Phosphoinositide 3-kinase gamma (PI3Kγ) has been regarded as a promising drug target for the treatment of various diseases, and the diverse physiological roles of class I PI3K isoforms (α, β, δ, and γ) highlight the importance of isoform selectivity in the development of PI3Kγ inhibitors. However, the high structural conservation among the PI3K family makes it a big challenge to develop selective PI3Kγ inhibitors. A novel machine learning-based virtual screening with multiple PI3Kγ protein structures was developed to discover novel PI3Kγ inhibitors. A large chemical database was screened using the virtual screening model, the top-ranked compounds were then subjected to a series of bio-evaluations, which led to the discovery of JN-KI3. The selective inhibition mechanism of JN-KI3 against PI3Kγ was uncovered by a theoretical study. 49 hits were identified through virtual screening, and the cell-free enzymatic studies found that JN-KI3 selectively inhibited PI3Kγ at a concentration as low as 3,873 nM but had no inhibitory effect on Class IA PI3Ks, leading to the selective cytotoxicity on hematologic cancer cells. Meanwhile, JN-KI3 potently blocked the PI3K signaling, finally led to distinct apoptosis of hematologic cell lines at a low concentration. Lastly, the key residues of PI3Kγ and the structural characteristics of JN-KI3, which both would influence γ isoform-selective inhibition, were highlighted by systematic theoretical studies. The developed virtual screening model strongly manifests the robustness to find novel PI3Kγ inhibitors. JN-KI3 displays a specific cytotoxicity on hematologic tumor cells, and significantly promotes apoptosis associated with the inhibition of the PI3K signaling, which depicts PI3Kγ as a potential target for the hematologic tumor therapy. The theoretical results reveal that those key residues interacting with JN-KI3 are less common compared to most of the reported PI3Kγ inhibitors, indicating that JN-KI3 has novel structural characteristics as a selective PIK3γ inhibitor.

Triple negative breast cancer (TNBC) is a greatly aggressive subtype of breast cancer with high recurrence and mortality rates. Chemotherapy as a primary treatment for cancer is limited due to toxic side effects and drug resistance. Therefore, low toxicity and more effective breast cancer therapeutic approaches are greatly desired. In this study, a strategy which using ZIF-90 nanoparticles co-deliver Ce6-anti-miR-21 and Ce6-anti-miR-155 into the tumor cells was developed. Due to the pH responsive drug release of ZIF-90, antisense oligonucleotides (anti-miRNAs) and photosensitizers are able to be efficiently released inside tumor microenvironment. The nano delivery system captures overexpressed oncogenic miRNAs while the photosensitizer Ce6 generates ROS under light irradiation to effectively induce the apoptosis of tumor cell. This combinatorial effect was verified by results showing that the purposed therapic method could effectively inhibit tumor cell proliferation and metastasis. The concept of antisense oligonucleotide combined with photodynamic therapy has great potential in cancer treatment or adjuvant therapy.

Glycogen synthase kinase-3β (GSK3β) is associated with various key biological processes, and it has been considered as a critical therapeutic target for the treatment of many diseases. However, it is a big challenge to develop ATP-competition GSK3β inhibitors because of the high sequence homology with other kinases. In this work, a novel parallel virtual screening strategy based on multiple GSK3β protein structures, integrating molecular docking, complex-based pharmacophore, and naive Bayesian classification, was developed to screen a large chemical database, the 50 compounds with top-scores then underwent a luminescent kinase assay, which led to the discovery of two GSK3β inhibitor hits. The high screening enrichment rate indicates the reliability and practicability of the integrated protocol. Finally, molecular docking and molecular dynamics simulation were employed to investigate the binding modes of the GSK3β inhibitors, and some “hot residues” critical to GSK3β affinity were highlighted. The present study may provide some valuable guidance for the development of novel GSK3β inhibitors.

Background: Glycogen synthase kinase-3 (GSK3) is associated with various key biological processes and has been considered as an important therapeutic target for the treatment of many diseases. Great efforts have been made on the development of GSK3 inhibitors, especially ATP-competitive GSK3β inhibitor, but it is still a great challenge to develop selective GSK3β inhibitors because of the high sequence homology with other kinases. Objective: In order to reveal the selectivity mechanisms of GSK3β inhibition at the molecular level, a series of ATP-competitive GSK3β inhibitor was analyzed by a systematic computational method, combining 3DQSAR, molecular docking, molecular dynamic simulations and free energy calculations. Methods: Firstly, 3D-QSAR with CoMFA was built to explore the general structure activity relationships. Secondly, CDOCKER and Flexible docking were employed to predicted the reasonable docking poses of all studied inhibitors. And then, both GSK3β and CDK2 complexes were selected to conduct molecular dynamics simulations. Finally, the free energy calculations were employed to find the key selective-residues. Results: CoMFA model suggested the steric, hydrophobic fields play key roles in the bioactivities of inhibitors, and the binding mechanisms were well analyzed through molecular docking. The binding free energies predicted are in good agreement with the experimental bioactivities and the free energy calculations showed that the binding of GSK3β/inhibitors was mainly contributed from hydrogen bonding and hydrophobic interaction. Conclusion: Some key residues for selective binding were highlighted, which may afford important guidance for the rational design of novel ATP-competitive GSK3β inhibitors.