Showing papers by "Mutasem O. Taha published in 2023"
TL;DR: In this article , the authors used multiple docked poses of STAT3 inhibitors to augment training data for machine learning QSAR modeling, and the best learning algorithms (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to identify critical descriptors that determine anti-STAT3 bioactivity.
Abstract: STAT3 belongs to a family of seven vital transcription factors. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. In this work, we used multiple docked poses of STAT3 inhibitors to augment training data for machine learning QSAR modeling. Ligand–Receptor Contact Fingerprints and scoring values were implemented as descriptor variables. Escalating docking-scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to identify critical descriptors that determine anti-STAT3 bioactivity to be translated into pharmacophore model(s). Two successful pharmacophores were deduced and subsequently used for in silico screening against the National Cancer Institute (NCI) database. A total of 26 hits were evaluated in vitro for their anti-STAT3 bioactivities. Out of which, three hits of novel chemotypes, showed cytotoxic IC50 values in the nanomolar range (35 nM to 6.7 μM). However, two are potent dihydrofolate reductase (DHFR) inhibitors and therefore should have significant indirect STAT3 inhibitory effects. The third hit (cytotoxic IC50 = 0.44 μM) is purely direct STAT3 inhibitor (devoid of DHFR activity) and caused, at its cytotoxic IC50, more than two-fold reduction in the expression of STAT3 downstream genes (c-Myc and Bcl-xL). The presented work indicates that the concept of data augmentation using multiple docked poses is a promising strategy for generating valid machine learning models capable of discriminating active from inactive compounds.
1 citations
TL;DR: In this paper , four cephalosporins, namely, cefixime, ceftriaxone, cephalexin and cefadroxil, were docked into the GSK3β binding pocket.
Abstract: GSK3β is a serine/threonine kinase that has been suggested as a putative drug target for several diseases. Recent studies have reported the beneficial effects of cephalosporin antibiotics in cancer and Alzheimer's disease, implying potential inhibition of GSK3β. To investigate this mechanism, four cephalosporins, namely, cefixime, ceftriaxone, cephalexin and cefadroxil were docked into the GSK3β binding pocket. The third-generation cephalosporins, cefixime and ceftriaxone, exhibited the best docking scores due to the exclusive hydrogen bonding between their aminothiazole group and hinge residues of GSK3β. The stability of top-ranked poses and the possibility of covalent bond formation between the carbonyl carbon of the β-lactam ring and the nucleophilic thiol of Cys-199 were evaluated by molecular dynamics simulations and covalent docking. Finally, the in vitro inhibitory activities of the four cephalosporins were measured against GSK3β with and without preincubation. In agreement with the results of molecular docking, cefixime and ceftriaxone exhibited the best inhibitory activities with IC50 values of 2.55 μM and 7.35 μM, respectively. After 60 minutes preincubation with GSK3β, the IC50 values decreased to 0.55 μM for cefixime and 0.78 μM for ceftriaxone, supporting a covalent bond formation as suggested by molecular dynamics simulations and covalent docking. In conclusion, the third-generation cephalosporins are reported herein as GSK3β covalent inhibitors, offering insight into the mechanism behind their benefits in cancer and Alzheimer's disease.
1 citations
TL;DR: In this article , the authors used multiple docked poses of TTK inhibitors to augment training data for machine learning QSAR modeling, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to determine critical descriptors for predicting anti-TTK bioactivity and for pharmacophore generation.
Abstract: Dual specificity protein kinase threonine/Tyrosine kinase (TTK) is one of the mitotic kinases. High levels of TTK are detected in several types of cancer. Hence, TTK inhibition is considered a promising therapeutic anti‐cancer strategy. In this work, we used multiple docked poses of TTK inhibitors to augment training data for machine learning QSAR modeling. Ligand‐Receptor Contacts Fingerprints and docking scoring values were used as descriptor variables. Escalating docking‐scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to determine critical descriptors for predicting anti‐TTK bioactivity and for pharmacophore generation. Three successful pharmacophores were deduced and subsequently used for in silico screening against the NCI database. A total of 14 hits were evaluated in vitro for their anti‐TTK bioactivities. One hit of novel chemotype showed reasonable dose‐response curve with experimental IC50 of 1.0 μM. The presented work indicates the validity of data augmentation using multiple docked poses for building successful machine learning models and pharmacophore hypotheses.
TL;DR: In this paper , Chitosan (CS) was grafted with 2,4-dichlorophenoxyacetic acid to promote facile adsorption of CO2.
TL;DR: In this paper , the potential inhibitory activity of asenapine against central nervous system CaMKII isozymes was investigated using docking experiments and enzymatic assay using docking settings were validated using ROC analysis.
Abstract:
The aim of this study is to investigated the potential inhibitory activity of asenapine against central nervous system CaMKII isozymes as a potential repurposing study using docking experiments and enzymatic assay.
The Ca2+/calmodulin-dependent protein kinase II (CaMKII) is a multifunctional protein kinase ubiquitously expressed throughout the brain. Emerging biological data have indicated that inhibiting central nervous system CaMKII isoforms, namely, CaMKIIα and CaMKIIβ, may be a promising therapeutic strategy for the potential treatment of many neurological diseases including schizophrenia, depression, epilepsy, and learning deficit.
1- Study the possible attractive interactions of asenapine within the binding sites of the central CaMKII isozymes.
2- Evaluate the inhibitory activities of asenapine against central CaMKII isozymes.
Docking experiments of asenapine and other known CaMKII inhibitors were performed. Docking settings were validated using ROC analysis. After that, the inhibitory activities of asenapine against central CaMKII alpha and beta were evaluated by enzymatic assay.
Docking and scoring experiments of asenapine showed several binding interactions anchoring asenapine within CaMKIIα and CaMKIIβ catalytic sites while enzymatic assay results revealed that asenapine can inhibit CaMKIIα and CaMKIIβ in the micromolar range.
Our study provides evidence that asenapine can serve as a promising lead for the development of new CaMKIIα and CaMKIIβ inhibitors. Moreover, this study reinforces how the investment in drug repurposing could boost the drug discovery process.