Virtual docking screening and quantitative structure-activity relationship studies to explore AKT and PI3K inhibitors acting on mTOR in cancers by theoretical biology and medical modeling.

Introduction: The mechanistic target of rapamycin (mTOR) coordinates the growth and metabolism of eukaryotic cells with a central role in the regulation of many fundamental cellular processes. It is strongly connected to phosphatidylinositol 3-kinase (PI3K) and AKT signaling. Activation of the PI3K/AKT/mTOR pathway leads to a profound disruption in the control of cell growth and survival, which ultimately leads to competitive growth advantage, metastatic competence, angiogenesis and therapeutic resistance. Material and methods: To explore the common competitive adenosine triphosphate (ATP) inhibitors PI3K/AKT and PI3K/mTOR, we built a 2D mTOR-SAR model that predicted the bioactivity of AKT and PI3K inhibitors towards mTOR. The interaction of the best inhibitors was evaluated by docking analysis and compared to that of the standard AZ8055 and XL388 inhibitors. Results: A mechanistic target of rapamycin-quantitative structure-activity relationship (mTOR-QSAR) model with a correlation coefficient (R2) of 0.80813 and a root mean square error of 0.17756 was obtained, validated and evaluated by a cross-validation leave-one-out method. The best predicted AKT and PI3K inhibitor pIC50 activities were 9.36-9.95 and 9.23-9.87 respectively. Conclusions: After docking and several comparisons, the inhibitors with better predictions showed better affinity and interaction with mTOR compared to AZ8055 and XL388, so we have found that 2 AKT inhibitors and 9 mTOR inhibitors met the Lipinski and Veber criteria and could be future drugs.

Selective Non-toxics Inhibitors Targeting DHFR for Tuberculosis and Cancer Therapy: Pharmacophore Generation and Molecular Dynamics Simulation.

Dihydrofolate reductase (DHFR) is a crucial enzyme that catalyzes the conversion of folic acid. Its reserved properties and significance in both human (h-DHFR) and mycobacterium (mt-DHFR) make it a challenging target for developing drugs against cancer and bacterial infections. Although methotrexate (MTX) is commonly used for cancer therapy and bacterial infections, it has a toxic profile. In this study, we aimed to identify selective and non-toxic inhibitors against h-DHFR and mt-DHFR using an in silico approach. From a data set of 8 412 inhibitors, 11 compounds passed the toxicity and drug-likeness tests, and their interaction with h-DHFR and mt-DHFR was studied by performing molecular docking. To evaluate the inhibitory activity of the compounds against mt-DHFR, five known reference ligands and the natural ligand (dihydrofolate) were used to generate a pharmacophoric map. Two potential selective inhibitors for mt-DHFR and h-DHFR were selected for further investigation using molecular dynamics for 100 ns. As a result, BDBM18226 was identified as the best compound selective for mt-DHFR, non-toxic, with five features listed in the map, with a binding energy of -9.6 kcal/mol. BDBM50145798 was identified as a non-toxic selective compound with a better affinity than MTX for h-DHFR. Molecular dynamics of the two best ligands suggest that they provide more stable, compact, and hydrogen bond interactions with the protein. Our findings could significantly expand the chemical space for new mt-DHFR inhibitors and provide a non-toxic alternative toward h-DHFR for the respective treatment of tuberculosis and cancer therapy.

In Silico Analyses of All STAT3 Missense Variants Leading to Explore Divergent AD-HIES Clinical Phenotypes.

Autosomal dominant hyper-IgE syndrome (AD-HIES) is linked to dominant negative mutations of the STAT3 protein whose molecular basis for dysfunction is unclear and presenting with a variety of clinical manifestations with only supportive treatment. To establish the relationship between the impact of STAT3 mutations in different domains and the severity of the clinical manifestations, 105 STAT3 mutations were analyzed for their impact on protein stability, flexibility, function, and binding affinity using in Silico approaches. Our results showed that 73% of the studied mutations have an impact on the physicochemical properties of the protein, altering the stability, flexibility and function to varying degrees. In particular, mutations affecting the DNA binding domain (DBD) and the Src Homology 2 (SH2) have a significant impact on the protein structure and disrupt its interaction either with DNA or other STAT3 to form a heterodomain complex, leading to severe clinical phenotypes. Collectively, this study suggests that there is a close relationship between the domain involving the mutation, the degree of variation in the properties of the protein and the degree of loss of function ranging from partial loss to complete loss, explaining the variability of clinical manifestations between mild and severe.