Molecular Diversity最新文献

Cyclin-dependent kinases (CDKs), play essential roles in cell cycle progression. CDK activity is controlled through phosphorylation and inhibition by CDK inhibitors, such as p16. Mutations in p16 can lead to diseases such as cancer. This study examines a series of p16 mutants and their molecular interactions with CDK4 using modelling, molecular dynamics simulations, and docking studies. Despite no significant structural changes in p16 due to mutation, the binding affinity was found to be affected, correlating with conservation scales. Simulations revealed that specific mutations, such as G23D, P114S, and A60V resulted in loss of binding to CDK4, while others like R24Q and G67R showed partial loss. Surface electrostatics emphasised the significance of a positive patch on the binding surface of p16 that faces the CDK4 which was directly impacted due to mutations. Additionally, the partial binding mutants were found to have a lower stability compare to the Wildtype p16/CDK4 complex through the free energy landscape calculations. These findings provide useful insights into the molecular mechanisms by which p16 mutations influence CDK4 binding, potentially informing therapeutic strategies.

This study attempted to explore the molecular mechanism of Epimedium herb (EH) on rheumatoid arthritis (RA) treatment. We employed network pharmacology, molecular docking, and HPLC analysis to investigate the molecular mechanisms underlying the efficacy of EH in treating RA. To assess the efficacy of EH intervention, RA fibroblast-like synoviocytes (RA-FLS) and collagen-induced arthritis (CIA) mouse models were utilized. Ultimately, the active compounds icariin, luteolin, quercetin, and kaempferol were identified, with interleukin-1β (IL-1β), IL-6, tumor necrosis factor-alpha (TNF-α), and matrix metalloproteinase-9 (MMP-9) emerging as key targets of EH for RA. These targets were found to be downregulated in both in vitro and in vivo experiments following EH intervention. Furthermore, EH treatment induced apoptosis, reduced metastasis and invasion in RA-FLS, and ameliorated arthritis-related symptoms while regulating Th17 and Treg cells in CIA mice.

The p53 protein is regarded as the "Guardian of the Genome," but its mutation is tumor progression and present in more than half of malignant tumors. The pro-metastatic property of mutant p53 makes a strong argument for targeting mutant p53 with new therapeutic strategies. However, mutant p53 was considered as a challenging target for drug discovery due to the lack of small molecular binding pockets. Among them, mutant p53 Y220C creates a narrow crevice since the side chains dynamics on protein surface, which is suitable for designing small molecules to occupy the cavity and recovery the tumor suppressing function. Here, we describe the mechanism of p53 related signal pathway and how p53 Y220C regulate the tumorigenesis. We review the two types of p53 Y220C modulators including restoring the conformation of mutant p53 Y220C protein to wild-type p53 protein and recruiting histone acetyltransferase p300/CBP to acetylate p53 Y220C thus enables p53 Y220C dependent upregulation of apoptotic genes and downregulation of DNA damage response pathways. We also report clinical advances and challenges of these molecules in p53 Y220C medicated tumor therapy.

Identifying drug-target binding affinity (DTA) plays a critical role in early-stage drug discovery. Despite the availability of various existing methods, there are still two limitations. Firstly, sequence-based methods often extract features from fixed length protein sequences, requiring truncation or padding, which can result in information loss or the introduction of unwanted noise. Secondly, structure-based methods prioritize extracting topological information but struggle to effectively capture sequence features. To address these challenges, we propose a novel deep learning model named GraphkmerDTA, which integrates Kmer features with structural topology. Specifically, GraphkmerDTA utilizes graph neural networks to extract topological features from both molecules and proteins, while fully connected networks learn local sequence patterns from the Kmer features of proteins. Experimental results indicate that GraphkmerDTA outperforms existing methods on benchmark datasets. Furthermore, a case study on lung cancer demonstrates the effectiveness of GraphkmerDTA, as it successfully identifies seven known EGFR inhibitors from a screening library of over two thousand compounds. To further assess the practical utility of GraphkmerDTA, we integrated it with network pharmacology to investigate the mechanisms underlying the therapeutic effects of Lonicera japonica flower in treating Alzheimer's disease. Through this interdisciplinary approach, three potential compounds were identified and subsequently validated through molecular docking studies. In conclusion, we present not only a novel AI model for the DTA task but also demonstrate its practical application in drug discovery by integrating modern AI approaches with traditional drug discovery methodologies.

