Molecular Diversity最新文献

Transition-metal-catalyzed reductive cross-coupling is highly efficient for forming C-C bonds. It earns its limelight from its application by coupling unreactive electrophilic substrates to synthesize a variety of carbon-carbon bonds with various hybridizations (sp, sp², and sp³), late-stage functionalization, and bioactive molecules' synthesis. Reductive cross-coupling is challenging to bring selectivity but promising approach. Cobalt is comparatively more affordable than other highly efficient metals e.g., palladium and nickel but cobalt catalysis is still facing efficacy challenges. Researchers are trying to harness the maximum out of cobalt's catalytic properties. Shortly, with efficiency achieved combined with the affordability of cobalt, it will revolutionize industrial applications. This review gives insight into the core of cobalt-catalyzed reductive cross-coupling reactions with a variety of substrates forming a range of differently hybridized coupled products.

The anti-atherogenic potential of liver X receptors (LXRs) has been attributed to their inhibitory role in macrophage-mediated inflammation and promotion of reverse cholesterol transport. This study aimed to evaluate the efficacy of an LXR agonist, 1,8-cineole (Eucalyptol), in atherosclerosis through network pharmacology, molecular docking, and in vivo efficacy studies in high-fat-diet-induced atherosclerosis in hamsters. Network pharmacology analysis was performed by identifying potential targets of 1,8-Cineole and atherosclerosis, followed by the construction of component-target-disease and protein-protein interaction networks. Gene Ontology and KEGG pathway enrichment analysis of targets were performed. The top 5 targets were selected for molecular docking studies. Atherosclerosis was induced in male Golden Syrian hamsters, and the results of network pharmacology were verified. Fifty-one overlapped targets were identified for 1,8-cineole and atherosclerosis. In the protein-protein interaction studies, the top 5 ranked proteins were PPARG, FXR, ABCA-1, ABCG1, and LXRΑ. KEGG pathway analysis and molecular docking showed that ABCA-1 and LXRΑ were correlated in atherosclerosis. Animal studies showed amelioration of atherosclerotic lesions in the aorta of animals treated with 1,8-cineole compared to disease control aortas. A dose-dependent attenuation in ABCA-1 levels and inflammatory markers was observed in animals treated with 1,8-cineole, comparable to its levels in normal animals. In conclusion, 1,8-cineole showed anti-atherosclerotic effects in Golden Syrian hamsters via LXRΑ-induced ABCA-1 overexpression.

Capsaicin is a natural product with multiple biological activities, such as anti-inflammatory, analgesic, weight loss, anti-cancer and cardiovascular disease prevention. However, its further applications have been limited by its strong irritation, poor water solubility, and unsatisfied pharmacological effects. To ameliorate the problem, a series of derivatives of capsaicin and its analogues were designed and synthesized. Three candidate compounds (HJ-1-3, HJ-1-4, HJ-1-6) have shown the potential to reduce body fat accumulation and lose weight on different indicators with biological evaluation in vitro and in vivo.

The Hippo signalling pathway is prominent and governs cell proliferation and stem cell activity, acting as a growth regulator and tumour suppressor. Defects in Hippo signalling and hyperactivation of its downstream effector's Yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ) play roles in cancer development, implying that pharmacological inhibition of YAP and TAZ activity could be an effective cancer treatment strategy. Conversely, YAP and TAZ can also have beneficial effects in promoting tissue repair and regeneration following damage, therefore their activation may be therapeutically effective in certain instances. Recently, a complex network of intracellular and extracellular signalling mechanisms that affect YAP and TAZ activity has been uncovered. The YAP/TAZ-TEAD interaction leads to tumour development and the protein structure of YAP/TAZ-TEAD includes three interfaces and one hydrophobic pocket. There are clinical and preclinical trial drugs available to inhibit the hippo signalling pathway, but these drugs have moderate to severe side effects, so researchers are in search of novel, potent, and selective hippo signalling pathway inhibitors. In this review, we have discussed the hippo pathway in detail, including its structure, activation, and role in cancer. We have also provided the various inhibitors under clinical and preclinical trials, and advancement of small molecules their detailed docking analysis, structure-activity relationship, and biological activity. We anticipate that the current study will be a helpful resource for researchers.

