ABSTRACT
Inflammation is a protective stress response triggered by external stimuli, with 5-lipoxygenase (5LOX) playing a pivotal role as a potent mediator of the leukotriene (Lts) inflammatory pathway. Nordihydroguaiaretic acid (NDGA) functions as a natural orthosteric inhibitor of 5LOX, while 3-acetyl-11-keto-ß-boswellic acid (AKBA) acts as a natural allosteric inhibitor targeting 5LOX. However, the precise mechanisms of inhibition have remained unclear. In this study, Gaussian accelerated molecular dynamics (GaMD) simulation was employed to elucidate the inhibitory mechanisms of NDGA and AKBA on 5LOX. It was found that the orthosteric inhibitor NDGA was tightly bound in the protein's active pocket, occupying the active site and inhibiting the catalytic activity of the 5LOX enzyme through competitive inhibition. The binding of the allosteric inhibitor AKBA induced significant changes at the distal active site, leading to a conformational shift of residues 168-173 from a loop to an α-helix and significant negative correlated motions between residues 285-290 and 375-400, reducing the distance between these segments. In the simulation, the volume of the active cavity in the stable conformation of the protein was reduced, hindering the substrate's entry into the active cavity and, thereby, inhibiting protein activity through allosteric effects. Ultimately, Markov state models (MSM) were used to identify and classify the metastable states of proteins, revealing the transition times between different conformational states. In summary, this study provides theoretical insights into the inhibition mechanisms of 5LOX by AKBA and NDGA, offering new perspectives for the development of novel inhibitors specifically targeting 5LOX, with potential implications for anti-inflammatory drug development.
Subject(s)
Arachidonate 5-Lipoxygenase , Lipoxygenase Inhibitors , Markov Chains , Molecular Dynamics Simulation , Arachidonate 5-Lipoxygenase/metabolism , Arachidonate 5-Lipoxygenase/chemistry , Lipoxygenase Inhibitors/pharmacology , Lipoxygenase Inhibitors/chemistry , Humans , Catalytic Domain , Protein Binding , Masoprocol/pharmacology , Masoprocol/chemistry , Protein ConformationABSTRACT
Reproductive toxicity poses significant risks to fertility and progeny health, making its identification in pharmaceutical compounds crucial. In this study, we conducted a comprehensive in silico investigation of reproductive toxic molecules, identifying three distinct categories represented by Dimethylhydantoin, Phenol, and Dicyclohexyl phthalate. Our analysis included physicochemical properties, target prediction, and KEGG and GO pathway analyses, revealing diverse and complex mechanisms of toxicity. Given the complexity of these mechanisms, traditional molecule-target research approaches proved insufficient. Support Vector Machines (SVMs) combined with molecular descriptors achieved an accuracy of 0.85 in the test dataset, while our custom deep learning model, integrating molecular SMILES and graphs, achieved an accuracy of 0.88 in the test dataset. These models effectively predicted reproductive toxicity, highlighting the potential of computational methods in pharmaceutical safety evaluation. Our study provides a robust framework for utilizing computational methods to enhance the safety evaluation of potential pharmaceutical compounds.
Subject(s)
Reproduction , Support Vector Machine , Reproduction/drug effects , Humans , Computer Simulation , Computational Biology/methods , Cluster Analysis , Phthalic Acids/toxicity , AnimalsABSTRACT
Chronic atrophic gastritis (CAG), characterized by inflammation and erosion of the gastric lining, is a prevalent digestive disorder and considered a precursor to gastric cancer (GC). Coptis chinensis France (CCF) is renowned for its potent heat-clearing, detoxification, and anti-inflammatory properties. Zuojin Pill (ZJP), a classic Chinese medicine primarily composed of CCF, has demonstrated effectiveness in CAG treatment. This study aims to elucidate the potential mechanism of CCF treatment for CAG through a multifaceted approach encompassing network pharmacology, molecular docking, molecular dynamics simulation and experimental verification. The study identified three major active compounds of CCF and elucidated key pathways, such as TNF signaling, PI3K-Akt signaling and p53 signaling. Molecular docking revealed interactions between these active compounds and pivotal targets like PTGS2, TNF, MTOR, and TP53. Additionally, molecular dynamics simulation validated berberine as the primary active compound of CCF, which was further confirmed through experimental verification. This study not only identified berberine as the primary active compound of CCF but also provided valuable insights into the molecular mechanisms underlying CCF's efficacy in treating CAG. Furthermore, it offers a reference for refining therapeutic strategies for CAG management.
