Molecular Diversity最新文献

Gestational diabetes mellitus (GDM) is characterized by glucose intolerance during pregnancy, and emerging evidence implicates dysregulated iron metabolism as a critical modulator of its pathogenesis. Ferroptosis, an iron-mediated cell death, has recently been studied in GDM, with research beginning to unravel the connection between iron-induced oxidative stress and placental dysfunction. In this study, we employed datasets from the Gene Expression Omnibus database to identify markers of ferroptosis that are associated with GDM. A total of 57 differentially expressed genes related to ferroptosis were identified. Feature selection was performed using machine learning approaches, including Boruta, Random Forest, and LASSO regression, to pinpoint the most critical genes. Among them, GPX3 emerged as the central biomarker linked to ferroptosis in GDM. We further validated GPX3 expression across various placental cell types using single cell RNA sequencing data. Further CIBERSORT analysis determined a significant association between GPX3 and several immune cell populations, including macrophages, B cells, monocytes, and T cells. Finally, mRNA expression of GPX3 was experimentally validated in placental samples from GDM patients, where it was found to correlate with a reduced sTFR/ferritin ratio, suggesting disrupted iron homeostasis. In conclusion, GPX3 is identified as a crucial immuno-ferroptotic biomarker in GDM, with potential diagnostic value. Integrating bioinformatics, machine learning, and clinical validation, this study highlights the role of GPX3 at the intersection of immune infiltration and iron metabolism, offering new insights for future diagnostic and therapeutic strategies in GDM.

Carbazole and triazole derivatives exhibit diverse biological activities and pharmacological properties. Herein, we report a series of novel 1,2,4-triazole thioethers containing carbazole moiety and evaluate their biological activities. The results showed that some of the title compounds exhibited excellent antibacterial activities in vitro against Xanthomonas axonopodis pv. citri (Xac) in vitro. In particular, compound E36 exhibits the most excellent antibacterial effect against Xac, with an EC₅₀ value of 9.4 mg/L. This efficacy was significantly superior to those of the control drugs bismerthiazol (BMT, EC₅₀ values of 70.5 mg/L) and thiodiazole-copper (TDC, EC₅₀ values of 96.0 mg/L). Meanwhile, E36 also demonstrated a significant in vivo effect against Xac, with the therapeutic and protective efficacy of 48.57% and 51.96%, respectively, at a concentration of 200 mg/L, which was superior to TDC and equivalent to BMT. Additionally, E36 exhibited notable antifungal activity against Verticillium dahliae. Further mechanistic studies revealed that compound E36 attenuates the pathogenicity of Xac by suppressing bacterial motility and reducing extracellular polysaccharide (EPS) production. Concurrently, it enhances host disease resistance by upregulating the expression of the citrus rbcL protein, thereby promoting carbon fixation and improving photosynthetic efficiency. This work indicates that 1,2,4-triazole thioethers containing carbazole moiety has the potential to be developed as novel bactericidal agents.

Dengue, Zika, and West Nile viruses are major global health threats that belong to the genus Flavivirus and demand urgent attention. The viral proteases, particularly the viral protease complex (NS2B-NS3; NS2B: A small cofactor protein that activates NS3, NS3: A large multifunctional protein) complex, play a vital role in viral replication, making them prime targets for antiviral drug development. This review article has included the synthetic approach and Structure Activities Relationship (SAR) of such compounds, emphasizing how structural modifications in N-heterocyclic analogs affect inhibitory effectiveness toward proteases. Synthetic approaches such as click chemistry, cyclization, and bioisosteric replacements have been reviewed in order to enhance the selectivity and bioavailability of such molecules. Furthermore, computational modeling and molecular docking studies have been emphasized that support the rational drug design of reported molecules by predicting key binding interactions and optimizing pharmacokinetic parameters. In summary, this article underscores the importance of N-heterocyclic structures to develop viral protease inhibitors and provides direction for future antiviral drug development efforts. This review also highlights the potential of N-containing heterocycles as promising scaffolds for protease inhibition with an emphasis on their synthetic accessibility and capacity to engage in strong interactions within viral active sites. The present review also focuses on a future for the synthesis of nitrogenous heterocyclic analogs with a greater leadership of in silico approaches, including computational docking, fragment-based screening, and high-throughput synthesis techniques. Recent advances demonstrate that structural optimization of these heterocycles has led to compounds with encouraging antiviral activity, i.e., supported by computational insights. Looking forward, integrating in silico approaches with innovative synthetic methodologies is expected to accelerate development of selective and potent flaviviral protease inhibitors. Together, these efforts may pave the way for effective treatments against emerging flavivirus infections.

