Discovery of Glyoxalase-I Inhibitors via Structure-Based Virtual Screening, Molecular Dynamics Simulations, and In vitro Enzyme Activity Assessment
Objective: To identify novel small-molecule inhibitors of human glyoxalase-I (Glo-I), a zinc-dependent enzyme implicated in cancer-cell detoxification and overexpressed in several cancers, using an integrated structure-based virtual screening and in vitro enzyme-inhibition approach, and to characterize the binding stability of both active hits by molecular dynamics (MD) simulation. Methods: Structure-based virtual screening of the Otava chemical library was performed against the high-resolution human Glo-I crystal structure (PDB: 3W0T, 1.35 Å) using the LibDock and CDocker algorithms in BIOVIA Discovery Studio (DS). Thirteen prioritized hits were evaluated for Glo-I inhibitory activity in vitro against recombinant human Glo-I (rhGlo-I), with myricetin as the positive control, and the binding stability of both active compounds was further examined by 100-ns MD simulations in GROMACS. Results: Of the 13 compounds evaluated, two showed measurable Glo-I inhibitory activity (15% hit rate). Compound D7015500159 showed the highest potency (IC₅₀ = 34.95 µM). Over 100 ns of MD simulation, the D7015500159–Glo-I complex remained stable, with a backbone root-mean-square deviation (RMSD) below 2 Å, favorable solvent-accessible surface area (SASA) values, and up to 10 hydrogen-bond (H-bond) interactions observed in a single frame. In contrast, the D7015870006–Glo-I complex exhibited substantial ligand-RMSD fluctuations (2–8 nm) and did not remain stably bound, consistent with its comparatively weaker inhibitory potency. Conclusion: The two active hits identified here, particularly D7015500159, represent candidate Glo-I inhibitors worth further optimization as anticancer agents, and illustrate that combining structure-based virtual screening with experimental validation is an efficient strategy for discovering inhibitors of tumor-specific metabolic pathways such as the glyoxalase system.
Authors
- Belal Omar Al-Najjar (ORCID: https://orcid.org/0000-0001-6811-1792)
- Qosay Ali Al-Balas (ORCID: https://orcid.org/0000-0002-8350-900X)
- Mohammed Bashar Al-Qazzan (ORCID: https://orcid.org/0000-0002-5723-6247)
- WISSAM AL-ASADI
Institutions
- Al-Ahliyya Amman University (JO)
- Jordan University of Science and Technology (JO)
Publication Details
- Journal
- International Journal of Applied Pharmaceutics
- Published
- 2026-11-07
- DOI
- https://doi.org/10.22159/ijap.2026v18i6.58857
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00