Discovery of novel selective PI3Kγ inhibitors through combining machine learning-based virtual screening with multiple protein structures and bio-evaluation

Machine learning models to identify lead compound and substitution optimization to have derived energetics and conformational stability through docking and MD simulations for sphingosine kinase 1

Sphingosine kinases (SphKs) are a group of important enzymes that circulate at low micromolar concentrations in mammals and have received considerable attention due to the roles they play in a broad array of biological processes including apoptosis, mutagenesis, lymphocyte migration, radio- and chemo-sensitization, and angiogenesis. In the present study, we constructed three classification models by four machine learning (ML) algorithms including naive bayes (NB), support vector machine (SVM), logistic regression, and random forest from 395 compounds. The generated ML models were validated by fivefold cross validation. Five different scaffold hit fragments resulted from SVM model-based virtual screening and docking results indicate that all the five fragments exhibit common hydrogen bond interaction a catalytic residue of SphK1. Further, molecular dynamics (MD) simulations and binding free energy calculation had been carried out with the identified five fragment leads and three cocrystal inhibitors. The best 15 fragments were selected. Molecular dynamics (MD) simulations showed that among these compounds, 7 compounds have favorable binding energy compared with cocrystal inhibitors. Hence, the study showed that the present lead fragments could act as potential inhibitors against therapeutic target of cancers and neurodegenerative disorders.

Molecular modeling strategy for detailing the primary mechanism of action of copanlisib to PI3K: combined ligand-based and target-based approach

Since dysregulation of the phosphatidylinositol 3-kinase (PI3K) signaling pathway is associated with the pathogenesis of cancer, inflammation, and autoimmunity, PI3K has emerged as an attractive target for drug development. Although copanlisib is the first pan-PI3K inhibitor to be approved for clinical use, the precise mechanism by which it acts on PI3K has not been fully elucidated. To reveal the binding mechanisms and structure-activity relationship between PI3K and copanlisib, a comprehensive modeling approach that combines 3D-quantitative structure-activity relationship (3D-QSAR), pharmacophore model, and molecular dynamics (MD) simulation was utilized. Initially, the structure-activity relationship of copanlisib and its derivatives were explored by constructing a 3D-QSAR. Then, the key chemical characteristics were identified by building common feature pharmacophore models. Finally, MD simulations were performed to elucidate the important interactions between copanlisib and different PI3K subtypes, and highlight the key residues for tight-binding inhibitors. The present study uncovered the principal mechanism of copanlisib's action on PI3K at the theoretical level, and these findings might provide guidance for the rational design of pan-PI3K inhibitors.Communicated by Ramaswamy H. Sarma.

Discovery of novel ULK1 inhibitors through machine learning-guided virtual screening and biological evaluation

Aim: Build a virtual screening model for ULK1 inhibitors based on artificial intelligence.Materials & methods: Build machine learning and deep learning classification models and combine molecular docking and biological evaluation to screen ULK1 inhibitors from 13 million compounds. And molecular dynamics was used to explore the binding mechanism of active compounds.Results & conclusion: Possibly due to less available training data, machine learning models significantly outperform deep learning models. Among them, the Naive Bayes model has the best performance. Through virtual screening, we obtained three inhibitors with IC50 of μM level and they all bind well to ULK1. This study provides an efficient virtual screening model and three promising compounds for the study of ULK1 inhibitors.

Identification of novel covalent JAK3 inhibitors through consensus scoring virtual screening: integration of common feature pharmacophore and covalent docking

Accumulated research strongly indicates that Janus kinase 3 (JAK3) is intricately involved in the initiation and advancement of a diverse range of human diseases, underscoring JAK3 as a promising target for therapeutic intervention. However, JAK3 shows significant homology with other JAK family isoforms, posing substantial challenges in the development of JAK3 inhibitors. To address these limitations, one strategy is to design selective covalent JAK3 inhibitors. Therefore, this study introduces a virtual screening approach that combines common feature pharmacophore modeling, covalent docking, and consensus scoring to identify novel inhibitors for JAK3. First, common feature pharmacophore models were constructed based on a selection of representative covalent JAK3 inhibitors. The optimal qualitative pharmacophore model proved highly effective in distinguishing active and inactive compounds. Second, 14 crystal structures of the JAK3-covalent inhibitor complex were chosen for the covalent docking studies. Following validation of the screening performance, 5TTU was identified as the most suitable candidate for screening potential JAK3 inhibitors due to its higher predictive accuracy. Finally, a virtual screening protocol based on consensus scoring was conducted, integrating pharmacophore mapping and covalent docking. This approach resulted in the discovery of multiple compounds with notable potential as effective JAK3 inhibitors. We hope that the developed virtual screening strategy will provide valuable guidance in the discovery of novel covalent JAK3 inhibitors.

