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Structural stability-guided scaffold hopping and computational modeling of tankyrase inhibitors targeting colorectal
1Department of Clinical Laboratory Sciences, Faculty of Applied Medical Sciences, Najran University, Najran, Saudi Arabia.
Abstract:
Colorectal cancer is one of the leading causes of cancer-related deaths worldwide, mainly due to aberrant Wnt/β-catenin signaling resulting from APC mutations. Tankyrase is a key regulator of this pathway and plays a crucial role in stabilizing AXIN, a negative regulator of β-catenin, and hence an attractive therapeutic target. The present study describes a comprehensive computational approach to discovering novel tankyrase inhibitors for CRC therapy. The reference (RK-582) for ligand-based screening and comparative analysis was taken from the crystal structure of tankyrase. A similarity search in the PubChem, applying an 80% cutoff, yielded 533 structurally similar compounds. These compounds were subjected to virtual screening using a drug-likeness filter. The top-ranking binding poses of three selected compounds (PubChem CIDs: 138594346, 138594730, and 138594428) were selected for DFT calculation and re-docking. DFT calculations revealed that compound 138594428 had the largest HOMO-LUMO gap (4.979 eV), indicating high electronic stability, while 138594346 exhibited a strong balance of stability and reactivity (4.473 eV). The MD simulations were conducted on all ns protein-ligand complexes for 500 ns, exploring their stability. MD simulations confirmed the conformational stability of these compounds, with 138594346 showing the lowest RMSD and RMSF fluctuations. Additionally, a machine learning model trained on 236 known Tankyrase inhibitors accurately predicted pIC₅₀ values, with compound 138594346 (pIC₅₀ = 7.70) closely matching the reference inhibitor (pIC₅₀ = 7.71), and 138594428 also exhibiting strong predicted activity (pIC₅₀ = 7.41). Collectively, these results highlight 138594346 and 138594428 as promising candidates for further experimental validation in the development of targeted CRC therapeutics.
Insights
This study identifies novel tankyrase inhibitors for colorectal cancer (CRC) therapy using computational methods. Compounds 138594346 and 138594428 show promise as potential drug candidates for targeted CRC treatment.
Area of Science:
- Computational Chemistry
- Drug Discovery
- Oncology
Background:
- Colorectal cancer (CRC) is a leading cause of cancer mortality globally.
- Aberrant Wnt/β-catenin signaling, often due to APC mutations, drives CRC progression.
- Tankyrase is a critical regulator of this pathway and a potential therapeutic target.
Purpose of the Study:
- To discover novel tankyrase inhibitors for colorectal cancer (CRC) therapy.
- To employ a comprehensive computational approach for identifying promising drug candidates.
- To computationally evaluate the stability and inhibitory potential of identified compounds.
Main Methods:
- Ligand-based screening using a reference inhibitor (RK-582) against the tankyrase crystal structure.
- Virtual screening of PubChem compounds followed by drug-likeness filtering.
- Density Functional Theory (DFT) calculations, molecular docking, and 500 ns molecular dynamics (MD) simulations.
- Machine learning model trained on known tankyrase inhibitors to predict activity (pIC₅₀).
Main Results:
- Three compounds (PubChem CIDs: 138594346, 138594730, 138594428) were selected for further analysis.
- DFT calculations indicated high electronic stability for compound 138594428 and a balance of stability/reactivity for 138594346.
- MD simulations confirmed the conformational stability of the protein-ligand complexes, with 138594346 showing lowest fluctuations.
- Machine learning model predicted high pIC₅₀ values for compounds 138594346 (7.70) and 138594428 (7.41), comparable to the reference (7.71).
Conclusions:
- Compounds 138594346 and 138594428 demonstrate significant potential as tankyrase inhibitors.
- These compounds warrant further experimental validation for the development of targeted CRC therapeutics.
- Computational approaches are effective in identifying novel drug candidates for cancer therapy.
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