Malonylation modification of proteins is closely related to many diseases, such as diabetes and cancer. Therefore, accurate identification of malonylation modification sites is crucial for elucidating the molecular mechanisms underlying these diseases. Traditional experimental methods suffer from the problems of high cost, long cycle time, difficulty, etc. With advancements in artificial intelligence, the prediction of protein post-translational modification sites through computational methods has emerged as a vital complement to experimental approaches. In this paper, we present a malonylation site prediction model, Catsoft_Kmalsite, the core innovation of which lies in its integration of complementary information from protein three-dimensional structural features and sequence/physicochemical features, coupled with a soft voting ensemble strategy based on Bayesian-optimized base classifiers. Specifically, we utilize AlphaFold2 to acquire protein tertiary structural information and employ CTDC, EAAC, and EGAAC methods to extract protein sequence and physicochemical features. Subsequently, two base classifiers are constructed using the CatBoost algorithm based on these two distinct feature sets, respectively. Following parameter fine-tuning of the base classifiers via Bayesian optimization, they are ultimately integrated using a soft voting strategy. All ablation experimental results show that the Catsoft_Kmalsite model exhibited good robustness and generalization ability. Across six metrics, including AUC, ACC, Sen, Pre, F1, and MCC, the model achieved average performances of 94.03%, 87.91%, 89.15%, 86.91%, 88.00%, and 0.7585, respectively, in fivefold cross-validation and specific performance of 95.18%, 89.55%, 90.87%, 88.79%, 89.82%, and 0.7912 on the independent test set; Catsoft_Kmalsite also outperformed other state-of-the-art studies in all evaluated metrics. Furthermore, we have developed a website for users to use ( http://1.94.102.146:8501/Catsoft_Kmalsite ). The code and dataset of Catsoft_Kmalsite are available at https://github.com/flyinsky6/Catsoft_Kmalsite .

Our study presents DeepMice, a novel artificial intelligence-based molecular docking framework designed to predict protein-ligand binding conformations with improved accuracy. DeepMice's scoring function utilizes a graph transformer network (GTN) as its backbone. It transforms residue-level representations into atomic-level representations, enhancing representation precision. A multilevel mapping module is incorporated to reduce the graph model's size and computational complexity. Subsequently, the mixture density network (MDN) is employed to further realize scoring prediction. In terms of conformational search, DeepMice employs a hybrid strategy combining global heuristic search and local gradient-based optimization. The process initiates with a global exploration using the Differential Evolution (DE) algorithm, followed by local refinement via the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. This combined approach enhances conformational search efficiency. Performance tests on the DEKOIS2.0 and DUD-E datasets showed that DeepMice outperformed existing virtual screening technologies such as Glide SP and RTMScore in terms of area under the receiver operating characteristic curve (AUROC), boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), and enrichment factor (EF) values. In particular, DeepMice demonstrates advanced molecular docking capabilities in the CASF-2016 standard test set. In addition, DeepMice considers the multiscale structure of proteins, optimizing the conformation scoring process and improving docking efficiency. In summary, DeepMice is an efficient and accurate molecular docking model, which is expected to accelerate the process of new drug research and development. The program based on the DeepMice model, which is now freely available at https://www.deepmice.com , provides a powerful tool for drug discovery.

