Fig. 1: LiP-Quant, a platform for drug target identification. | Nature Communications

Fig. 1: LiP-Quant, a platform for drug target identification.

From: A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes

Fig. 1

a Volcano plots of LiP samples from HeLa lysates (left panel, n = 4 of independent lysate replicates) and live HeLa cells (right panel, n = 3 of biologically independent cells) treated with rapamycin. Peptide mixes produced in the presence or absence of 2 µM rapamycin are compared. Fold changes (FC) in peptide abundance for treated and untreated samples are shown as a function of statistical significance. Significance cutoffs were q-value = 0.0001 (t-test, one sample, two-tailed) and FC = 1.5. Each protein is represented with a single data point, corresponding to the peptide with the lowest q-value. The known interactor of rapamycin (FKBP1A) is highlighted in red. Proteins passing both cutoffs are in blue. b Principle and experimental design of LiP-Quant. Sample preparation for MS analysis follows a multiplexed workflow that is suitable for the processing of drug libraries. c Compiled LiP-Quant score distributions (Gaussian smoothed kernel density) for known target and non-target proteins from all HeLa experiments with the exception of the promiscuous binder staurosporine. d Dose–response curves showing relative intensities of LiP-Quant peptides after partial proteolytic digestion of aliquots of HeLa lysates over a rapamycin concentration range. Curves of the top 5 LiP-Quant peptides ranked by LiP-Quant score, all of which are from the expected direct target FKBP1A, are shown. The numbers on top of the graph show the LiP-Quant score position of the relative peptides. Source data are provided as a Source Data file.

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