Literature DB >> 26808165

Target identification of natural and traditional medicines with quantitative chemical proteomics approaches.

Jigang Wang1, Liqian Gao2, Yew Mun Lee3, Karunakaran A Kalesh4, Yong Siang Ong2, Jaehong Lim2, Joo-Eun Jee2, Hongyan Sun5, Su Seong Lee6, Zi-Chun Hua7, Qingsong Lin8.   

Abstract

Natural and traditional medicines, being a great source of drugs and drug leads, have regained wide interests due to the limited success of high-throughput screening of compound libraries in the past few decades and the recent technology advancement. Many drugs/bioactive compounds exert their functions through interaction with their protein targets, with more and more drugs showing their ability to target multiple proteins, thus target identification has an important role in drug discovery and biomedical research fields. Identifying drug targets not only furthers the understanding of the mechanism of action (MOA) of a drug but also reveals its potential therapeutic applications and adverse side effects. Chemical proteomics makes use of affinity chromatography approaches coupled with mass spectrometry to systematically identify small molecule-protein interactions. Although traditional affinity-based chemical proteomics approaches have made great progress in the identification of cellular targets and elucidation of MOAs of many bioactive molecules, nonspecific binding remains a major issue which may reduce the accuracy of target identification and may hamper the drug development process. Recently, quantitative proteomics approaches, namely, metabolic labeling, chemical labeling, or label-free approaches, have been implemented in target identification to overcome such limitations. In this review, we will summarize and discuss the recent advances in the application of various quantitative chemical proteomics approaches for the identification of targets of natural and traditional medicines.
Copyright © 2016. Published by Elsevier Inc.

Keywords:  Chemical biology; Natural and traditional medicines; Quantitative proteomics; SILAC; Target identification; iTRAQ

Mesh:

Year:  2016        PMID: 26808165     DOI: 10.1016/j.pharmthera.2016.01.010

Source DB:  PubMed          Journal:  Pharmacol Ther        ISSN: 0163-7258            Impact factor:   12.310


  28 in total

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