Systematic analysis of traditional Chinese medicine prescriptions provides new insights into drug combination therapy for pox.

ETHNOPHARMACOLOGICAL RELEVANCE: The decline in cross-protection provided by the smallpox vaccine increases the risk of infection from other poxviruses. While drug combinations are a promising management, they remain underdeveloped for poxviruses. Prior to the development of the smallpox vaccine, China had long relied on herbal medicine to combat pox and accumulated a wealth of knowledge regarding different herb combinations and symptoms related to pox. The information was documented in the form of prescriptions. AIM OF THE STUDY: The extensive data of prescriptions offer the potential for uncovering commonalities underlying these prescriptions, thereby providing valuable insights into the development of drug combinations against pox. MATERIALS AND METHODS: The 2344 prescriptions were collected from the LTM-TCM database and 12 traditional Chinese medicine books. Firstly, the relative frequency of citation was utilized to identify the most used herbs among these prescriptions. TCMSP and LTM-TCM databases were employed to gather information about active compounds and their targets. GeneCards and DisGeNET databases were utilized to determine the associated targets for smallpox, cowpox, chickenpox, and mpox. Subsequently, network pharmacology analysis was conducted to investigate potential pathway information related to the most used herbs. A comparison of active compounds from these herbs resulted in the identification of 29 high-frequency compounds. The functions of these compounds were elucidated through gene overlap analysis, docking, and literature review. Finally, we summarized pox-related symptoms and used fidelity levels to distinguish specific herbs for corresponding symptoms. RESULTS: Based on 2344 traditional pox-related prescriptions, we identified 19 most used herbs and 64 associated bio-functional modules for poxvirus treatment, with the most significant one being immunoregulation primarily involving CD4+ regulation. We also identified 29 leads that possess anti-inflammatory, antimicrobial, and antiviral properties. These herbs and leads hold the potential for pox treatment. Additionally, docking analysis suggested that these leads could inhibit poxvirus DNA synthesis, RNA capping machinery processes, and mature poxvirus particle formation, as well as immunosuppressors. The clinical features of mpox in 2022 were found to align well with our description of symptoms related to the pox. CONCLUSION: Through the analysis of 2344 prescriptions for pox treatment, we obtained a comprehensive library of the most used herbs and high-frequency compounds, along with their potential functional spectrum. These libraries served as raw resources for drug combination development, while the identified symptom patterns and specific herbs greatly enhanced our insight into diverse treatments for pox patients.

Identification of novel PHGDH inhibitors based on computational investigation: an all-in-one combination strategy to develop potential anti-cancer candidates.

Objective: Biological studies have elucidated that phosphoglycerate dehydrogenase (PHGDH) is the rate-limiting enzyme in the serine synthesis pathway in humans that is abnormally expressed in numerous cancers. Inhibition of the PHGDH activity is thought to be an attractive approach for novel anti-cancer therapy. The development of structurally diverse novel PHGDH inhibitors with high efficiency and low toxicity is a promising drug discovery strategy. Methods: A ligand-based 3D-QSAR pharmacophore model was developed using the HypoGen algorithm methodology of Discovery Studio. The selected pharmacophore model was further validated by test set validation, cost analysis, and Fischer randomization validation and was then used as a 3D query to screen compound libraries with various chemical scaffolds. The estimated activity, drug-likeness, molecular docking, growing scaffold, and molecular dynamics simulation processes were applied in combination to reduce the number of virtual hits. Results: The potential candidates against PHGDH were screened based on estimated activity, docking scores, predictive absorption, distribution, metabolism, excretion, and toxicity (ADME/T) properties, and molecular dynamics simulation. Conclusion: Finally, an all-in-one combination was employed successfully to design and develop three potential anti-cancer candidates.

The Mechanism of Action of the Active Ingredients of Coptidis rhizoma against Porcine Epidemic Diarrhea Was Investigated Using Network Pharmacology and Molecular Docking Technology.

