In silico-based identification of human α-enolase inhibitors to block cancer cell growth metabolically

Jrhau Lung, Kuan Liang Chen, Chien Hui Hung, Chih Cheng Chen, Ming Szu Hung, Yu Ching Lin, Ching Yuan Wu, Kuan Der Lee, Neng Yao Shih, Ying Huang Tsai

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Unlimited growth of cancer cells requires an extensive nutrient supply. To meet this demand, cancer cells drastically upregulate glucose uptake and metabolism compared to normal cells. This difference has made the blocking of glycolysis a fascinating strategy to treat this malignant disease. α-enolase is not only one of the most upregulated glycolytic enzymes in cancer cells, but also associates with many cellular processes or conditions important to cancer cell survival, such as cell migration, invasion, and hypoxia. Targeting α-enolase could simultaneously disturb cancer cells in multiple ways and, therefore, is a good target for anticancer drug development. In the current study, more than 22 million chemical structures meeting the criteria of Lipinski’s rule of five from the ZINC database were docked to α-enolase by virtual screening. Twenty-four chemical structures with docking scores better than that of the enolase substrate, 2-phosphoglycerate, were further screened by the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties prediction. Four of them were classified as non-mutagenic, non-carcinogenic, and capable of oral administration where they showed steady interactions to α-enolase that were comparable, even superior, to the currently available inhibitors in molecular dynamics (MD) simulation. These compounds may be considered promising leads for further development of the α-enolase inhibitors and could help fight cancer metabolically.

Original languageEnglish
Pages (from-to)3281-3290
Number of pages10
JournalDrug Design, Development and Therapy
Volume11
DOIs
Publication statusPublished - Nov 16 2017

Keywords

  • Glycolysis
  • Metabolism
  • Molecular dynamics simulation
  • Virtual screening
  • α-enolase inhibitor

ASJC Scopus subject areas

  • Pharmacology
  • Pharmaceutical Science
  • Drug Discovery

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  • Cite this

    Lung, J., Chen, K. L., Hung, C. H., Chen, C. C., Hung, M. S., Lin, Y. C., Wu, C. Y., Lee, K. D., Shih, N. Y., & Tsai, Y. H. (2017). In silico-based identification of human α-enolase inhibitors to block cancer cell growth metabolically. Drug Design, Development and Therapy, 11, 3281-3290. https://doi.org/10.2147/DDDT.S149214