Proteolysis Targeting Chimeras are part of targeted protein degradation (TPD) techniques, which are significant for pharmacological and therapy development. Small-molecule interaction with the targeted protein is a complicated endeavor and a challenge to predict the proteins accurately. This study used machine learning algorithms and molecular fingerprinting techniques to build an AI-powered PROTAC Activity Prediction Tool that could predict PROTAC activity by examining chemical structures. The chemical structures of a diverse set of PROTAC drugs and their corresponding activities are selected as a dataset for training the tool. The processes used in this study included data preparation, feature extraction, and model training. Further, evaluation was done for the performance of the various classifiers, such as AdaBoost, Support Vector Machine, Random Forest, Gradient Boosting, and Multi-Layer Perceptron. The findings show that the methods selected here depict accurate PROTAC activities. All the models in this study showed an ROC curve better than 0.9, while the random forest on the test set of the AI-DPAPT had an area under the curve score of 0.97, thus showing accurate results. Furthermore, the study revealed significant insights into the molecular features that can influence the functions of the PROTAC. These findings can potentially increase the understanding of the structure-activity correlations involved in the TPD. Overall, the investigation contributes to computational drug development by introducing this platform powered by artificial intelligence that predicts the function of PROTAC. In addition, it sped up the processes of identifying and improving previously unknown medications. The AI-DPAPT platform can be accessed online using a web server at https://ai-protac.streamlit.app/ .

This study investigates the molecular targets and pathways affected by valencene in non-small cell lung cancer (NSCLC) through network pharmacology and in vitro assays. Valencene's chemical structure was sourced from PubChem, and target identification utilized the PharmMapper database, cross-referenced with UniProtKB for official gene symbols. NSCLC-associated targets were identified via GeneCards, followed by protein-protein interaction analysis using STRING. Molecular docking studies employed AutoDock Vina to assess binding interactions with key nuclear receptors (RXRA, RXRB, RARA, RARB, THRB). Molecular dynamics simulations were conducted in GROMACS over 200 ns, while ADME/T properties were evaluated using Protox. In vitro assays measured cell viability in A549 and HEL 299 cells via MTT assays, assessed apoptosis through Hoechst staining, and evaluated mitochondrial potential with JC-1. Molecular docking revealed strong binding affinities of valencene (below - 5 kcal/mol) to nuclear receptors, outperforming 5-fluorouracil (5-FU). Molecular dynamics simulations indicated robust structural stability of the THRB-valencene complex, with favorable interaction energies. Notably, valencene exhibited a selectivity index of 2.293, higher than 5-FU's 2.231, suggesting enhanced safety for normal cells (HEL 299). Fluorescence microscopy confirmed dose-dependent DNA fragmentation and decreased mitochondrial membrane potential. These findings underscore valencene's potential as an effective therapeutic agent for lung cancer, demonstrating an IC₅₀ of 16.71 μg/ml in A549 cells compared to 5-FU's 12.7 μg/ml, warranting further investigation in preclinical models and eventual clinical trials.