Subject(s)
Coptis , Drugs, Chinese Herbal , Gastritis, Atrophic , Molecular Dynamics Simulation , Network Pharmacology , Humans , Coptis/chemistry , Drugs, Chinese Herbal/chemistry , Drugs, Chinese Herbal/therapeutic use , Drugs, Chinese Herbal/pharmacology , Gastritis, Atrophic/drug therapy , Gastritis, Atrophic/metabolism , Molecular Docking Simulation , Signal Transduction/drug effects , Tumor Suppressor Protein p53/metabolism , Berberine/chemistry , Berberine/therapeutic use , Berberine/pharmacology , Tumor Necrosis Factor-alpha/metabolismABSTRACT
Peptides, with recognized physiological and medical implications, such as the ability to lower blood pressure and lipid levels, are central to our research on umami taste perception. This study introduces a computational strategy to tackle the challenge of identifying optimal umami receptors for these peptides. Our VmmScore algorithm includes two integral components: Mlp4Umami, a predictive module that evaluates the umami taste potential of peptides, and mm-Score, which enhances the receptor matching process through a machine learning-optimized molecular docking and scoring system. This system encompasses the optimization of docking structures, clustering of umami peptides, and a comparative analysis of docking energies across peptide clusters, streamlining the receptor identification process. Employing machine learning, our method offers a strategic approach to the intricate task of umami receptor determination. We undertook virtual screening of peptides derived from Lateolabrax japonicus, experimentally verifying the umami taste of three identified peptides and determining their corresponding receptors. This work not only advances our understanding of the mechanisms behind umami taste perception but also provides a rapid and cost-effective method for peptide screening. The source code is publicly accessible at https://github.com/heyigacu/mlp4umami/, encouraging further scientific exploration and collaborative efforts within the research community.
Subject(s)
Deep Learning , Peptides , Peptides/chemistry , Humans , Animals , Receptors, G-Protein-Coupled/chemistry , Receptors, G-Protein-Coupled/metabolism , Molecular Docking Simulation , Software , Taste Perception/physiology , Algorithms , Taste/physiologyABSTRACT
In this study, our focus was on investigating H-1,2,3-triazole derivative HP661 as a novel and highly efficient oral OXPHOS inhibitor, with its molecular-level inhibitory mechanism not yet fully understood. We selected the ND1, NDUFS2, and NDUFS7 subunits of Mitochondrial Complex I as the receptor proteins and established three systems for comparative analysis: protein-IACS-010759, protein-lead compound 10, and protein-HP661. Through extensive analysis involving 500 ns Gaussian molecular dynamics simulations, we gained insights into these systems. Additionally, we constructed a Markov State Models to examine changes in secondary structures during the motion processes. The research findings suggest that the inhibitor HP661 enhances the extensibility and hydrophilicity of the receptor protein. Furthermore, HP661 induces the unwinding of the α-helical structure in the region of residues 726-730. Notably, key roles were identified for Met37, Phe53, and Pro212 in the binding of various inhibitors. In conclusion, we delved into the potential molecular mechanisms of triazole derivative HP661 in inhibiting Complex I. These research outcomes provide crucial information for a deeper understanding of the mechanisms underlying OXPHOS inhibition, offering valuable theoretical support for drug development and disease treatment design.
Subject(s)
Electron Transport Complex I , Markov Chains , Molecular Dynamics Simulation , Electron Transport Complex I/antagonists & inhibitors , Electron Transport Complex I/chemistry , Electron Transport Complex I/metabolism , Humans , Triazoles/chemistry , Enzyme Inhibitors/chemistry , Enzyme Inhibitors/pharmacology , Administration, OralABSTRACT
Vitamin B12 is a group of biologically active cobalamin compounds. In this study, we investigated the inhibitory effects of methylcobalamin (MeCbl) and hydroxocobalamin acetate (OHCbl Acetate) on protein tyrosine phosphatase 1B (PTP1B). MeCbl and OHCbl Acetate exhibited an IC50 of approximately 58.390 ± 2.811 µM and 8.998 ± 0.587 µM, respectively. The Ki values of MeCbl and OHCbl Acetate were 25.01 µM and 4.04 µM respectively. To elucidate the inhibition mechanism, we conducted a 500 ns Gaussian accelerated molecular dynamics (GaMD) simulation. Utilizing PCA and tICA, we constructed Markov state models (MSM) to examine secondary structure changes during motion. Our findings revealed that the α-helix at residues 37-42 remained the most stable in the PTP1B-OHCbl Acetate system. Furthermore, upon binding of OHCbl Acetate or MeCbl, the WPD loop of PTP1B moved inward to the active pocket, forming a closed conformation and potentially obstructs substrate entry. Protein-ligand interaction analysis and MM-PBSA showed that OHCbl Acetate exhibited lower binding free energy and engaged in more residue interactions with PTP1B. In summary, our study confirmed the substantial inhibitory activity of OHCbl Acetate against PTP1B, with its inhibitory potency notably surpassing that of MeCbl. We demonstrated potential molecular mechanisms of OHCbl Acetate inhibiting PTP1B.
