Journal of molecular modeling · 2026
Expanding the chemical space of Bcl-2 inhibitors: a multiscale computational exploration of the benzopyran (chromene) scaffold.
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Sharma Kritika, Yash N D, Dangi Mansi, Kadamb Maitree, Shrivastava Aparna, Kumaran J Senthil, Yadav Sakshi, Jain Monika, Singh Amit Kumar, Muthukumaran Jayaraman
Аннотация
CONTEXT: Dysregulation of apoptosis mediated by antiapoptotic Bcl-2 family proteins contributes significantly to cancer progression and treatment resistance. Although the BH3 mimetic venetoclax has demonstrated remarkable clinical efficacy, the emergence of resistance-associated mechanisms has created a need for alternative Bcl-2-targeting scaffolds. Benzopyran derivatives, including 4H-chromene and chromone derivatives, represent a structurally diverse class of compounds with reported anticancer and apoptosis-modulating activities; however, their interaction landscape with Bcl-2 remains insufficiently explored. In the present study, a curated library of 356 benzopyran family compounds was investigated against Bcl-2 to identify potential lead candidates. Three compounds, PM28524659, PM31145889, and PM34262894, exhibited favorable binding characteristics relative to the reference chromene-derived Bcl-2 inhibitor HA14-1. Binding mode analysis indicated occupancy of the BH3-binding groove, with interactions involving ASP49, ARG53, and TYR148 residues. Molecular similarity, scaffold, and pharmacophore analyses revealed preservation of key target recognition features despite low overall structural similarity to HA14-1. Based on their physicochemical, pharmacokinetic, and molecular descriptor profiles, PM28524659 and PM31145889 were selected for further investigation through long-timescale molecular dynamics simulations. Both complexes remained stable throughout the 500-ns MD simulation period and maintained favorable interactions with the binding pocket while preserving the overall structural integrity of Bcl-2 protein. Although the identified compounds exhibited lower predicted binding affinity than venetoclax, their favorable ligand efficiency, relatively simple molecular architecture, and scope for structural optimization support their further evaluation as Bcl-2-targeting scaffolds for apoptosis-directed anticancer drug discovery.
METHODS: A dataset comprising 356 curated benzopyran family compounds was subjected to virtual screening against Bcl-2 (PDB ID: 6O0K) using AutoDock Vina. Protein-ligand interactions were analyzed using Discovery Studio Visualizer and PyMOL. Molecular similarity, scaffold, and pharmacophore analyses were performed using two-dimensional and three-dimensional descriptor-based approaches. Drug-likeness, physicochemical properties, pharmacokinetic parameters, and toxicity profiles were assessed using SwissADME, ProTox-3, and related cheminformatics tools. The selected protein-ligand complexes were further investigated by 500-ns molecular dynamics simulations using GROMACS 2025.3 and the CHARMM36 force field in an explicit solvent environment employing the CHARMM-modified TIP3P water model. Trajectory analyses included root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), intermolecular hydrogen bond analysis, principal component analysis (PCA), and MM/PBSA-based binding free energy analysis to characterize structural stability, residue-level flexibility, compactness, protein-ligand interactions, dominant collective essential motions, and binding affinity. The trajectory-derived parameters were further subjected to descriptive statistical and distributional analyses using Python-based tools, including Pandas, NumPy, Matplotlib, and Seaborn, with correlation and cross-correlation analyses used to examine parameter relationships and variability across the simulated systems.
METHODS: A dataset comprising 356 curated benzopyran family compounds was subjected to virtual screening against Bcl-2 (PDB ID: 6O0K) using AutoDock Vina. Protein-ligand interactions were analyzed using Discovery Studio Visualizer and PyMOL. Molecular similarity, scaffold, and pharmacophore analyses were performed using two-dimensional and three-dimensional descriptor-based approaches. Drug-likeness, physicochemical properties, pharmacokinetic parameters, and toxicity profiles were assessed using SwissADME, ProTox-3, and related cheminformatics tools. The selected protein-ligand complexes were further investigated by 500-ns molecular dynamics simulations using GROMACS 2025.3 and the CHARMM36 force field in an explicit solvent environment employing the CHARMM-modified TIP3P water model. Trajectory analyses included root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), intermolecular hydrogen bond analysis, principal component analysis (PCA), and MM/PBSA-based binding free energy analysis to characterize structural stability, residue-level flexibility, compactness, protein-ligand interactions, dominant collective essential motions, and binding affinity. The trajectory-derived parameters were further subjected to descriptive statistical and distributional analyses using Python-based tools, including Pandas, NumPy, Matplotlib, and Seaborn, with correlation and cross-correlation analyses used to examine parameter relationships and variability across the simulated systems.