QM/MM and molecular dynamics investigation of the mechanism of covalent inhibition of TAK1 kinase

Acetic Acid Mediated for One-Pot Synthesis of Novel Pyrazolyl s-Triazine Derivatives for the Targeted Therapy of Triple-Negative Breast Tumor Cells (MDA-MB-231) via EGFR/PI3K/AKT/mTOR Signaling Cascades

Here, we described the synthesis of novel pyrazole-s-triazine derivatives via an easy one-pot procedure for the reaction of β-dicarbonyl compounds (ethylacetoacetate, 5,5-dimethyl-1,3-cyclohexadione or 1,3-cyclohexadionone) with N,N-dimethylformamide dimethylacetal, followed by addition of 2-hydrazinyl-4,6-disubstituted-s-triazine either in ethanol-acetic acid or neat acetic acid to afford a novel pyrazole and pyrazole-fused cycloalkanone systems. The synthetic protocol proved to be efficient, with a shorter reaction time and high chemical yield with broad substrates. The new pyrazolyl-s-triazine derivatives were tested against the following cell lines: MCF-7 (breast cancer); MDA-MB-231 (triple-negative breast cancer); U-87 MG (glioblastoma); A549 (non-small cell lung cancer); PANC-1 (pancreatic cancer); and human dermal fibroblasts (HDFs). The cell viability assay revealed that most of the s-triazine compounds induced cytotoxicity in all the cell lines tested. However, compounds 7d, 7f and 7c, which all have a piperidine or morpholine moiety with one aniline ring or two aniline rings in their structures, were the most effective. Compounds 7f and 7d showed potent EGFR inhibitory activity with IC₅₀ values of 59.24 and 70.3 nM, respectively, compared to Tamoxifen (IC₅₀ value of 69.1 nM). Compound 7c exhibited moderate activity, with IC₅₀ values of 81.6 nM. Interestingly, hybrids 7d and 7f exerted remarkable PI3K/AKT/mTOR inhibitory activity with 0.66/0.82/0.80 and 0.35/0.56/0.66-fold, respectively, by inhibiting their concentrations to 4.39, 37.3, and 69.3 ng/mL in the 7d-treatment, and to 2.39, 25.34 and 57.6 ng/mL in the 7f-treatment compared to the untreated control.

Cofactor-free ActVA-Orf6 monooxygenase catalysis via proton-coupled electron transfer: A QM/MM study

Uncovering the comprehensive catalytic mechanism for the activation of triplet O2 through metal-free and cofactor-free oxidases and oxygenases remains one of the most challenging questions in the area of enzymatic...

Docking-generated multiple ligand poses for bootstrapping bioactivity classifying Machine Learning: Repurposing covalent inhibitors for COVID-19-related TMPRSS2 as case study

In the present work we introduce the use of multiple docked poses for bootstrapping machine learning-based QSAR modelling. Ligand-receptor contact fingerprints are implemented as descriptor variables. We implemented this method for the discovery of potential inhibitors of the serine protease enzyme TMPRSS2 involved the infectivity of coronaviruses. Several machine learners were scanned, however, Xgboost, support vector machines (SVM) and random forests (RF) were the best with testing set accuracies reaching 90%. Three potential hits were identified upon using the method to scan known untested FDA approved drugs against TMPRSS2. Subsequent molecular dynamics simulation and covalent docking supported the results of the new computational approach.