Dataset Information

Dynamic cellular maps of molecular species: Application to drug-target interactions.

ABSTRACT: The design of living cell studies aimed at deciphering the mechanism of action of drugs targeting proteins with multiple functions, expressed in a wide range of concentrations and cellular locations, is a real challenge. We recently showed that the antitumor drug plitidepsin (APL) localizes sufficiently close to the elongation factor eEF1A2 so as to suggest the formation of drug-protein complexes in living cells. Here we present an extension of our previous micro-spectroscopy study, that combines Generalized Polarization (GP) images, with the phasor approach and fluorescence lifetime imaging microscopy (FLIM), using a 7-aminocoumarin drug analog (APL*) as fluorescence tracer. Using the proposed methodology, we were able to follow in real time the formation and relative distribution of two sets of APL-target complexes in live cells, revealing two distinct patterns of behavior for HeLa-wt and APL resistant HeLa-APL-R cells. The information obtained may complement and facilitate the design of new experiments and the global interpretation of the results obtained with other biochemical and cell biology methods, as well as possibly opening new avenues of study to decipher the mechanism of action of new drugs.

SUBMITTER: Garcia C

PROVIDER: S-EPMC5773516 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Dynamic cellular maps of molecular species: Application to drug-target interactions.

García Carolina C Losada Alejandro A Sacristán Miguel A MA Martínez-Leal Juan Fernando JF Galmarini Carlos M CM Lillo M Pilar MP

Scientific reports 20180118 1

The design of living cell studies aimed at deciphering the mechanism of action of drugs targeting proteins with multiple functions, expressed in a wide range of concentrations and cellular locations, is a real challenge. We recently showed that the antitumor drug plitidepsin (APL) localizes sufficiently close to the elongation factor eEF1A2 so as to suggest the formation of drug-protein complexes in living cells. Here we present an extension of our previous micro-spectroscopy study, that combine ...[more]

PMID: 29348621

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Project description:MotivationIn silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action.ResultsWe propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process.AvailabilitySoftware and datasets are available on request.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Dynamic cellular maps of molecular species: Application to drug-target interactions.

Publications

Dynamic cellular maps of molecular species: Application to drug-target interactions.

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