α-Glucosidase has always been one essential target for clinical prevention and treatment of diabetes. To develop effective α-glucosidase inhibitors, twenty-seven thiadiazole based β-carboline derivatives (TC1-TC27) were designed and synthesized by pharmacophore hybridization strategy, and systematically evaluated their inhibitory activity and binding characteristics against α-glucosidase. All synthesized derivatives (TC1-TC27) displayed significant inhibitory activity against α-glucosidase, with TC16 emerging as the most potent compound (IC₅₀ = 2.62 ± 0.21 μM), far surpassing the reference inhibitor acarbose (IC₅₀ = 210.75 ± 9.52 μM). Furthermore, fluorescence spectra and CD spectra results illustrated the binding of TC16 onto α-glucosidase, which caused the enzyme conformation transition to induce activity decrease. Finally, molecular docking elucidated hydrogen bonds and hydrophobic interactions kept the binding of TC16 onto α-glucosidase. In summary, this work provides a class of thiadiazole based β-carboline derivatives as potential α-glucosidase inhibitors.

Umami, recognized as the fifth basic taste, is primarily induced by specific amino acids and nucleotides, such as L-glutamate and inosinate, which interact with specialized taste receptors. Traditional foods like soy sauce, cheese, and fermented Asian products are rich in umami flavor. Despite extensive research into the biological mechanisms of umami perception, computational methods for predicting umami taste from molecular structures are underdeveloped due to the lack of dataset and inadequate feature representation from molecules. This study uses machine learning to introduce a computational model for classifying peptides and small molecules as umami or non-umami, addressing the gaps through comprehensive feature extraction and model integration. We curated a balanced dataset of 868 compounds (439 umami and 429 non-umami), and extracted a rich set of molecular descriptors representing their physicochemical and structural properties. Ensemble models, including LightGBM, XGBoost, and ExtraTrees, demonstrated high predictive accuracy across different datasets. Notably, the random forest classifier achieved an accuracy of 92.13% on the peptide-only dataset, while linear discriminant analysis and ExtraTrees classifiers attained an accuracy of 98.84% on the small molecules dataset. On the combined dataset, LightGBM achieved the highest accuracy of 96.55%, highlighting the effectiveness of integrating peptide and small molecule data for umami prediction. A user-friendly web server, UmamiPredict ( https://cosylab.iiitd.edu.in/umami/ ), facilitates users in predicting the umami taste of molecules and peptides with SMILES representations of molecules or peptides as input.

Matrix metalloproteinase-13 (MMP-13) is a zinc-dependent endopeptidase involved in extracellular matrix degradation and inflammation, contributing to the progression of various diseases. This study applied an integrated computational approach encompassing QSAR modeling, machine learning (ML), scaffold analysis, docking, and molecular dynamics (MD) simulations to investigate the structure-activity relationships and binding mechanisms of MMP-13 inhibitors. A curated dataset of 1,741 unique compounds from ChEMBL was used to develop predictive QSAR models based on PubChem fingerprints. Among eight regression models, LGBM, SVR, and RF exhibited superior predictive performance, with LGBM achieving the best generalization (test RMSE = 0.825, R² = 0.646, Q² = 0.628). Similarly, LGBM and SVM classifiers demonstrated high accuracy (0.802) and MCC (0.589) with test data. Docking analysis identified three top candidates (ChEMBL1770157, ChEMBL425020 and ChEMBL5182668) with strong binding affinities of -10.98, -10.93 and -10.80 kcal/mol, respectively. The identified interaction hotspots, particularly Thr245, Ala186, Leu185, Val219, and the highly versatile His222, represent key residues to target for enhancing binding affinity. Subsequent 200 ns MD simulations confirmed their structural stability and favorable binding dynamics within the MMP-13 active site. Scaffold analysis revealed the predominance of sulfonamide and carboxyl-containing polar functional groups, known to be important for solubility and target binding. The findings underscore the importance of physicochemical and structural attributes in MMP-13 inhibitor design and support the therapeutic potential of targeting MMP-13 in diverse pathological contexts.

Epoxides are significant heterocycles in the structural makeup of a variety of natural products. Their ring-opening reactions have emerged as a versatile and efficient strategies for synthesizing a variety of functionalized molecules. Such reactions have been extensively applied towards the preparation of complex naturally occurring products. The focus on epoxide ring-opening reactions within the scientific community has been increased, influenced by the goal to understand the synthesis of compounds that are important for their biological and structural significance. In this article, we have provided a concise account on the applications of epoxide's ring cleavage towards the syntheses of polyketides and related naturally occurring compounds, documented since last decade (2014-2023).

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.