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

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.

Late-Stage Functionalization of Platensimycin Leading to Multiple Analogues with Improved Antibacterial Activity in Vitro and in Vivo

“…To ascertain the interaction of A28 -ecFabF (C163Q) complex under dynamic conditions, we conducted a molecular dynamics simulation using PTM as a control. 42 The stability of the complex from the previous docking experiments was first evaluated by the root-mean-square deviation (RMSD) of ecFabF (C163Q) backbone using the generated trajectory data in a simulation of 50 nanoseconds, in comparison with the experimental PTM-ecFabF (C163Q) complex. The backbone RMSD values of A28 - and PTM-ecFabF (C163Q) complexes were similar, suggesting that A28 binds to ecFabF (C163Q) and induces similar structural fluctuations with those of PTM (Figure 5C).…”

“…To ascertain the interaction of the A28 -ecFabF (C163Q) complex under dynamic conditions, we conducted a molecular dynamics simulation using PTM as a control . The stability of the complex from the previous docking experiments was first evaluated by the root-mean-square deviation (RMSD) of the ecFabF (C163Q) backbone using the generated trajectory data in a simulation of 50 ns, in comparison with the experimental PTM-ecFabF (C163Q) complex.…”

Enolase 1, a Moonlighting Protein, as a Potential Target for Cancer Treatment

“…, tropolone (10), pyridine (11) and hydroxyquinoline (12) (Figure 4) may be considered promising anticancer agents for further development, and could fight the metabolism of cancer by inhibiting ENO1 (99,100). Furthermore, upregulation of ENO1 by CagA can be attenuated by U0126 (13) (selective inhibitor of both MEK1/2) and PP1 (14) (Figure 4) (inhibitor of Src kinase).…”

Ligand Unbinding Pathway and Mechanism Analysis Assisted by Machine Learning and Graph Methods

“…Identifying process paths in biomolecular simulations is an ubiquituous problem. − In a more general sense, the problem is related to clustering trajectories of moving objects . Ligand unbinding paths can be determined, for example, by SEEKR, contact fingerprint analysis, ,, volume-based metadynamics, adaptive bias potentials, or, as in our case, via a principal component analysis of protein–ligand contacts (conPCA). , Choosing the latter approach already presumes that protein–ligand contacts forming the relevant coordinates reveal distinct paths, which may not necessarily be the case. Sorting trajectories by visual inspection can be tedious due to the necessity of manually sorting hundreds of simulations .…”