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bioRxiv · 10.1101/2024.10.14.618373

Discovery of Natural MCL1 Inhibitors using Pharmacophore modelling, QSAR, Docking, ADMET, Molecular Dynamics, and DFT Analysis

Das, U.·Chanda, T.·Kumar, J.·Peter, A.
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Abstract

Mcl-1, a Bcl-2 family protein, is a key regulator of apoptosis and is often overexpressed in cancers such as lung, breast, pancreatic, cervical, ovarian cancers, leukemia, and lymphoma. Its role in inhibiting apoptosis enables tumor cells to evade cell death and contributes to drug resistance. Targeting Mcl-1 is crucial in inducing apoptosis and overcoming resistance to therapies that target other anti-apoptotic proteins, making it a prominent target for anticancer drug development across multiple malignancies. So, our study aimed to discover potent antileukemic compounds targeting MCL1. We began by selecting a diverse set of molecules from the BindingDB database to construct a structure-based pharmacophore model, which was subsequently used to virtually screen a library of 407,270 compounds from the COCONUT database. Subsequently, we developed an e-pharmacophore model using the co- crystallized inhibitor AMG-176, for further screening. A QSAR model was then implemented to estimate the IC50 values of these compounds, filtering those with predicted IC50 values below the median. The top hits were subjected to molecular docking and MMGBSA binding energy calculations against MCL1, leading to the selection of two promising candidates for further ADMET analysis. To assess their electronic properties, density functional theory (DFT) calculations were conducted, which included geometry optimization, frontier molecular orbital (FMO) analysis, HOMO-LUMO gaps, and global reactivity descriptors. These analyses confirmed favourable profiles and reactivity for the chosen compounds. In addition, predictions for physicochemical and ADMET properties aligned well with the expected bioactivity and safety profiles of the candidates. Molecular dynamics (MD) simulations further validated their strong binding affinity and stability, positioning them as potential MCL1 inhibitors. Our comprehensive computational approach highlights these compounds as promising and safe antileukemic agents, with future in vivo and in vitro validation recommended for further confirmation.

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Das, U., Chanda, T., Kumar, J., Peter, A.. 2024-10-17. Discovery of Natural MCL1 Inhibitors using Pharmacophore modelling, QSAR, Docking, ADMET, Molecular Dynamics, and DFT Analysis. https://doi.org/10.1101/2024.10.14.618373

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