Subject(s)
Molecular Dynamics Simulation , Protein Tyrosine Phosphatase, Non-Receptor Type 1 , Vitamin B 12 , Protein Tyrosine Phosphatase, Non-Receptor Type 1/antagonists & inhibitors , Protein Tyrosine Phosphatase, Non-Receptor Type 1/chemistry , Protein Tyrosine Phosphatase, Non-Receptor Type 1/metabolism , Vitamin B 12/chemistry , Vitamin B 12/analogs & derivatives , Vitamin B 12/pharmacology , Enzyme Inhibitors/chemistry , Enzyme Inhibitors/pharmacology , Humans , Molecular Docking Simulation , Protein Binding , Kinetics , Structure-Activity RelationshipABSTRACT
Type 2 diabetes mellitus (T2DM) is marked by persistent hyperglycemia, insulin resistance, and pancreatic ß-cell dysfunction, imposing substantial health burdens and elevating the risk of systemic complications and cardiovascular diseases. While the pathogenesis of diabetes remains elusive, a cyclical relationship between insulin resistance and inflammation is acknowledged, wherein inflammation exacerbates insulin resistance, perpetuating a deleterious cycle. Consequently, anti-inflammatory interventions offer a therapeutic avenue for T2DM management. In this study, a herb called Baikal skullcap, renowned for its repertoire of bioactive compounds with anti-inflammatory potential, is posited as a promising source for novel T2DM therapeutic strategies. Our study probed the anti-diabetic properties of compounds from Baikal skullcap via network pharmacology, molecular docking, and cellular assays, concentrating on their dual modulatory effects on diabetes through Protein Tyrosine Phosphatase 1B (PTP1B) enzyme inhibition and anti-inflammatory actions. We identified the major compounds in Baikal skullcap using liquid chromatography-mass spectrometry (LC-MS), highlighting six flavonoids, including the well-studied baicalein, as potent inhibitors of PTP1B. Furthermore, cellular experiments revealed that baicalin and baicalein exhibited enhanced anti-inflammatory responses compared to the active constituents of licorice, a known anti-inflammatory agent in TCM. Our findings confirmed that baicalin and baicalein mitigate diabetes via two distinct pathways: PTP1B inhibition and anti-inflammatory effects. Additionally, we have identified six flavonoid molecules with substantial potential for drug development, thereby augmenting the T2DM pharmacotherapeutic arsenal and promoting the integration of herb-derived treatments into modern pharmacology.
Subject(s)
Diabetes Mellitus, Type 2 , Flavanones , Insulin Resistance , Scutellaria baicalensis , Diabetes Mellitus, Type 2/drug therapy , Liquid Chromatography-Mass Spectrometry , Chromatography, Liquid , Molecular Docking Simulation , Tandem Mass Spectrometry , Flavonoids/pharmacology , Inflammation , Anti-Inflammatory Agents/pharmacologyABSTRACT
Recently, studies have reported a correlation that individuals with diabetes show an increased risk of developing Alzheimer's disease (AD). Mulberry leaves, serving as both a traditional medicinal herb and a food source, exhibit significant hypoglycemic and antioxidative properties. The flavonoid compounds in mulberry leaf offer therapeutic effects for relieving diabetic symptoms and providing neuroprotection. However, the mechanisms of this effect have not been fully elucidated. This investigation aimed to investigate the combined effects of specific mulberry leaf flavonoids (kaempferol, quercetin, rhamnocitrin, tetramethoxyluteolin, and norartocarpetin) on both type 2 diabetes mellitus (T2DM) and AD. Additionally, the role of the gut microbiota in these two diseases' treatment was studied. Using network pharmacology, we investigated the potential mechanisms of flavonoids in mulberry leaves, combined with gut microbiota, in combating AD and T2DM. In addition, we identified protein tyrosine phosphatase 1B (PTP1B) as a key target for kaempferol in these two diseases. Molecular docking and molecular dynamics simulations showed that kaempferol has the potential to inhibit PTP1B for indirect treatment of AD, which was proven by measuring the IC50 of kaempferol (279.23 µM). The cell experiment also confirmed the dose-dependent effect of kaempferol on the phosphorylation of total cellular protein in HepG2 cells. This research supports the concept of food-medicine homology and broadens the range of medical treatments for diabetes and AD, highlighting the prospect of integrating traditional herbal remedies with modern medical research.
