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: ["Chemical"].

Please return the output as a JSON object of the format: {"Chemical": ["Arsenic", ...]}. 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:

Monitoring Conformational Changes in Peroxisome Proliferator-Activated Receptor alpha by a Genetically Encoded Photo-Amino Acid, Cross-Linking, and Mass Spectrometry. Chemical cross-linking combined with an enzymatic digestion and mass spectrometric analysis of the reaction products has evolved into an alternative strategy to structurally resolve protein complexes. We investigated conformational changes in peroxisome proliferator-activated receptor α (PPARα) upon ligand binding. Using E. coli cells with a special tRNA/aminoacyl-tRNA synthetase pair, two PPARα variants were prepared, in which Leu-258 or Phe-273 were site-specifically replaced by the genetically encoded photo-reactive amino acid para-benzoylphenylalanine (Bpa). PPARα variants were subjected to UV-induced cross-linking, both in the absence and presence of ligands. After the photo-cross-linking reaction, reaction mixtures were enzymatically digested and peptides were analyzed by mass spectrometry. The inter-residue distances disclosed by the photo-chemical cross-links served to monitor conformational changes in PPARα upon agonist and antagonist binding. The data obtained with our strategy underline the potential of genetically encoded internal photo-cross-linkers in combination with mass spectrometry as an alternative method to monitor in-solution 3D-protein structures.
{"Chemical": ["Leu", "Bpa", "amino acid", "para-benzoylphenylalanine", "Amino Acid", "Phe"]}