The objective of this study was to elucidate the mechanism of action of the active components of Coptidis rhizoma against porcine epidemic diarrhea and to provide a theoretical foundation for further development of novel anti-PED therapeutic agents based on Coptidis rhizoma. The potential targets of Coptidis rhizoma against PEDV were identified through a comprehensive literature review and analysis using the TCMSP pharmacological database, SwissDrugDesign database, GeneCards database, and UniProt database. Subsequently, the STRING database and Cytoscape 3.7.1 software were employed to construct a protein-protein interaction (PPI) network and screen key targets. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted on the identified targets. Molecular docking studies were performed using AutoDock 1.5.7 software to analyze the binding energy and modes of interaction between the active components of Coptidis rhizoma and the target proteins. The PyMOL 2.5.0a0 software was employed to visualize the docking results. Through comprehensive analysis, 74 specific targets of active components of Coptidis rhizoma against PEDV were identified. The core gene targets were screened, and an interaction network diagram was subsequently generated. Ultimately, 14 core targets were identified, with STAT3, ESR1, CASP3, and SRC exhibiting the most significant interactions. GO enrichment analysis revealed a total of 215 molecular items, including 48 biological function items, 139 biological process items, and 28 cellular component items. KEGG enrichment analysis identified 140 signaling pathways. Molecular docking analysis demonstrated that epiberberine and palmatine exhibited high binding affinity with STAT3 protein, worenine showed high binding affinity with ESR1 protein, obacunone exhibited high binding affinity with CASP3 protein, and epiberberine, obacunone, berberine, and berberruine exhibited high binding affinity with SRC protein. A network pharmacology and molecular docking technology approach was employed to screen six important active components of Coptidis rhizoma and four important potential targets against PEDV infection. The findings indicated that the active components of Coptidis rhizoma could serve as promising pharmaceutical agents for the prevention and control of PEDV, with significant potential for clinical application.

Effects of anaerobic treatment on the non-volatile components and angiotensin-converting enzyme (ACE) inhibitory activity of purple-colored leaf tea.

This study investigated the effect of anaerobic treatment on the non-volatile components and angiotensin-converting enzyme (ACE) inhibitory activity in purple-colored leaf tea. Results showed that after 8 h of anaerobic treatment, the γ-aminobutyric acid (GABA) content significantly increased from 0.02 mg/g to 1.72 mg/g (p < 0.05), while lactic acid content gradually rose from non-detectable levels to 3.56 mg/g. Notably, certain flavonols like quercetin and myricetin exhibited significant increments, whereas the total anthocyanins (1.01 mg/g) and epigallocatechin-3-(3''-O-methyl) gallate (13.47 mg/g) contents remained almost unchanged. Furthermore, the ACE inhibition rate of purple-colored leaf tea increased significantly from 42.16% to 49.20% (p < 0.05) at a concentration of 2 mg/mL. Moreover, galloylated catechins showed stronger ACE inhibitory activity than non-galloylated catechins in both in vitro ACE inhibitory activity and molecular docking analysis. These findings might contribute to the development of special purple-colored leaf tea products with potential therapy for hypertension.

Discovering novel Cathepsin L inhibitors from natural products using artificial intelligence.

Cathepsin L (CTSL) is a promising therapeutic target for metabolic disorders. Current pharmacological interventions targeting CTSL have demonstrated potential in reducing body weight gain, serum insulin levels, and improving glucose tolerance. However, the clinical application of CTSL inhibitors remains limited. In this study, we used a combination of artificial intelligence and experimental methods to identify new CTSL inhibitors from natural products. Through a robust deep learning model and molecular docking, we screened 150 molecules from natural products for experimental validation. At a concentration of 100 µM, we found that 36 of them exhibited more than 50 % inhibition of CTSL. Notably, 13 molecules displayed over 90 % inhibition and exhibiting concentration-dependent effects. The molecular dynamics simulation on the two most potent inhibitors, Plumbagin and Beta-Lapachone, demonstrated stable interaction at the CTSL active site. Enzyme kinetics studies have shown that these inhibitors exert an uncompetitive inhibitory effect on CTSL. In conclusion, our research identifies Plumbagin and Beta-Lapachone as potential CTSL inhibitors, offering promising candidates for the treatment of metabolic disorders and illustrating the effectiveness of artificial intelligence in drug discovery.

Rapidly screening of pancreatic lipase inhibitors from Clematis tangutica using affinity ultrafiltration-HPLC-QTOFMS technique combined with targeted separation, in vitro validation, and molecular docking.

