p53-Mdm2 interactions will be useful to achieve synergistic drug efficacy with currently used anti-tumor therapies.
{"Chemical": ["nutlin-3"], "Protein": ["Puma", "protein phosphatase", "p53", "Mdm2", "PARP"]}