The ATP-binding cassette transporter superfamily plays a pivotal role in cellular detoxification and drug efflux. ATP-binding cassette subfamily G member 2 (ABCG2) referred to as the Breast cancer resistance protein has emerged as a key member involved in multidrug resistance displayed by cancer cells. Understanding the molecular basis of substrate and inhibitor recognition, and binding within the transmembrane domain of ABCG2 is crucial for the development of effective therapeutic strategies. Herein, utilizing state-of-the-art molecular docking algorithms and molecular dynamic (MD) simulations, molecular binding of substrates and inhibitors with ABCG2 are defined, distinctly. We performed extensive virtual screening of Drugbank to identify the potential candidates, and MD simulations of docked complexes were carried out in POPC lipid bilayer. Further, the binding affinities of compounds were estimated by free binding energy employing MM-GBSA. To gain deeper insight into the binding affinities and molecular characteristics contributing to inhibitory potential of certain substrates, we included some well-known inhibitors, like Imatinib, Tariquidar, and Ko 143, in our analysis. Docking results show three compounds, Docetaxel > Tariquidar > Tezacaftor having the highest binding affinities (≤ 12.00 kcal/mol) for ABCG2. Remarkably, MM-GBSA results suggest the most stable binding of Tariquidar with ABCG2 as compared to the other inhibitors. Furthermore, our results suggested that Docetaxel, Ozanimod, Pitavastatin, and Tezacaftor have the strongest affinity for the drug-binding site(s) of ABCG2. These results provide valuable insights into the key residues that may govern substrate/inhibitor recognition, shedding light on the molecular determinants influencing substrate specificity, transport kinetics, and ABCG2-mediated drug efflux.

SH2 (Src Homology 2) domains play a crucial role in phosphotyrosine-mediated signaling and have emerged as promising drug targets, particularly in cancer therapy. STAT3 (Signal Transducer and Activator of Transcription 3), which contains an SH2 domain, plays a pivotal role in cancer progression and immune evasion because it facilitates the dimerization of STAT3, which is essential for their activation and subsequent nuclear translocation. SH2 domain-mediated STAT3 inhibition disrupts this binding, reduces phosphorylation of STAT3, and impairs dimerization. This study employed an in silico approach to screen potential natural compounds that could target the SH2 domain of STAT3 and inhibit its function. The phytomolecules (182455) were retrieved from the ZINC 15 database and were docked using various modes like HTVS, SP, and XP. The phytomolecules exhibiting higher binding affinity were selected. MM-GBSA was performed to determine binding free energy, and the QikProp tool was utilized to assess the pharmacokinetic properties of potential hit compounds, narrowing down the list of candidates. Molecular dynamics simulations, thermal MM-GBSA, and WaterMap analysis were performed on compounds that exhibited favorable binding affinities and pharmacokinetic characteristics. Based on docking scores and binding interactions, ZINC255200449, ZINC299817570, ZINC31167114, and ZINC67910988 were identified as potential STAT3 inhibitors. ZINC67910988 demonstrated superior stability in molecular dynamics simulation and WaterMap analysis. Furthermore, DFT was performed to determine energetic and electronic properties, and HOMO and LUMO sites were predicted for electronic structure calculation. Additionally, network pharmacology was performed to map the compounds' interactions within biological networks, highlighting their multitarget potential. Compound-target networks elucidate the relationships between compounds and multiple targets, along with their associated pathways and help to minimize off-target effects. The identified lead compound showed strong potential as a STAT3 inhibitor, warranting further validation through in vitro and in vivo studies.

