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Computational characterization and machine learning analysis of quantum optimized marine fungal metabolites targeting
Rima Bhardwaj1, Talha Jawaid2, Saif Ahmed3
1Department of Chemistry, Poona College, Savitribai Phule Pune University, Pune, India.
Abstract:
Cancer immune evasion is predominantly mediated through immune checkpoint pathways, such as the PD-1/PD-L1 axis. In this mechanism, PD-L1, which is often overexpressed on tumor cells, binds to PD-1 receptors on T cells, resulting in the inhibition of T cell activity and allowing tumors to evade immune surveillance. Targeting this interaction is of therapeutic significance. Marine fungal metabolites were investigated as potential PD-L1 inhibitors using a multi-level computational approach that combines quantum chemical, dynamic, energetic, and machine learning studies. Preliminary virtual screening narrowed down the list to the top four contenders, CMNPD20987, CMNPD20986, CMNPD24819, and CMNPD20907, with docking values ranging from - 10.7 to - 8.2 kcal/mol. HOMO-LUMO gap analysis based on the density functional theory demonstrated the highest electronic stability of CMNPD24819 (5.087 eV) and the highest reactivity of CMNPD20907 (3.954 eV). Redocking studies highlighted stable interactions with critical PD-L1 amino acid residues like Tyr56 (π-π stacking), Asp122, Gln66, Ile116, and Lys124 (hydrogen bonding). Triplicate 200 ns MD simulations established the structural stability of the chosen complexes with low RMSD and RMSF values. MM/GBSA binding free energies estimated significant affinity, with notable affinity for CMNPD24819 (- 34.39 kcal/mol) and CMNPD20987 (- 30.63 kcal/mol). Analysis of free energy landscapes showed deep minima of the free energy basin, indicating stable conformational states. The machine learning regression model trained on ChEMBL PD-L1 inhibitors predicted high pIC50 values for the selected compounds, with CMNPD20907, CMNPD20986, CMNPD20987, and CMNPD24819scoring above the reference molecule. This holistic analysis highlights the electronic strength, beneficial binding profiles, and biomedical value of the marine fungal metabolites as potential future immune checkpoint inhibitors of cancer.
Insights
Marine fungal metabolites show promise as novel cancer immune checkpoint inhibitors by targeting the PD-1/PD-L1 pathway. Computational studies identified four potent compounds with significant binding affinity and stability for potential therapeutic development.
Area of Science:
- Computational chemistry and drug discovery
- Immunology and cancer research
- Marine natural products
Background:
- Cancer immune evasion is often mediated by immune checkpoints, particularly the PD-1/PD-L1 axis.
- Overexpressed PD-L1 on tumor cells inhibits T cell activity, enabling immune escape.
- Targeting the PD-1/PD-L1 interaction is a significant therapeutic strategy in oncology.
Purpose of the Study:
- To investigate marine fungal metabolites as potential inhibitors of the PD-1/PD-L1 immune checkpoint.
- To employ a multi-level computational approach for identifying and validating lead compounds.
- To assess the binding affinity, stability, and electronic properties of candidate metabolites.
Main Methods:
- Virtual screening and molecular docking to identify potential PD-L1 inhibitors from marine fungal metabolites.
- Quantum chemical calculations (DFT) for HOMO-LUMO gap analysis and electronic stability assessment.
- Molecular dynamics (MD) simulations and MM/GBSA calculations to evaluate binding free energies and complex stability.
- Machine learning models trained on known PD-L1 inhibitors to predict compound efficacy (pIC50).
Main Results:
- Four marine fungal metabolites (CMNPD20987, CMNPD20986, CMNPD24819, CMNPD20907) were identified as top candidates with strong docking scores.
- CMNPD24819 exhibited the highest electronic stability, while CMNPD20907 showed the highest reactivity.
- MD simulations and binding energy calculations confirmed stable interactions and significant binding affinity, particularly for CMNPD24819 and CMNPD20987.
- Machine learning predictions indicated high pIC50 values for the selected compounds, surpassing the reference molecule.
Conclusions:
- Marine fungal metabolites possess favorable electronic properties and binding profiles for inhibiting the PD-1/PD-L1 pathway.
- The identified compounds demonstrate significant potential as novel immune checkpoint inhibitors for cancer therapy.
- This study highlights the value of integrating computational methods for discovering new anti-cancer agents from natural sources.
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