Subject(s)
Alzheimer Disease , Diabetes Mellitus, Type 2 , Gastrointestinal Microbiome , Morus , Humans , Diabetes Mellitus, Type 2/drug therapy , Kaempferols , Molecular Dynamics Simulation , Network Pharmacology , Alzheimer Disease/drug therapy , Molecular Docking Simulation , Fruit , FlavonoidsABSTRACT
Fresh green leaves give off a smell known as "green odor." It has antibacterial qualities and can be used to attract or repel insects. However, a common method for evaluating green odor molecules has never existed. Machine learning techniques are widely used in research to forecast molecular attributes for binary classification. In this work, the green odor molecules were first trained and learned using machine learning methods, and then clustering analysis and molecular docking were performed to further explore their molecular characteristics and mechanisms of action. For comparison, four algorithmic models were employed, MLP performed the best in all metrics, including Accuracy, Precision, Average Precision, Matthews coefficient, and Area under curve. We determined by difference analysis that, in comparison to non-green odor molecules, green odor molecules have a lower molecular mass and fewer electrons. Based on the MLP algorithm, we constructed a binary classification prediction website for green odors. The first application of deep learning techniques to the study of green odor molecules can be seen as a signal of a new era in which green odor research has advanced into intelligence and standardization.
Subject(s)
Odorants , Smell , Molecular Docking Simulation , Algorithms , Machine LearningABSTRACT
Gout, a painful condition marked by elevated uric acid levels often linked to the diet's high purine and alcohol content, finds a potential treatment target in xanthine oxidase (XO), a crucial enzyme for uric acid production. This study explores the therapeutic properties of alkaloids extracted from sunflower (Helianthus annuus L.) receptacles against gout. By leveraging computational chemistry and introducing a novel R-based clustering algorithm, "TriDimensional Hierarchical Fingerprint Clustering with Tanimoto Representative Selection (3DHFC-TRS)," we assessed 231 alkaloid molecules from sunflower receptacles. Our clustering analysis pinpointed six alkaloids with significant gout-targeting potential, particularly emphasizing the fifth cluster's XO inhibition capabilities. Through molecular docking and the BatchDTA prediction model, we identified three top compounds-2-naphthylalanine, medroxalol, and fenspiride-with the highest XO affinity. Further molecular dynamics simulations assessed their enzyme active site interactions and binding free energies, employing MM-PBSA calculations. This investigation not only highlights the discovery of promising compounds within sunflower receptacle alkaloids via LC-MS but also introduces medroxalol as a novel gout treatment candidate, showcasing the synergy of computational techniques and LC-MS in drug discovery.