Chronic lymphocytic leukemia (CLL) is a malignancy caused by the overexpression of the anti-apoptotic protein B-cell lymphoma-2 (BCL-2), making it a critical therapeutic target. This study integrates computational screening, molecular docking, and molecular dynamics to identify and validate novel BCL-2 inhibitors from the ChEMBL database. Starting with 836 BCL-2 inhibitors, we performed ADME and Lipinski's Rule of Five (RO5) filtering, clustering, maximum common substructure (MCS) analysis, and machine learning models (Random Forest, SVM, and ANN), yielding a refined set of 124 compounds. Among these, 13 compounds within the most common substructure (MCS1) cluster showed promising features and were prioritized. A docking-based re-evaluation highlighted four lead compounds-ChEMBL464268, ChEMBL480009, ChEMBL464440, and ChEMBL518858-exhibiting notable binding affinities. Although a reference molecule outperformed in docking, molecular dynamics (MD), and binding energy analyses, it failed ADME and Lipinski criteria, unlike the selected leads. Further validation through MD simulations and MM/GBSA energy calculations confirmed stable binding interactions for the leads, with ChEMBL464268 showing the highest stability and binding affinity (ΔGtotal = - 80.35 ± 11.51 kcal/mol). Free energy landscape (FEL) analysis revealed stable energy minima for these complexes, underscoring conformational stability. Despite moderate activity (pIC₅₀ values from 4.3 to 5.82), the favorable pharmacokinetic profiles of these compounds position them as promising BCL-2 inhibitor leads, with ChEMBL464268 emerging as the most promising candidate for further CLL therapeutic development.

A series of novel isatin-oxime ether derivatives were designed, synthesized and characterized by ¹H NMR and ¹³C NMR and HRMS. These compounds were evaluated for their in vitro cytotoxicity against three human cancer cell lines (A549, HepG2 and Hela) by MTT assay. According to the experimental results, compounds 6a (IC₅₀ = 0.34μM), 6c (IC₅₀ = 14nM) and 6r (IC₅₀ = 45nM) were found as the excellent selectivity and high activity against A549, whereas compounds 6m (IC₅₀ = 12nM) and 6n (IC₅₀ = 25nM) displayed the significant activity for HepG2, respectively. Compound 6f (IC₅₀ = 30nM), 6n (IC₅₀ = 9nM) and 6o (IC₅₀ = 20nM) also showed the excellent activity against Hela. From the experiments of cell migration and colony formation assays, the findings demonstrated that 6m can effectively suppress the migration and growth of HepG2 cells. In addition, the results of molecular docking studies determined the strong binding interactions between the potential active compounds 6m and 6n and the active sites of isocitrate dehydrogenase 1 (IDH1) with the lowest binding affinity energy.

Src homology-2 domain-containing protein tyrosine phosphatase 1 (SHP-1) is a member of protein tyrosine phosphatase (PTP) family, and serves as a crucial negative regulator of various oncogenic signaling pathways. The development of SHP-1 agonists has garnered extensive research attention and is considered as a promising strategy for treating tumors. In this review, we comprehensively analyze the advancements of SHP-1 agonists, focusing on their structures and biological activities. Based on the structure skeletons, we classify these SHP-1 agonists as kinase inhibitors, sorafenib derivatives, obatoclax derivatives, lithocholic acid derivatives and thieno[2,3-b]quinoline derivatives. Additionally, we discuss the potential opportunities and challenges for developing SHP-1 agonists. It is hoped that this review will provide inspiring insights into the discovery of drugs targeting SHP-1.

Overexpressed AXL kinase is involved in various human malignancies, which incurs tumor progression, poor prognosis, and drug resistance. Suppression of the aberrant AXL axis with genetic tools or small-molecule inhibitors has achieved valid antitumor efficacies in both preclinical studies and clinical antitumor campaigns. Herein we will report the design, synthesis, and structure-activity relationship (SAR) exploration of a series of anilinopyrimidine type II AXL inhibitors. Among these inhibitors, 4l exhibited the enzymatic AXL and cellular BaF3/TEL-AXL IC₅₀ values of 0.5 nM and less than 0.2 nM, respectively. Western blot analysis displayed that 4l dose-dependently inhibited the phosphorylation of AXL and its downstream cascade Akt, which was better than that of the reference control R428. Moreover, 4l markedly suppressed the AXL/GAS6-mediated migration in NCI-H1299 cells.