4.7 Article

Computational Investigation Identified Potential Chemical Scaffolds for Heparanase as Anticancer Therapeutics

Journal

Publisher

MDPI
DOI: 10.3390/ijms22105311

Keywords

Heparanase; pharmacophore modeling; virtual screening; molecular docking; molecular dynamics simulations; binding free energy calculations; MM/PBSA

Funding

  1. National Research Foundation of Korea (NRF) - Ministry of Education [2020R1A6A1A03044344]
  2. Bio & Medical Technology Development Program of the National Research Foundation (NRF) - Korean government (MSIT) [NRF-2018M3A9A7057263]
  3. National Research Foundation of Korea [2020R1A6A1A03044344, 2018M3A9A7057263] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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Heparanase (Hpse) is an enzyme associated with tumor growth, metastasis and angiogenesis, making it a potential target for cancer therapeutics. This study utilized a ligand-based pharmacophore model to screen a database of natural and synthetic compounds, identifying 33 potential Hpse inhibitors with better binding affinities than reference molecules. These hit compounds could serve as lead scaffolds for developing cancer treatments targeting Heparanase upregulation.
Heparanase (Hpse) is an endo-beta-D-glucuronidase capable of cleaving heparan sulfate side chains. Its upregulated expression is implicated in tumor growth, metastasis and angiogenesis, thus making it an attractive target in cancer therapeutics. Currently, a few small molecule inhibitors have been reported to inhibit Hpse, with promising oral administration and pharmacokinetic (PK) properties. In the present study, a ligand-based pharmacophore model was generated from a dataset of well-known active small molecule Hpse inhibitors which were observed to display favorable PK properties. The compounds from the InterBioScreen database of natural (69,034) and synthetic (195,469) molecules were first filtered for their drug-likeness and the pharmacophore model was used to screen the drug-like database. The compounds acquired from screening were subjected to molecular docking with Heparanase, where two molecules used in pharmacophore generation were used as reference. From the docking analysis, 33 compounds displayed higher docking scores than the reference and favorable interactions with the catalytic residues. Complex interactions were further evaluated by molecular dynamics simulations to assess their stability over a period of 50 ns. Furthermore, the binding free energies of the 33 compounds revealed 2 natural and 2 synthetic compounds, with better binding affinities than reference molecules, and were, therefore, deemed as hits. The hit compounds presented from this in silico investigation could act as potent Heparanase inhibitors and further serve as lead scaffolds to develop compounds targeting Heparanase upregulation in cancer.

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