INTRODUCTION: Screening of novel pancreatic lipase inhibitors from complex natural products is a meaningful task. OBJECTIVES: Through accurately screening and separating pancreatic lipase inhibitors from Clematis tangutica (C. tangutica), to discover new leading compounds for slimming and accelerate the development and utilization of Tibetan medicine resources. METHODS: An integrated strategy that combines affinity ultrafiltration and high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (AU-HPLC-QTOFMS), targeted separation, in vitro validation, and molecular docking was developed to screen pancreatic lipase inhibitors from C. tangutica. The AU-HPLC-QTOFMS technique was performed to fish for the potential active substances. Macroporous resin, preparative liquid chromatography, and high-speed countercurrent chromatography were implemented for the accurate and targeted separation of active compounds. The inhibitory activities of target compounds to pancreatic lipase were detected by the inhibition experiments in vitro. The binding affinities and binding sites were analyzed using molecular docking. RESULTS: A total of eleven kinds of pancreatic lipase inhibitory substances were screened from C. tangutica. Seven triterpenoid saponins were screened for the first time as lipase inhibitors and successfully prepared with purities higher than 97%. Tanguticoside B, clematangoticoside J, hederoside H1, and rutin showed stronger inhibitory effects with IC50 values of 1.539 ± 0.048, 1.661 ± 0.092, 1.793 ± 0.069, and 1.792 ± 0.094 mmol/l. Moreover, they have the lowest ΔG values of -10.84, -9.97, -10.87, and -9.39 kcal/mol to pancreatic lipase. CONCLUSION: The integrated strategy using AU-HPLC-QTOFMS, targeted separation, in vitro validation, and molecular docking was feasible for rapidly screening and directionally isolating pancreatic lipase inhibitors from C. tangutica.

Investigating how HIV-1 antiretrovirals differentially behave as substrates and inhibitors of P-glycoprotein via molecular dynamics simulations.

HIV-1 can rapidly infect the brain upon initial infection, establishing latent reservoirs that induce neuronal damage and/or death, resulting in HIV-Associated Neurocognitive Disorder. Though anti-HIV-1 antiretrovirals (ARVs) suppress viral load, the blood-brain barrier limits drug access to the brain, largely because of highly expressed efflux proteins like P-glycoprotein (P-gp). While no FDA-approved P-gp inhibitor currently exists, HIV-1 protease inhibitors show promise as partial P-gp inhibitors, potentially enhancing drug delivery to the brain. Herein, we employed docking and molecular dynamics simulations to elucidate key differences in P-gp's interactions with several antiretrovirals, including protease inhibitors, with known inhibitory or substrate-like behaviors towards P-gp. Our results led us to hypothesize new mechanistic details of small-molecule efflux by and inhibition of P-gp, where the "Lower Pocket" in P-gp's transmembrane domain serves as the primary initial site for small-molecule binding. Subsequently, this pocket merges with the more traditionally studied drug binding site-the "Upper Pocket"-thus funneling small-molecule drugs, such as ARVs, towards the Upper Pocket for efflux. Furthermore, our results reinforce the understanding that both binding energetics and changes in protein dynamics are crucial in discerning small molecules as non-substrates, substrates, or inhibitors of P-gp. Our findings indicate that interactions between P-gp and inhibitory ARVs induce bridging of transmembrane domain helices, impeding P-gp conformational changes and contributing to the inhibitory behavior of these ARVs. Overall, insights gained in this study could serve to guide the design of future P-gp-targeting therapeutics for a wide range of pathological conditions and diseases, including HIV-1.

Exserolide J ameliorates lipid accumulation in vitro by regulating liver X receptor alpha and peroxisome proliferator-activated receptor alpha proteins.

