Drug Design, Development and Therapy (Nov 2017)
In silico-based identification of human α-enolase inhibitors to block cancer cell growth metabolically
Abstract
Jrhau Lung,1 Kuan-Liang Chen,2 Chien-Hui Hung,3 Chih-Cheng Chen,4,5 Ming-Szu Hung,5–7 Yu-Ching Lin,5–7 Ching-Yuan Wu,8 Kuan-Der Lee,9 Neng-Yao Shih,10 Ying Huang Tsai11,12 1Department of Medical Research and Development, Chang Gung Memorial Hospital, Chiayi, 2Department of Endodontics, ChiMei Medical Center, Tainan, 3Graduate Institute of Clinical Medical Sciences, Chang Gung University, Taoyuan, 4Division of Hematology and Oncology, Chang Gung Memorial Hospital, Chiayi, 5Department of Medicine, Chang Gung University, Taoyuan, 6Department of Respiratory Care, Chang Gung University of Science and Technology, Chiayi, 7Division of Thoracic Oncology, Department of Pulmonary and Critical Care Medicine, 8Department of Chinese Medicine; Chang Gung Memorial Hospital, Chiayi, 9Department of Hematology and Oncology, Taipei Medical University Hospital, Taipei, 10National Institute of Cancer Research, National Health Research Institutes, Tainan, 11Department of Respiratory Care, College of Medicine, Chang Gung University, Taoyuan, 12Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Chang Gung Memorial Hospital-Kaohsiung Medical Center, Kaohsiung, Taiwan 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. Keywords: α-enolase inhibitor, virtual screening, molecular dynamics simulation, glycolysis, metabolism