Subject(s)
Ethanolamines , Gout , Helianthus , Helianthus/metabolism , Uric Acid/metabolism , Uric Acid/therapeutic use , Molecular Docking Simulation , Enzyme Inhibitors/pharmacology , Gout/drug therapy , Xanthine Oxidase/chemistry , Xanthine Oxidase/metabolismABSTRACT
Dipeptidyl peptidase 4 (DPP4) inhibitors can effectively inhibit the activity of DPP4, increasing the concentrations of glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), which allows for them to effectively contribute to the reduction of blood sugar levels. Leu-Pro-Ala-Val-Thr-Ile-Arg (LPAVTIR) and Leu-Pro-Pro-Glu-His-Asp-Trp-Arg (LPPEHDWR) were the two peptides with the strongest inhibitory activity against DPP4 selected from silkworm pupa proteins. In this study, four systems were established: Apo (ligand-free DPP4), IPI (IPI-bound DPP4), LPAVTIR (LPAVTIR-bound DPP4), LPPEHDWR (LPPEHDWR-bound DPP4), and Gaussian accelerated molecular dynamic (GaMD) simulation was conducted to investigate the mechanism of action of two inhibitory peptides binding to DPP4. Our study revealed that the LPAVTIR peptide possessed a more stable structure and exhibited a tighter binding to the Ser630 active site in DPP4, thus exhibiting a favorable competitive inhibition effect. In contrast, the LPPEHDWR peptide caused the horizontal α-helix (residues 201-215) composed of Glu205 and Glu206 residues in DPP4 to disappear. The spatial arrangement of active sites Ser630 relative to Glu205 and Glu206 was disrupted, resulting in enzyme inactivation. Moreover, the size of the substrate channel and cavity volume was significantly reduced after the binding of the inhibitory peptide to the protein, which was an important factor in the inhibition of the enzyme activity. A similar effect was also found from IPI (our positive control). By stabilizing the active site of DPP4, the IPI peptide induced the disappearance of the horizontal α-helix and a notable reduction in the active cavity volume. In conclusion, our study provided a solid theoretical foundation for the inhibitory mechanisms of IPI, LPAVTIR, and LPPEHDWR on DPP4, offering valuable insights for advancing the development of drug targets for type 2 diabetes.
Subject(s)
Diabetes Mellitus, Type 2 , Dipeptidyl-Peptidase IV Inhibitors , Humans , Dipeptidyl Peptidase 4 , Molecular Dynamics Simulation , Peptides/pharmacology , Dipeptidyl-Peptidase IV Inhibitors/pharmacologyABSTRACT
Xanthine oxidase (XO) is a crucial enzyme in the development of hyperuricemia and gout. This study focuses on LWM and ALPM, two food-derived inhibitors of XO. We used molecular docking to obtain three systems and then conducted 200 ns molecular dynamics simulations for the Apo, LWM, and ALPM systems. The results reveal a stronger binding affinity of the LWM peptide to XO, potentially due to increased hydrogen bond formation. Notable changes were observed in the XO tunnel upon inhibitor binding, particularly with LWM, which showed a thinner, longer, and more twisted configuration compared to ALPM. The study highlights the importance of residue F914 in the allosteric pathway. Methodologically, we utilized the perturbed response scan (PRS) based on Python, enhancing tools for MD analysis. These findings deepen our understanding of food-derived anti-XO inhibitors and could inform the development of food-based therapeutics for reducing uric acid levels with minimal side effects.
Subject(s)
Deep Learning , Hyperuricemia , Humans , Xanthine Oxidase , Structure-Activity Relationship , Molecular Docking Simulation , Molecular Dynamics Simulation , Enzyme Inhibitors/chemistry , Hyperuricemia/drug therapy , Peptides/pharmacology , Peptides/therapeutic useABSTRACT
Secreted proteins play critical roles in regulating immune responses, exerting cytotoxic effects on tumor cells, promoting inflammatory processes, and influencing cellular metabolism. Deciphering the intricate relationship between the heterogeneity of secreted proteins and their transcriptional states is pivotal in the study of cellular heterogeneity. Here we proposed a cell-antibody conjugate-based sequencing methodology (Cellab-seq) for joint characterization of secreted proteins and transcriptome. Cellab-seq utilizes a chemoenzymatic strategy to construct cell-antibody conjugates, which enables the capture of secreted proteins and their signal transduction with the incorporation of barcode detection antibodies. We applied Cellab-seq to investigate how gene expression influences the activity of secreted proteins in NK cells. Altogether, this strategy facilitates a nuanced understanding of cellular dynamics under diverse physiological conditions, ultimately contributing to the prevention, diagnosis and treatment of diseases.
