{'fraction_non_alphanumeric': 0.03223593964334705, 'fraction_numerical': 0.021262002743484224, 'mean_word_length': 5.786046511627907, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Drug target identification from protein dynamics using quantitative pathway analysis.

Dynamic proteomics promises to greatly facilitate identification of target proteins for drug molecules. Cohen et al. [Science, 2008, 322 (5907), 1511-1516] illustrated this potential, with the responses of 812 fluorescently tagged proteins to camptothecin administration monitored over 48 h. Directly from this data, one can restrict the list of candidate targets to 52 proteins. However, this approach has numerous limitations: equipment, labor (tagging and analyzing ≥1 colony/protein), and data analysis (aggregating individual cell data into population-relevant data sets). Furthermore, analytical success requires both explicit knowledge of drug target time-course evolution and, most importantly, monitoring of the target, itself. To address these issues, we developed a quantitative pathway analysis (qPA) technique, which employs well-annotated signaling pathways and elucidates putative drug targets and other molecules of interest. qPA, using more general assumptions and only 3 out of 144 available time points, identified the single known camptothecin target, TOPI, among only a handful of putative targets. Importantly, identification was possible without containing TOPI within the input data. These results demonstrate the potential of qPA in drug target discovery and highlight the importance of systems biology approaches for analysis of proteomics data.