Exserolides are isocoumarin derivatives containing lactone moiety. Recently, some isocoumarins have been demonstrated to ameliorate hyperlipidemia, a major factor for inducing cardiovascular diseases. However, the effects and mechanisms of action of exserolides on hyperlipidemia are not known. The aim of this study is to investigate whether the marine fungus Setosphaeria sp.-derived exserolides (compounds I, J, E, and F) exert lipid-lowering effects via improving reverse cholesterol transport (RCT) in vitro. RAW264.7 macrophages and HepG2 cells were used to establish lipid-laden models, and the levels of intracellular lipids and RCT-related proteins were determined by assay kits and Western blotting, respectively. We observed that exserolides (at a 5 µM concentration) significantly decreased intracellular cholesterol and triglyceride levels in oxidized low-density lipoprotein-laden RAW264.7 cells and markedly improved [3H]-cholesterol efflux. Among the four tested compounds, exserolide J increased the protein levels of ATP-binding cassette transporter A1, peroxisome proliferator-activated receptor α (PPARα), and liver X receptor α (LXRα). Furthermore, treatment with exserolides significantly decreased oleic acid-laden lipid accumulation in HepG2 hepatocytes. Mechanistically, exserolides enhance PPARα protein levels; furthermore, compound J increases cholesterol 7 alpha-hydroxylase A1 and LXRα protein levels. Molecular docking revealed that exserolides, particularly compound J, can interact with PPARα and LXRα proteins. These data suggest that the terminal carboxyl group of compound J plays a key role in lowering lipid levels by stimulating LXRα and PPARα proteins. In conclusion, compound J exhibits powerful lipid-lowering effects in vitro. However, its hypolipidemic effects in vivo should be investigated in the future.

Unraveling the treatment effects of huanglian jiedu decoction on drug-induced liver injury based on network pharmacology, molecular docking and experimental validation.

Huanglian Jiedu Decoction (HJD) is a well-known Traditional Chinese Medicine formula that has been used for liver protection in thousands of years. However, the therapeutic effects and mechanisms of HJD in treating drug-induced liver injury (DILI) remain unknown. In this study, a total of 26 genes related to both HJD and DILI were identified, which are corresponding to a total of 41 potential active compounds in HJD. KEGG analysis revealed that Tryptophan metabolism pathway is particularly important. The overlapped genes from KEGG and GO analysis indicated the significance of CYP1A1, CYP1A2, and CYP1B1. Experimental results confirmed that HJD has a protective effect on DILI through Tryptophan metabolism pathway. In addition, the active ingredients Corymbosin, and Moslosooflavone were found to have relative strong intensity in UPLC-Q-TOF-MS/MS analysis, showing interactions with CYP1A1, CYP1A2, and CYP1B1 through molecule docking. These findings could provide insights into the treatment effects of HJD on DILI.

Indole alkaloids from endophytic fungus Robillarda sessilis and their antibacterial activity.

Three undescribed indole alkaloids, fusarindoles F and G (1 and 2), and chlamydosporin B (3), together with five known compounds (4-8) were isolated from Robillarda sessilis. Their structures were elucidated based on NMR, UV, HRESIMS, and ECD calculation. Fusarindole F (1) own unusual asymmetric bis-indole structure. Compounds 5, 6, 7 exhibited moderate antibacterial activity against methicillin-resistant Staphylococcus aureus with a MIC value of 12.5 µg/mL. According to molecular docking experiment, the target proteins of compound 7 against methicillin-resistant S. aureus may be ELANE, MAOB and STAT3.

Cobdock: an accurate and practical machine learning-based consensus blind docking method.

Probing the surface of proteins to predict the binding site and binding affinity for a given small molecule is a critical but challenging task in drug discovery. Blind docking addresses this issue by performing docking on binding regions randomly sampled from the entire protein surface. However, compared with local docking, blind docking is less accurate and reliable because the docking space is too largetly sampled. Cavity detection-guided blind docking methods improved the accuracy by using cavity detection (also known as binding site detection) tools to guide the docking procedure. However, it is worth noting that the performance of these methods heavily relies on the quality of the cavity detection tool. This constraint, namely the dependence on a single cavity detection tool, significantly impacts the overall performance of cavity detection-guided methods. To overcome this limitation, we proposed Consensus Blind Dock (CoBDock), a novel blind, parallel docking method that uses machine learning algorithms to integrate docking and cavity detection results to improve not only binding site identification but also pose prediction accuracy. Our experiments on several datasets, including PDBBind 2020, ADS, MTi, DUD-E, and CASF-2016, showed that CoBDock has better binding site and binding mode performance than other state-of-the-art cavity detector tools and blind docking methods.

Integrative Analysis of Machine Learning and Molecule Docking Simulations for Ischemic Stroke Diagnosis and Therapy.