Designing Small Molecule PI3Kγ Inhibitors: A Review of Structure-Based Methods and Computational Approaches

The PI3K/AKT/mTOR pathway plays critical roles in a wide array of biological processes. Phosphatidylinositol 3-kinase gamma (PI3Kγ), a class IB PI3K family member, represents a potential therapeutic opportunity for the treatment of cancer, inflammation, and autoimmunity. In this Perspective, we provide a comprehensive overview of the structure, biological function, and regulation of PI3Kγ. We also focus on the development of PI3Kγ inhibitors over the past decade and emphasize their binding modes, structure-activity relationships, and pharmacological activities. The application of computational technologies and artificial intelligence in the discovery of novel PI3Kγ inhibitors is also introduced. This review aims to provide a timely and updated overview on the strategies for targeting PI3Kγ.

Gingerenone A Attenuates Ulcerative Colitis via Targeting IL-17RA to Inhibit Inflammation and Restore Intestinal Barrier Function

Ulcerative colitis (UC) is a complicated and recurrent intestinal disease. Currently available drugs for UC treatment are scarce, therefore, novel therapeutic drugs for the UC are urgently to be developed. Gingerenone A (GA) is a phenolic compound known for its anti-inflammatory effect, but its effect on UC remains unknown. Here, it is shown that GA protects mice against UC, which is closely associated with inhibiting intestinal mucosal inflammation and enhancing intestinal barrier integrity in vivo and in vitro. Of note, RNA sequencing analysis demonstrates an evident correlation with IL-17 signaling pathway after GA treatment, and this effect is further corroborated by Western blot. Mechanistically, GA directly interacts with IL-17RA protein through pull-down, surface plasmon resonance analysis and molecular dynamics simulation. Importantly, lentivirus-mediated IL-17RA/Act1 knock-down or GA co-treatment with brodalumab/ixekizumab significantly impairs the protective effects of GA against DSS-induced inflammation and barrier dysfunction, suggesting a critical role of IL-17RA signaling for GA-mediated protection against UC. Overall, these results indicate that GA is an effective agent against UC mainly through the direct binding of IL-17RA to inhibit inflammatory signaling activation.

Isovaleryl Sucrose Esters from Atractylodes japonica and Their Cytotoxic Activity

Cancer represents one of the most significant health challenges currently facing humanity, and plant-derived antitumour drugs represent a prominent class of anticancer medications in clinical practice. Isovaleryl sucrose esters, which are natural constituents, have been identified as having potential antitumour effects. However, the mechanism of action remains unclear. In this study, 12 isovaleryl sucrose ester components, including five new (1-5) and seven known compounds (6-12), were isolated from the roots of Atractylodes japonica. The structures of the compounds were elucidated using 1D and 2D-NMR spectroscopy, complemented by HR-ESI-MS mass spectrometry. The cytotoxic activities of all the compounds against human colon cancer cells (HCT-116) and human lung adenocarcinoma cells (A549) were also evaluated using the CCK8 assay. The results demonstrated that compounds 2, 4, and 6 were moderately inhibitory to HCT-116 cells, with IC50 values of 7.49 ± 0.48, 9.03 ± 0.21, and 13.49 ± 1.45 μM, respectively. Compounds 1 and 6 were moderately inhibitory to A549, with IC50 values of 8.36 ± 0.77 and 7.10 ± 0.52 μM, respectively. Molecular docking revealed that compounds 1-9 exhibited a stronger affinity for FGFR3 and BRAF, with binding energies below -7 kcal/mol. Compound 2 exhibited the lowest binding energy of -10.63 kcal/mol to FGFR3. We screened the compounds with lower binding energies, and the protein-ligand complexes already obtained after molecular docking were subjected to exhaustive molecular dynamics simulation experiments, which simulated the dynamic behaviour of the molecules in close proximity to the actual biological environment, thus providing a deeper understanding of their functions and interaction mechanisms. The present study provides a reference for the development and use of iso-valeryl sucrose esters in the antitumour field.

