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Unraveling the molecular targets of natural products: Insights from genomic and proteomic analyses
Author(s) -
Wong Chi Chun,
Cheng Ka Wing,
He QingYu,
Chen Feng
Publication year - 2008
Publication title -
proteomics – clinical applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 54
eISSN - 1862-8354
pISSN - 1862-8346
DOI - 10.1002/prca.200880002
Subject(s) - computational biology , proteome , proteomics , context (archaeology) , drug discovery , identification (biology) , natural product , drug development , biology , transcriptome , drug , data science , bioinformatics , computer science , genetics , pharmacology , biochemistry , gene , gene expression , paleontology , botany
Natural products and their derivatives have been an invaluable source of drug leads for the pharmaceutical industry over the past decades, especially for antibacterial and anticancer purposes. Nature products, with their chemical diversity and biochemical specificity, are ideal starting points of drug development. Rational drug design based on natural product scaffolds, however, was hindered by a lack of knowledge regarding their mechanisms of action. Advances in proteomic technologies hold the key to revolutionize the target identification of natural products. In this regard, chemical proteomics have demonstrated the capabilities to identify specific targets by screening against the proteome. On the other hand, high‐throughput proteome analysis reveals the multiple impacts of drug‐target interaction in a global context, providing insights for elucidation of signaling pathways involved in the drug response, and uncovering predictive markers of drug efficacy and toxicity. Increasingly, studies have exploited integration of transcriptome and proteome datasets, which offers additional information on regulation of molecular network at transcriptional and post‐translational levels. In this review, we discuss major proteomic approaches applied to studying the mechanism of action of natural products and merits of combining datasets from proteomics and transcriptomics analysis.