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In Silico Targeting of Fascin Protein for Cancer Therapy: Benchmarking, Virtual Screening and Molecular Dynamics
Heba H A Hassan1, Muhammad I Ismail2, Mohammed A S Abourehab3
1Drug Design and Discovery Laboratory, Zewail City of Science and Technology, October Gardens, 6th of October City, Giza 12578, Egypt.
Abstract:
Fascin is an actin-bundling protein overexpressed in various invasive metastatic carcinomas through promoting cell migration and invasion. Therefore, blocking Fascin binding sites is considered a vital target for antimetastatic drugs. This inspired us to find new Fascin binding site blockers. First, we built an active compound set by collecting reported small molecules binding to Fascin's binding site 2. Consequently, a high-quality decoys set was generated employing DEKOIS 2.0 protocol to be applied in conducting the benchmarking analysis against the selected Fascin structures. Four docking programs, MOE, AutoDock Vina, VinaXB, and PLANTS were evaluated in the benchmarking study. All tools indicated better-than-random performance reflected by their pROC-AUC values against the Fascin crystal structure (PDB: ID 6I18). Interestingly, PLANTS exhibited the best screening performance and recognized potent actives at early enrichment. Accordingly, PLANTS was utilized in the prospective virtual screening effort for repurposing FDA-approved drugs (DrugBank database) and natural products (NANPDB). Further assessment via molecular dynamics simulations for 100 ns endorsed Remdesivir (DrugBank) and NANPDB3 (NANPDB) as potential binders to Fascin binding site 2. In conclusion, this study delivers a model for implementing a customized DEKOIS 2.0 benchmark set to enhance the VS success rate against new potential targets for cancer therapies.
Insights
Researchers identified potential anti-metastatic drugs by blocking Fascin, a protein linked to cancer invasion. Using computational methods, they found Remdesivir and a natural product that may inhibit Fascin binding site 2.
Area of Science:
- Biochemistry
- Molecular Biology
- Computational Chemistry
Background:
- Fascin is an actin-bundling protein implicated in cancer cell migration and invasion.
- Targeting Fascin binding sites is a promising strategy for developing antimetastatic drugs.
Purpose of the Study:
- To identify novel small molecules that block Fascin binding site 2.
- To establish a robust virtual screening (VS) workflow for discovering Fascin inhibitors.
Main Methods:
- Construction of an active compound set and a high-quality decoy set using the DEKOIS 2.0 protocol.
- Benchmarking of four molecular docking programs (MOE, AutoDock Vina, VinaXB, PLANTS) against Fascin crystal structure (PDB: 6I18).
- Prospective VS of FDA-approved drugs (DrugBank) and natural products (NANPDB) using the PLANTS program, followed by molecular dynamics simulations.
Main Results:
- All evaluated docking tools showed better-than-random performance, with PLANTS demonstrating superior screening efficiency.
- PLANTS successfully identified potent active compounds with early enrichment.
- Molecular dynamics simulations identified Remdesivir and NANPDB3 as potential binders to Fascin binding site 2.
Conclusions:
- A customized DEKOIS 2.0 benchmark set can improve VS success rates for new therapeutic targets.
- Remdesivir and NANPDB3 are promising candidates for further investigation as antimetastatic agents targeting Fascin.
- This study provides a validated computational approach for drug discovery against Fascin in cancer therapy.
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