Developing a Naïve Bayesian Classification Model with PI3Kγ structural features for virtual screening against PI3Kγ: Combining molecular docking and pharmacophore based on multiple PI3Kγ conformations.

Phosphatidylinositol 3-kinase gamma (PI3Kγ) plays a critical role in immune signaling, thus identifying PI3Kγ as a potential therapeutic target. However, developing selective PI3Kγ inhibitors is hampered by the highly conserved structure of the ATP-binding pocket. Focused effort would be needed to improve upon the γ-subtype selectivity of the inhibitors; therefore, in the present study, a naïve Bayesian classification (NBC) model with PI3Kγ structural features that integrates molecular docking and pharmacophore based on multiple PI3Kγ conformations was developed for virtual screening against PI3Kγ to find novel selective PI3Kγ inhibitors. First, the active PI3Kγ inhibitors/decoy dataset was used to prove whether molecular docking or pharmacophore, integrating multiple PI3Kγ conformations always has higher prediction accuracy than that of any single conformation. Second, both internal cross-validation and external prediction revealed that the NBC model combining molecular docking and pharmacophore could significantly improve the enrichment of active PI3Kγ inhibitors. Then, an analog dataset based on JN-PK1 (a reference compound) was constructed and submitted to virtual screening using the optimal NBC model. Finally, a novel inhibitor with higher PI3Kγ inhibitory activity than JN-PK1 was identified through a series of biological assays, showing both good accuracy and significant reliability of the NBC model with the PI3Kγ structural features. We hope that the developed virtual screening strategy will provide valuable guidance for the discovery of novel selective PI3Kγ inhibitors.

Exploring PI3Kγ binding preference with Eganelisib, Duvelisib, and Idelalisib via energetic, pharmacophore and dissociation pathway analyses.

Phosphatidylinositol 3-kinase (PI3K) is the central regulator of cellular functions and is suggested as a target for various diseases; thus, effective PI3K inhibitors provide a promising opportunity for the pharmaceutical intervention of many diseases. Among them, PI3Kγ has received more attention because of its essential role in immune signaling. However, the development of novel selective PI3Kγ inhibitors is a major challenge due to the high sequence homology across the class I PI3K isoforms. Therefore, understanding the substrate specificity and receptor-ligand interaction of PI3Kγ would be an appropriate strategy for the rational design of potent γ-selective inhibitors. In this study, by combining various molecular modeling approaches (including classic and enhanced sampling molecular dynamics (MD) simulations, end-point binding free energy calculations, and pharmacophore models), three quinolinone core-containing inhibitors, Idelalisib/CAL-101, Duvelisib/IPI-145, and Eganelisib/IPI-549, were employed to reveal the selective binding mechanisms targeting PI3Kγ. The classic MD and free energy calculations highlight the significant interaction and some key residues for the selective binding against PI3Kγ. Furthermore, the dissociation pathway analysis based on umbrella sampling simulations reveals that hydrophobic interactions are dominant for binding of the three ligands during the dissociation processes, and cooperation between the P-loop and the ligands always exists in the binding/dissociation process. Finally, the pharmacophore model revealed that IPI-549 contains a unique hydrophobic feature, and PI3Kγ exhibits an important hydrogen bond donor feature of hydrogen amide. These findings may provide some important information for the rational design and optimization of PI3Kγ-selective inhibitors.

Theoretical study of myriocin-binding mechanism targeting serine palmitoyltransferase.

Sphingolipids (SLs) are vital for cells as forming membrane and transducing signals. The first step for de novo biosynthesis of SLs is catalyzed by the pyridoxal-5'-phosphate (PLP)-dependent enzyme serine palmitoyltransferase (SPT), which has been proven to be a promising drug target for treating various diseases. However, there are few SPT-specific inhibitors have been identified so far. Myriocin, a natural fungal product, is confirmed as the most potent inhibitor of SPT and has been widely used, but studies of its molecular mechanism are still underway. Besides, there is no intact co-crystal structure of SPT-binding myriocin until now. Aiming to uncover the interaction mechanism between SPT- and PLP-binding myriocin at the molecular level, a systematic computational strategy was performed in this present study. Firstly, covalent docking was implemented to preliminarily predict the binding pose SPT/PLP-myriocin aldimine and its structurally similar intermediate SPT/PLP-ß-ketoacid aldimine. Secondly, two binding complexes were treated as initial structures to perform molecular dynamics simulations and binding free energy calculations. The calculated docking scores and predicted binding energies were consistent with the reported bioactivities. Finally, the binding mechanism of myriocin binding with SPT was meticulously described, and the key residues making favorable contributions were highlighted. Taken together, the current study could provide some important information and valuable guidance for further rational screening, design, and modification of potent specific SPT inhibitors.

A multi-conformational virtual screening approach based on machine learning targeting PI3Kγ.

Nowadays, more and more attention has been attracted to develop selective PI3Kγ inhibitors, but the unique structural features of PI3Kγ protein make it a very big challenge. In the present study, a virtual screening strategy based on machine learning with multiple PI3Kγ protein structures was developed to screen novel PI3Kγ inhibitors. First, six mainstream docking programs were chosen to evaluate their scoring power and screening power; CDOCKER and Glide show satisfactory reliability and accuracy against the PI3Kγ system. Next, virtual screening integrating multiple PI3Kγ protein structures was demonstrated to significantly improve the screening enrichment rate comparing to that with an individual protein structure. Last, a multi-conformational Naïve Bayesian Classification model with the optimal docking programs was constructed, and it performed a true capability in the screening of PI3Kγ inhibitors. Taken together, the current study could provide some guidance for the docking-based virtual screening to discover novel PI3Kγ inhibitors.

Integrated molecular modeling techniques to reveal selective mechanisms of inhibitors to PI3Kδ with marketed Idelalisib.

Phosphatidylinositol-3-kinase (PI3K) is important for cell proliferation, differentiation, and apoptosis, and the diverse physiological roles of different PI3K isoforms have highlighted the significance of the development of PI3Kδ inhibitors. A large number of PI3Kδ inhibitors have been reported after the FDA approval of Idelalisib, but the clinical use of Idelalisib was limited because of its serious side effects. Therefore, great efforts have been made on the development of PI3Kδ inhibitors with higher selectivity and lower toxicity, but there is no new PI3Kδ inhibitor coming into the market so far. Even so, as the first listed PI3K inhibitor, Idelalisib could be used as an effective tool to investigate the selective inhibition mechanism of PI3Kδ. Thus, in this study, a modeling strategy integrated 3D-QSAR, pharmacophore model, and molecular dynamics simulation was employed to reveal the key chemical characteristics of Idelalisib analogs and the binding pattern between the inhibitors and PI3Kδ. First, the CoMFA model with high statistical significance was built to reveal the general structure-activity relationships. And then, a reliable pharmacophore model with a robust discrimination capability was constructed to expound the main chemical characteristics of the PI3Kδ inhibitors. Finally, molecular dynamics simulation was conducted to explore the binding modes and some key residues refer to δ-selective binding were highlighted with binding-free energy calculation. In summary, these models and results would provide some effective help for the discovery or the rational design of novel PI3Kδ inhibitors.