Subject(s)
Killer Cells, Natural , TranscriptomeABSTRACT
Histone deacetylases (HDACs) have emerged as promising targets for anticancer drug development. They regulate gene expression by removing acetyl groups from lysine residues on histone tails, leading to chromatin condensation. A hydrazide-based HDAC inhibitor, N-(4-(2-Propylhydrazine-1-carbonyl)benzyl)-1H-indole-2-carboxamide (11h), has been reported to exhibit significant in vivo antitumor activity. In comparison to the lead compound N-(4-(2-Propylhydrazine-1-carbonyl)benzyl)cinnamamide (17), compound 11h demonstrates 2- to 5-fold higher HDAC inhibition and cell-based antitumor activity. However, the inhibitory mechanism of 11h remains insufficiently explored. In this study, we conducted 500 ns Gaussian Accelerated Molecular Dynamics (GaMD) simulations on Histone deacetylase 3 (HDAC3) and two complex systems (HDAC3-17 and HDAC3-11h). Our findings revealed that upon inhibitor binding, the active pocket volume of HDAC3 undergone alterations, and the movement of the L6-loop toward the active site impeded substrate entry. Moreover, we observed a destabilization of the α-helix in the aa75-89 region of HDAC3 compared to the two complex systems, indicating partial unwinding. Notably, 11h exhibited a closer proximity of its carbonyl oxygen to the active pocket's Zn2+ metal compared to 17, increasing the likelihood of coordination with the Zn2+ metal. The analysis of protein-ligand interactions highlighted a greater number of hydrogen bonds and other interactions between 11h and the receptor protein when compared to 17, underscoring the stronger binding of 11h to HDAC3. In conclusion, our study provided theoretical insights into the inhibitory mechanism of hydrazide-based HDAC inhibitors on HDAC3, thereby contributing to the development of improved drug targets for cancer therapy.Communicated by Ramaswamy H. Sarma.
ABSTRACT
There are reports indicating that licochalcones can inhibit the proliferation, migration, and invasion of cancer cells by promoting the expression of autophagy-related proteins, inhibiting the expression of cell cycle proteins and angiogenic factors, and regulating autophagy and apoptosis. This study aims to reveal the potential mechanisms of licochalcone A (LCA), licochalcone B (LCB), licochalcone C (LCC), licochalcone D (LCD), licochalcone E (LCE), licochalcone F (LCF), and licochalcone G (LCG) inhibition in liver cancer through computer-aided screening strategies. By using machine learning clustering analysis to search for other structurally similar components in licorice, quantitative calculations were conducted to collect the structural commonalities of these components related to liver cancer and to identify key residues involved in the interactions between small molecules and key target proteins. Our research results show that the seven licochalcones molecules interfere with the cancer signaling pathway via the NF-κB signaling pathway, PDL1 expression and PD1 checkpoint pathway in cancer, and others. Glypallichalcone, Echinatin, and 3,4,3',4'-Tetrahydroxy-2-methoxychalcone in licorice also have similar structures to the seven licochalcones, which may indicate their similar effects. We also identified the key residues (including ASN364, GLY365, TRP366, and TYR485) involved in the interactions between ten flavonoids and the key target protein (nitric oxide synthase 2). In summary, we provide valuable insights into the molecular mechanisms of the anticancer effects of licorice flavonoids, providing new ideas for the design of small molecules for liver cancer drugs.
Subject(s)
Chalcones , Liver Neoplasms , Humans , Network Pharmacology , Chalcones/pharmacology , Chalcones/chemistry , Flavonoids , NF-kappa B , Liver Neoplasms/drug therapyABSTRACT
The disease of SARS-CoV-2 has caused considerable morbidity and mortality globally. Spike proteins on the surface of SARS-CoV-2 allow it to bind with human cells, leading to infection. Fullerenes and their derivatives are promising SARS-CoV-2 inhibitors and drug-delivery vehicles. In this study, Gaussian accelerated molecular dynamics simulations and the Markov state model were employed to delve into the inhibitory mechanism of Fullerene-linear-polyglycerol-b-amine sulfate (F-LGPS) on spike proteins. During the study, it was discovered that fullerene derivatives can operate at the interface of the receptor-binding domain (RBD) and the N-terminal domain (NTD), keeping structural domains in a downward conformation. It was also observed that F-LGPS demonstrated superior inhibitory effects on the XBB variant in comparison to the wild-type variant. This study yielded invaluable insights for the potential development of efficient therapeutics targeting the spike protein of SARS-CoV-2.
