Pharmacophore modeling, multiple docking, and molecular dynamics studies on Wee1 kinase inhibitors

Abstract

Wee1-like protein kinase (Wee1) is a tyrosine kinase that regulates the G checkpoint and prevents entry into mitosis in response to DNA damage. Based on a series of signaling pathways initiated by Wee1, Wee1 has been recognized as a potential target for cancer therapy. In order to discover potent Wee1 inhibitors with novel scaffolds, ligand-based pharmacophore model has been built based on 101 known Wee1 inhibitors. Then the best pharmacophore model, AADRRR.340, with good PLS statistics (R= 0.9212, Q= 0.7457), was selected and validated. The validated model was used as a 3D search query for databases virtual screening. The filtered molecules were further analyzed and refined by Lipinski's rule of 5, multiple docking procedures (HTVS, SP, GOLD, XP and unique QPLD, that is, quantum polarized ligand docking), ADMET screening and the Prime/MM-GBSA method binding free energy calculations. Eight leads were identified as potential Wee1 inhibitors, and a 50 ns molecular dynamics (MD) simulation was carried out for top four inhibitors to predict the stability of ligand-protein complex. MM-PBSA based on MD simulation and the energy contribution per residue to the binding energy was calculated. In the end, three hits with good stabilization and affinity to protein were identified.

Synthetic and Medicinal Chemistry Approaches Toward WEE1 Kinase Inhibitors and Its Degraders

“…Later in 2019, via ensemble docking of 20 WEE1 crystal structures, two new ligands 1 and 2 (Figure ) were identified . In the same year, Hu et al screened the ZINC database via ligand-based pharmacophore modeling and identified eight potential ligands with new scaffolds, which were further subjected to QSAR, ADMET, and binding free energy studies followed by molecular dynamics (MD) simulations and ultimately proposed 3 (Figure ) as a potent WEE1i . Furthermore, vanillates ( 4 ) (Figure ) have also exhibited micromolar WEE1 inhibition and were initially treated as an important class of WEE1i .…”

Virtual Screening and Elucidation of Putative Binding Mode for Small Molecule Antagonist of BCL2 BH4 Domain

“…Schrodinger"s Protein preparation wizard [15] was used to import the BCL2 protein from PDB (PDB ID:1GM), and prepossessed: Hydrogen atoms were added, disulfide bonds were created, missing loops and side chains were added using Prime [16] Termini was capped, water molecules beyond 5Å from het groups were deleted, finally, het sates at pH 7.0 +/-2.0 was generated using Epik [4]. Hydrogen bonds in the protein structure were optimized, water molecule with less than 3 Hydrogen bonds to non-water residues/molecule was deleted, and finally, the whole protein structure was minimized converging heavy atoms to RSMD: 0.3Å.…”

“…Apoptosis is an evolutionarily preserved mechanism of controlled cell deletion in nature playing a critical role such as the deletion of redundant or damaged cells in diverse fundamental body processes of multicellular organisms [4]. Its machinery consists of two major inextricably linked activation pathways: the extrinsic pathway via interaction of transmembrane receptors and death ligands (such as Fas, TNF-α, and tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)) [5] [6] and the intrinsic pathway with its consequential leakage of multiple proapoptotic proteins including cytochrome c and Smac/DIABLO from the mitochondria to the cytosol due to mitochondrial membrane potential loss [7] as well as a third, though less wellknown, initiation pathway known as the intrinsic endoplasmic reticulum pathway [6].…”

N1-(3-(Trifluoromethyl)Phenyl) Isophthalamide Derivatives as Promising Inhibitors of Vascular Endothelial Growth Factor Receptor: Pharmacophore-Based Design, Docking, and MM-PBSA/MM-GBSA Binding Energy Estimation

“…In this context, the recent advances in the development of molecular modeling techniques for the search of biologically active compounds cannot be overlooked. The literature describes cases of successful application of pharmacophore screening [28,29], molecular docking, and molecular dynamics [30][31][32] to identify new chemical structures with anti-kinase activity. In addition, the improvements in technical and theoretical background of machine learning algorithms have made it possible to adapt them, inter alia, for the modeling of protein-ligand interactions [33][34][35][36].…”