Discovery of natural MCL1 inhibitors using pharmacophore modelling, QSAR, docking, ADMET, molecular dynamics, and DFT
Uddalak Das1, Tathagata Chanda2, Jitendra Kumar3
1Department of Plant Biotechnology, University of Agricultural Sciences, Bangalore, Bengaluru, Karnataka 560065, India; School of Biotechnology, Jawaharlal Nehru University, New Delhi 110067, India.
Abstract:
Mcl-1, a member of the Bcl-2 family, is a crucial regulator of apoptosis, frequently overexpressed in various cancers, including lung, breast, pancreatic, cervical, ovarian cancers, leukemia, and lymphoma. Its anti-apoptotic function allows tumor cells to evade cell death and contributes to drug resistance, making it an essential target for anticancer drug development. This study aimed to discover potent antileukemic compounds targeting Mcl-1. We selected diverse molecules from the BindingDB database to construct a structure-based pharmacophore model, which facilitated the virtual screening of 407,270 compounds from the COCONUT database. An e-pharmacophore model was developed using the co-crystallized inhibitor, followed by QSAR modeling to estimate IC50 values and filter compounds with predicted values below the median. The top hits underwent molecular docking and MMGBSA binding energy calculations against Mcl-1, resulting in the selection of two promising candidates for further ADMET analysis. DFT calculations assessed their electronic properties, confirming favorable reactivity profiles of the screened compounds. Predictions for physicochemical and ADMET properties aligned with expected bioactivity and safety. Molecular dynamics simulations further validated their strong binding affinity and stability, positioning them as potential Mcl-1 inhibitors. Our comprehensive computational approach highlights these compounds as promising antileukemic agents, with future in vivo and in vitro validation recommended for further confirmation.
Insights
Researchers identified novel antileukemic compounds targeting Mcl-1, a protein crucial in cancer cell survival and drug resistance. Computational methods screened millions of molecules, revealing two promising drug candidates for further validation against leukemia.
Area of Science:
- Computational chemistry and drug discovery
- Molecular biology and cancer research
Background:
- Mcl-1, a Bcl-2 family protein, is overexpressed in numerous cancers, promoting tumor cell survival and drug resistance.
- Targeting Mcl-1 is a key strategy for developing novel anticancer therapeutics, particularly for leukemia.
Purpose of the Study:
- To discover potent small molecules that inhibit Mcl-1 for potential antileukemic drug development.
- To utilize a comprehensive computational approach for virtual screening and lead compound identification.
Main Methods:
- Structure-based pharmacophore modeling and virtual screening of a large chemical database (COCONUT).
- Quantitative Structure-Activity Relationship (QSAR) modeling, molecular docking, and binding energy calculations (MMGBSA).
- Assessment of electronic properties (DFT), physicochemical and ADMET predictions, and molecular dynamics simulations.
Main Results:
- Virtual screening identified two lead compounds with predicted high affinity and stability against Mcl-1.
- Computational analyses indicated favorable electronic properties, reactivity, and ADMET profiles for the selected candidates.
- Molecular dynamics simulations confirmed strong binding and stability, supporting their potential as Mcl-1 inhibitors.
Conclusions:
- The study successfully identified and computationally validated two promising antileukemic compounds targeting Mcl-1.
- These compounds represent potential candidates for further preclinical and clinical development as novel leukemia therapies.
- A robust computational workflow was established for the discovery of Mcl-1 inhibitors.
