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Wee1-like protein kinase (Wee1) is a tyrosine kinase that regulates the G 2 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. 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 partial least square (PLS) statistics (R 2  = 0.9212, Q 2  = 0.7457), was selected and validated. The validated model was used as a three-dimensional (3D) search query for databases virtual screening. The filtered molecules were further analyzed and refined by Lipinski's rule of 5, multiple docking procedures (high throughput virtual screening (HTVS), standard precision (SP), genetic optimization for ligand docking (GOLD), extra precision (XP), and unique quantum polarized ligand docking (QPLD)); absorption, distribution, metabolism, excretion, and toxicity (ADMET) screening; and the Prime/molecular mechanics generalized born surface area (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. Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) based on MD simulation and the energy contribution per residue to the binding energy were calculated. In the end, three hits with good stabilization and affinity to protein were identified. Communicated by Ramaswamy H. Sarma.

Filamentous temperature-sensitive protein Z (FtsZ), playing a key role in bacterial cell division, is regarded as a promising target for the design of antimicrobial agent. This study is looking for potential high-efficiency FtsZ inhibitors. Ligand-based pharmacophore and E-pharmacophore, virtual screening and molecular docking were used to detect promising FtsZ inhibitors, and molecular dynamics simulation was used to study the stability of protein-ligand complexes in this paper. Sixty-three inhibitors from published literatures with pIC50 ranging from 2.483 to 5.678 were collected to develop ligand-based pharmacophore model. 4DXD bound with 9PC was selected to develop the E-pharmacophore model. The pharmacophore models validated by test set method and decoy set were employed for virtual screening to exclude inactive compounds against ZINC database. After molecular docking, ADME analysis, IFD docking and MM-GBSA, 8 hits were identified as potent FtsZ inhibitors. A 50 ns molecular dynamics simulation was implemented on the compounds to assess the stability between potent inhibitors and FtsZ. The results indicated that the candidate compounds had a high docking score and were strongly combined with FtsZ by forming hydrogen bonding interactions with key amino acid residues, and van der Waals forces and hydrophobic interactions had significant contribution to the stability of the binding. Molecular dynamics simulation results showed that the protein-ligand compounds performed well in both the stability and flexibility of the simulation process. [ABSTRACT FROM AUTHOR]

The 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3) is a master regulator of glycolysis in cancer cells by synthesizing fructose-2,6-bisphosphate (F-2,6-BP), a potent allosteric activator of phosphofructokinase-1 (PFK-1), which is a rate-limiting enzyme of glycolysis. PFKFB3 is an attractive target for cancer treatment. It is valuable to discover promising inhibitors by using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking and molecular dynamics simulation. Twenty molecules with known activity were used to build 3D-QSAR pharmacophore models. The best pharmacophore model was ADHR called Hypo1, which had the highest correlation value of 0.98 and the lowest RMSD of 0.82. Then, the Hypo1 was validated by cost value method, test set method and decoy set validation method. Next, the Hypo1 combined with Lipinski's rule of five and ADMET properties were employed to screen databases including Asinex and Specs, total of 1,048,159 molecules. The hits retrieved from screening were docked into protein by different procedures including HTVS, SP and XP. Finally, nine molecules were picked out as potential PFKFB3 inhibitors. The stability of PFKFB3-lead complexes was verified by 40 ns molecular dynamics simulation. The binding free energy and the energy contribution of per residue to the binding energy were calculated by MM-PBSA based on molecular dynamics simulation. [ABSTRACT FROM AUTHOR]