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Bioorthogonal approach to identify unsuspected drug targets in live cells.

ABSTRACT: A proteomics method to pull down secondary drug targets from live cells is described. The drug of interest is modified with trans-cyclooctene (TCO) and incubated with live cells. Upon cell lysis, the modified drug bound to the protein is pulled down using magnetic beads decorated with a cleavable tetrazine-modified linker. Samples are then run on an SDS-PAGE gel and isolated bands are submitted for mass spectrometry analysis to identify drug targets.

SUBMITTER: Yang KS

PROVIDER: S-EPMC3856564 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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Bioorthogonal approach to identify unsuspected drug targets in live cells.

Yang Katherine S KS Budin Ghyslain G Tassa Carlos C Kister Olivier O Weissleder Ralph R

Angewandte Chemie (International ed. in English) 20130819 40

A proteomics method to pull down secondary drug targets from live cells is described. The drug of interest is modified with trans-cyclooctene (TCO) and incubated with live cells. Upon cell lysis, the modified drug bound to the protein is pulled down using magnetic beads decorated with a cleavable tetrazine-modified linker. Samples are then run on an SDS-PAGE gel and isolated bands are submitted for mass spectrometry analysis to identify drug targets. ...[more]

PMID: 23960025

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Project description:Model-based optimization approaches are valuable in developing new drugs for human metabolic disorders. The core objective in most optimal drug designs is positive therapeutic effects. In this study, we considered the effects of therapeutic, adverse, and target variation simultaneously. A fuzzy optimization method was applied to formulate a multiobjective drug design problem for detecting enzyme targets in the presynaptic dopamine metabolic network to remedy two types of enzymopathies caused by deficiencies of vesicular monoamine transporter 2 (VMAT2) and tyrosine hydroxylase (TH). The fuzzy membership approach transforms a two-stage drug discovery problem into a unified decision-making problem. We developed a nested hybrid differential evolution algorithm to efficiently identify a set of potential drug targets. Furthermore, we also simulated the effects of current clinical drugs for Parkinson's disease (PD) in this model and tried to clarify the possible causes of neurotoxic and neuroprotective effects. The optimal drug design could yield 100% satisfaction grade when both therapeutic effect and the number of targets were considered in the objective. This scenario required regulating one to three and one or two enzyme targets for 50%-95% and 50%-100% VMAT2 and TH deficiencies, respectively. However, their corresponding adverse and target variation effect grades were less satisfactory. For the most severe deficiencies of VMAT2 and TH, a compromise design could be obtained when the effects of therapeutic, adverse, and target variation were simultaneously applied to the optimal drug discovery problem. Such a trade-off design followed the no free lunch theorem for optimization; that is, a more serious dopamine deficiency required more enzyme targets and lower satisfaction grade. In addition, the therapeutic effects of current clinical medications for PD could be enhanced in combination with new enzyme targets. The increase of toxic metabolites after treatment might be the cause of neurotoxic effects of some current PD medications.

Dataset Information

Bioorthogonal approach to identify unsuspected drug targets in live cells.

Publications

Bioorthogonal approach to identify unsuspected drug targets in live cells.

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