Optimization of virtual screening against phosphoinositide 3-kinase delta: Integration of common feature pharmacophore and multicomplex-based molecular docking

Extensive research has accumulated which suggests that phosphatidylinositol 3-kinase delta (PI3Kδ) is closely related to the occurrence and development of various human diseases, making PI3Kδ a highly promising drug target. However, PI3Kδ exhibits high homology with other members of the PI3K family, which poses significant challenges to the development of PI3Kδ inhibitors. Therefore, in the present study, a hybrid virtual screening (VS) approach based on a ligand-based pharmacophore model and multicomplex-based molecular docking was developed to find novel PI3Kδ inhibitors. 13 crystal structures of the human PI3Kδ-inhibitor complex were collected to establish models. The inhibitors were extracted from the crystal structures to generate the common feature pharmacophore. The crystallographic protein structures were used to construct a naïve Bayesian classification model that integrates molecular docking based on multiple PI3Kδ conformations. Subsequently, three VS protocols involving sequential or parallel molecular docking and pharmacophore approaches were employed. External predictions demonstrated that the protocol combining molecular docking and pharmacophore resulted in a significant improvement in the enrichment of active PI3Kδ inhibitors. Finally, the optimal VS method was utilized for virtual screening against a large chemical database, and some potential hit compounds were identified. We hope that the developed VS strategy will provide valuable guidance for the discovery of novel PI3Kδ inhibitors.

Evaluation of the anti-inflammatory effects of PI3Kδ/γ inhibitors for treating acute lung injury

Phosphatidylinositol 3-kinase delta (PI3Kδ) and gamma (PI3Kγ) are predominantly located in immune and hematopoietic cells. It is well-established that PI3Kδ/γ plays important roles in the immune system and participates in inflammation; hence, it could be a potential target for anti-inflammatory therapy. Currently, several PI3K inhibitors are used clinically to treat cancers with aberrant PI3K signaling; however, their role in treating acute respiratory inflammatory diseases has rarely been explored. Herein, we investigated the potential anti-inflammatory activities of several pharmacological PI3K inhibitors, including marketed drugs idelalisib (PI3Kδ), duvelisib (PI3Kδ/γ), and copanlisib (pan-PI3K with preferential α/δ) and the clinical drug eganelisib (PI3Kγ), for treating acute lung injury (ALI). In the lipopolysaccharide-induced RAW264.7 macrophage inflammatory model, the four inhibitors significantly suppressed proinflammatory cytokine expression by inhibiting the PI3K signaling pathway. Oral administration of PI3K inhibitors markedly improved lung injury in a murine model of ALI. PI3K pathway inhibition decreased inflammatory cell infiltration and totalprotein levels, as well as reduced the expression of associated lung inflammatory factors. Collectively, all four representative PI3K inhibitors exerted prominent anti-inflammatory properties, indicating that PI3K δ and/or γ inhibition could be ideal targets to treat respiratory inflammatory diseases by reducing the inflammatory response. The findings of the current study provide a new basis for utilizing PI3K inhibitors to treat acute respiratory inflammatory diseases.

LRFN5 and OLFM4 as novel potential biomarkers for major depressive disorder: a pilot study

Evidences have shown that both LRFN5 and OLFM4 can regulate neural development and synaptic function. Recent genome-wide association studies on major depressive disorder (MDD) have implicated LRFN5 and OLFM4, but their expressions and roles in MDD are still completely unclear. Here, we examined serum concentrations of LRFN5 and OLFM4 in 99 drug-naive MDD patients, 90 drug-treatment MDD patients, and 81 healthy controls (HCs) using ELISA methods. The results showed that both LRFN5 and OLFM4 levels were considerably higher in MDD patients compared to HCs, and were significantly lower in drug-treatment MDD patients than in drug-naive MDD patients. However, there were no significant differences between MDD patients who received a single antidepressant and a combination of antidepressants. Pearson correlation analysis showed that they were associated with the clinical data, including Hamilton Depression Scale score, age, duration of illness, fasting blood glucose, serum lipids, and hepatic, renal, or thyroid function. Moreover, these two molecules both yielded fairly excellent diagnostic performance in diagnosing MDD. In addition, a combination of LRFN5 and OLFM4 demonstrated a better diagnostic effectiveness, with an area under curve of 0.974 in the training set and 0.975 in the testing set. Taken together, our data suggest that LRFN5 and OLFM4 may be implicated in the pathophysiology of MDD and the combination of LRFN5 and OLFM4 may offer a diagnostic biomarker panel for MDD.

Developing new PI3Kγ inhibitors by combining pharmacophore modeling, molecular dynamic simulation, molecular docking, fragment-based drug design, and virtual screening

Since dysregulation of the phosphatidylinositol 3-kinase gamma (PI3Kγ) signaling pathway is associated with the pathogenesis of cancer, inflammation, and autoimmunity, PI3Kγ has emerged as an attractive target for drug development. IPI-549 is the only selective PI3Kγ inhibitor that has advanced to clinical trials, thus, IPI-549 could serve as a promising template for designing novel PI3Kγ inhibitors. In this present study, a modeling strategy consisting of common feature pharmacophore modeling, receptor-ligand pharmacophore modeling, and molecular dynamics simulation was utilized to identify the key pharmacodynamic characteristic elements of the target compound and the key residue information of the PI3Kγ interaction with the inhibitors. Then, 10 molecules were designed based on the structure-activity relationships, and some of them exhibited satisfactory predicted binding affinities to PI3Kγ. Finally, a hierarchical multistage virtual screening method, involving the developed common feature and receptor-ligand pharmacophore model and molecular docking, was constructed for screening the potential PI3Kγ inhibitors. Overall, we hope these findings would provide some guidance for the development of novel PI3Kγ inhibitors.