In this work, we designed novel peptide conjugates with plant-based iridoid and lichen-derived depside derivatives to target the wild-type EGFR (WT) and its mutants, L858R and T790M/L858R/C797S triple mutant. These mutations are often expressed in multiple cancers, particularly lung cancer. Specifically, the iridoids included 7-deoxyloganetic acid (7-DGA) and loganic acid (LG), while the depside derivative was sekikaic acid (SK). These compounds are known for their innate anticancer properties and were conjugated with two separate peptide sequences KLPGWSG (K) and YSIPKSS (Y). These sequences have been shown to target EGFR in previous phage display library screening, although the mechanism is unknown. Thus, we created the di-conjugates for dual targeting and investigated their interactions of the di-conjugates and that of the neat peptides with the kinase domain of EGFR (WT) and the two mutants using molecular docking, molecular dynamics (MD) simulations, and MM-GBSA analysis. Docking studies revealed that the (7-DGA)₂-K showed the highest binding affinity at - 9.3 kcal/mol with the L858R mutant, while (LG)₂-Y displayed the highest binding affinity at - 9.0 kcal/mol for the triple mutant receptor. Our results indicated that several of the conjugates interacted with crucial residues of the kinase domain, including ASP855 and THR854 (activation loop), MET793 and PRO794 (hinge region), ARG841 (catalytic loop), and LYS728 and LEU718 of the glycine-rich P-loop. Interestingly, strong hydrophobic interactions were also observed with the C-terminal tail residues, such as PHE997 and ALA1000 as well as with ARG999 for the YSIPKSS peptide and most of the conjugates. The hydroxyl group of the cyclopentane ring and the oxygen of the pyran ring of the (7-DGA)₂-peptide conjugates contributed to binding particularly in the hinge region, while the peptide components formed an extended structure that bound well into the C-lobe. The (SK)₂-Y di-conjugate and KLPGWSG peptide formed hydrogen bonds with the SER797 residue of the triple mutant. Overall, our results show that the (7-DGA)₂-K, di-conjugate, the (7-DGA)₂-Y di-conjugate, and the neat YSIPKSS demonstrated strong and stable binding with the L858R mutant and the highly resistant triple mutant EGFR, respectively. The novel designed conjugates demonstrate potential for further optimization for laboratory studies aimed at developing new therapeutics for targeting specific EGFR mutant expressing cells.

Umami, a fundamental human taste modality, refers to the savory flavors in meats and broths, often associated with monosodium glutamate and protein richness. With limited knowledge of umami molecules, the food industry seeks efficient approaches for identifying novel tastants. In this study, we have devised a virtual screening pipeline for identifying highly potent umami tastants from large molecular databases. We curated the most extensive classification dataset containing 439 umami and 428 non-umami molecules and trained a transformer-based architecture to differentiate between the two classes, achieving 93% accuracy. Additionally, we built a neural network model for predicting the potency of umami compounds, the first effort of its kind. The classification and potency prediction models were combined with similarity analysis and toxicity screening to build an end-to-end virtual framework for the rational discovery of novel tastants. We applied this framework to the FooDB database containing around 70,000 molecules as an illustrative use case for screening potent umami compounds. The screened molecules were validated using molecular docking with the umami taste receptor. This study demonstrates the potential of data-driven methods in discovering new tastants from structural and chemical features of molecules and proposes an efficient implementation for industrial applications.

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.

This study aims to investigate the anti-inflammatory effects of Resveratrol (RES) in the treatment of Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS) by integrating network pharmacology, molecular docking, and experimental validation. Potential targets of RES were identified using DrugBank and SwissTargetPrediction, while IC/BPS-related targets were obtained from DisGeNET and Genecards. Molecular docking was performed using UCSF Chimera and SwissDock to validate the binding affinity of RES to key targets. Experimental validation involved treating TNF-α induced urothelial cells with RES, followed by assessments using RT-qPCR, ELISA, and Western blotting. A total of 86 drug targets and 211 disease targets were analyzed, leading to the identification of 8 key therapeutic targets for RES in IC/BPS treatment. Molecular docking revealed a strong affinity of RES for ESR2, with notable interactions also observed with SHBG, PTGS2, PPARG, KIT, PI3KCA, and AKT1. In vitro experiments confirmed that RES significantly alleviated the inflammatory response in TNF-α-induced urothelial cells, normalizing the expression levels of ESR2, SHBG, PPARG, and AKT1. RES can modulate critical pathways involving ESR2, SHBG, PPARG, and AKT1, highlighting its potential as a therapeutic agent for IC/BPS. This study provides a theoretical foundation for the clinical application of RES in treating IC/BPS.