Subject(s)
COVID-19 , Fullerenes , Humans , SARS-CoV-2 , Fullerenes/pharmacology , Spike Glycoprotein, Coronavirus , Molecular Dynamics Simulation , Protein BindingABSTRACT
To explore the anti-hyperuricemia components in sunflower (Helianthus annuus L.) calathide extract (SCE), we identified abietic acid (AA) via liquid chromatography-mass spectrometry and found an excellent inhibitor of xanthine oxidase (IC50 = 10.60 µM, Ki = 193.65 nM) without cytotoxicity. Based on the transcriptomics analysis of the human embryonic kidney 293T cell model established using 1 mM uric acid, we evaluated that AA showed opposite modulation of purine metabolism to the UA group and markedly suppressed the intensity of purine nucleoside phosphorylase, ribose phosphate pyrophosphokinase 2, and ribose 5-phosphate isomerase A. Molecular docking also reveals the inhibition of purine nucleoside phosphorylase and ribose phosphate pyrophosphokinase 1. The SCE exhibits similar regulation of these genes, so we conclude that AA was a promising component in SCE against hyperuricemia. This present study provided a novel cell model for screening anti-hyperuricemia natural drugs in vitro and illustrated that AA, a natural diterpenoid, is a potential inhibitor of purine biosynthesis or metabolism.
Subject(s)
Helianthus , Hyperuricemia , Humans , Helianthus/metabolism , Purine-Nucleoside Phosphorylase/metabolism , Molecular Docking Simulation , Ribose-Phosphate Pyrophosphokinase/metabolism , HEK293 Cells , Hyperuricemia/drug therapy , Hyperuricemia/metabolism , Kidney/metabolism , Purines/metabolism , Xanthine OxidaseABSTRACT
Intercellular proximity labeling has emerged as a promising approach to enable the study of cell-cell interactions (CCIs), but the efficiency of current platforms is limited. Here, we use Ru(bpy)3 2+ to construct an efficient photocatalytic proximity labeling (PPL) system on the cell surface that allows the highly discriminative CCI detection with spatiotemporal resolution. Through the mechanism study and quantitative characterization on living cells, we demonstrate that the singlet-oxygen (1 O2 ) mechanism is more efficient and specific than the single electron transfer (SET) mechanism in Ru-mediated PPL. Ru(bpy)3 2+ catalysts with different cell-anchoring moieties are prepared to facilitate the catalyst loading on primary cells. Finally, based on this system, we develop a "live" T cell receptor (TCR) multimer with TCR-T cells that could sensitively identify and discriminate cells presenting antigens of different affinity, providing a powerful tool to better understand the heterogeneity of antigen presenting cells.
ABSTRACT
FucoID is an intercellular proximity labeling technique for studying cell-cell interactions (CCIs) via fucosyltransferase (FT)-meditated fucosyl-biotinylation, which has been applied to probe antigen-specific dendritic cell (DC)-T cell interactions. In this system, bait cells of interest with cell surface-anchored FT are used to capture the interacting prey cells by transferring a biotin-modified substrate to prey cells. Here, we leveraged FucoID to study CCIs directed by different molecular pairs, e.g., programmed cell death protein-1(PD-1)/programmed cell death protein-ligand-1 (PD-L1), and identify unknown or little studied CCIs, e.g., the interaction of DCs and B cells. To expand the application of FucoID to complex systems, we also synthesized site-specific antibody-based FT conjugate, which substantially improves the ability of FucoID to probe molecular signatures of specific CCI when cells of interest (bait cells) cannot be purified, e.g., in clinical samples. Collectively, these studies demonstrate the general applicability of FucoID to study unknown CCIs in complex systems at a molecular resolution.
ABSTRACT
Anticancer peptide (ACP) is a short peptide with less than 50 amino acids that has been discovered in a variety of foods. It has been demonstrated that traditional Chinese medicine or food can help treat cancer in some cases, which suggests that ACP may be one of the therapeutic ingredients. Studies on the anti-cancer properties of Sanghuangporus sanghuang have concentrated on polysaccharides, flavonoids, triterpenoids, etc. The function of peptides has not received much attention. The purpose of this study is to use computer mining techniques to search for potential anticancer peptides from 62 proteins of Sanghuang. We used mACPpred to perform sequence scans after theoretical trypsin hydrolysis and discovered nine fragments with an anticancer probability of over 0.60. The study used AlphaFold 2 to perform structural modeling of the first three ACPs discovered, which had blast results from the Cancer PPD database. Using reverse docking technology, we found the target proteins and interacting residues of two ACPs with an unknown mechanism. Reverse docking results predicted the binding modes of the ACPs and their target protein. In addition, we determined the active part of ACPs by quantum chemical calculation. Our study provides a framework for the future discovery of functional peptides from foods. The ACPs discovered have the potential to be used as drugs in oncology clinical treatment after further research.