Binding and selectivity studies of phosphatidylinositol 3-kinase (PI3K) inhibitors

Overexpression of the Phosphatidylinositol 3-kinase (PI3K) proteins have been observed in cancer cells. Targeting the phosphatidylinositol 3-kinase (PI3K) signaling transduction pathway by inhibition of the PI3K substrate recognition sites has been proved to be an effective approach to block cancer progression. Many PI3K inhibitors have been developed. Seven drugs have been approved by the US FDA with a mechanism of targeting the phosphatidylinositol 3-kinase/protein kinase-B/mammalian target of rapamycin (PI3K/AKT/mTOR) signaling pathway. In this study, we used docking tools to investigate selective binding of ligands toward four different subtypes of PI3Ks (PI3Kα, PI3Kβ, PI3Kγ and PI3Kδ). The affinity predicted from both the Glide dock and the Movable-Type (MT)-based free energy calculations agreed well with the experimental data. The validation of our predicted methods with a large dataset of 147 ligands showed very small mean errors. We identified residues that may dictate the subtype-specific binding. Particularly, residues Asp964, Ser806, Lys890 and Thr886 of PI3Kγ might be utilized for PI3Kγ-selective inhibitor design. Residues Val828, Trp760, Glu826 and Tyr813 may be important for PI3Kδ-selective inhibitor binding.

Investigation into the anti-airway inflammatory role of the PI3Kγ inhibitor JN-PK1: An in vitro and in vivo study

Phosphatidylinositol 3-kinase gamma (PI3Kγ) has been proven to be a potential target for the treatment of inflammatory diseases of the airway; however, there are few reports of selective PI3Kγ inhibitors being used in the field of airway inflammation thus far. Herein, a study employing in vitro and in vivo methodologies was carried out to assess the anti-airway inflammatory effects of JN-PK1, a selective PI3Kγ inhibitor. In RAW264.7 macrophages, JN-PK1 inhibited PI3Kγ-dependent, cellular C5a-induced AKT Ser473 phosphorylation in a concentration- and time-dependent manner and had no significant effect on cell viability.Furthermore, JN-PK1 significantly suppressed LPS-induced, proinflammatory cytokine expression and nitric oxide production through inhibition of the PI3K signaling pathway in RAW264.7 cells. Then, a murine asthma model was established to evaluate the anti-airway inflammation effect of JN-PK1. BALB/c mice were sensitized and challenged with ovalbumin (OVA) to develop an inflammatory response, fibrosis formation, and other airway changes similar to the symptomatology of asthma in humans. Oral administration of JN-PK1 remarkably attenuated OVA-induced asthma in association with the inhibition of the PI3K signaling pathway. That is to say, the oral administration significantly inhibited increases in inflammatory cell counts and reduced T-helper type 2 cytokine production in bronchoalveolar lavage fluid. Pulmonary histological studies showed that oral administration of JN-PK1 not only reduced the infiltration of inflammatory cells but also retarded airway inflammation and fibration. Taken together, JN-PK1 could be developed as a promising candidate for inflammation therapy, and our findings support some potential for therapeutic inhibition of PI3Kγ to treat inflammatory airway diseases.

Target-Based Small Molecule Drug Discovery for Colorectal Cancer: A Review of Molecular Pathways and In Silico Studies

Colorectal cancer is one of the most prevalent cancer types. Although there have been breakthroughs in its treatments, a better understanding of the molecular mechanisms and genetic involvement in colorectal cancer will have a substantial role in producing novel and targeted treatments with better safety profiles. In this review, the main molecular pathways and driver genes that are responsible for initiating and propagating the cascade of signaling molecules reaching carcinoma and the aggressive metastatic stages of colorectal cancer were presented. Protein kinases involved in colorectal cancer, as much as other cancers, have seen much focus and committed efforts due to their crucial role in subsidizing, inhibiting, or changing the disease course. Moreover, notable improvements in colorectal cancer treatments with in silico studies and the enhanced selectivity on specific macromolecular targets were discussed. Besides, the selective multi-target agents have been made easier by employing in silico methods in molecular de novo synthesis or target identification and drug repurposing.