Due to the narrow therapeutic window and high mortality of ischemic stroke, it is of great significance to investigate its diagnosis and therapy. We employed weighted gene coexpression network analysis (WGCNA) to ascertain gene modules related to stroke and used the maSigPro R package to seek the time-dependent genes in the progression of stroke. Three machine learning algorithms were further employed to identify the feature genes of stroke. A nomogram model was built and applied to evaluate the stroke patients. We analyzed single-cell RNA sequencing (scRNA-seq) data to discern microglia subclusters in ischemic stroke. The RNA velocity, pseudo time, and gene set enrichment analysis (GSEA) were performed to investigate the relationship of microglia subclusters. Connectivity map (CMap) analysis and molecule docking were used to screen a therapeutic agent for stroke. A nomogram model based on the feature genes showed a clinical net benefit and enabled an accurate evaluation of stroke patients. The RNA velocity and pseudo time analysis showed that microglia subcluster 0 would develop toward subcluster 2 within 24 h from stroke onset. The GSEA showed that the function of microglia subcluster 0 was opposite to that of subcluster 2. AZ_628, which screened from CMap analysis, was found to have lower binding energy with Mmp12, Lgals3, Fam20c, Capg, Pkm2, Sdc4, and Itga5 in microglia subcluster 2 and maybe a therapeutic agent for the poor development of microglia subcluster 2 after stroke. Our study presents a nomogram model for stroke diagnosis and provides a potential molecule agent for stroke therapy.

Therapeutic role of Wuda granule in gastrointestinal motility disorder through promoting gastrointestinal motility and decreasing inflammatory level.

Introduction: Previous studies indicated that Wuda Granule (WDG) has been applied in the treatment of gastrointestinal motility disorder (GMD), but the effect and underlying mechanisms is yet to be elucidated. This study aimed to explore the mechanism and pharmacological effect of WDG for GMD via network analysis, verification of animal experiments and clinical experiments. Methods: The chemical components of WDG were identified from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP, http://lsp.nwu.edu.cn/index.php), and the Encyclopedia of Traditional Chinese Medicine (ETCM, http://www.tcmip.cn/ETCM/index.php/Home/Index/) according to oral bioavailability (OB) ≥ 20% and drug-likeness (DL) ≥ 0.10. The targets of WDG compounds were retrieved from the Swiss Target Prediction database (http://www.swisstargetprediction.ch/) and targets related to GMD were retrieved from GeneCards database (https://www.genecards.org/). Network analysis were performed to screen the key active compounds of WDG and its hub targets. Then the pharmacological effect of WDG were verified via vivo experiments in rats and clinical experiments. Results: The results showed that 117 effective active compounds of WDG were screened and 494 targets of WDG compounds targeting GMD were selected. These targets were involved in the biological process of inflammatory regulation and the regulation of gastrointestinal motility. The mechanism was mainly involved in the regulation of PI3K-Akt signaling pathway and Rap1 signaling pathway. In addition, molecular docking analysis suggested that eight key active compounds of WDG may be mainly responsible for the effect of WDG on GMD by targeting HARS, AKT, and PIK3CA, respectively. Animal experiments and clinical trials both suggested that WDG could exert therapeutical effect on GMD via inhibiting inflammation and promoting gastrointestinal motility, it could also improve digestive function of patients with laparoscopic colorectal cancer after surgery. Conclusion: This study was the first to demonstrate that WDG improved GMD mainly via inhibiting inflammatory level and promoting gastrointestinal motility, providing new insights for the understanding of WDG for GMD, inspiration for future research and reference for clinical strategy in terms of the treatment of GMD.

Exploration of 2D and 3D-QSAR analysis and docking studies for novel dihydropteridone derivatives as promising therapeutic agents targeting glioblastoma.

Background: Dihydropteridone derivatives represent a novel class of PLK1 inhibitors, exhibiting promising anticancer activity and potential as chemotherapeutic drugs for glioblastoma. Objective: The aim of this study is to develop 2D and 3D-QSAR models to validate the anticancer activity of dihydropteridone derivatives and identify optimal structural characteristics for the design of new therapeutic agents. Methods: The Heuristic method (HM) was employed to construct a 2D-linear QSAR model, while the gene expression programming (GEP) algorithm was utilized to develop a 2D-nonlinear QSAR model. Additionally, the CoMSIA approach was introduced to investigate the impact of drug structure on activity. A total of 200 novel anti-glioma dihydropteridone compounds were designed, and their activity levels were predicted using chemical descriptors and molecular field maps. The compounds with the highest activity were subjected to molecular docking to confirm their binding affinity. Results: Within the analytical purview, the coefficient of determination (R2) for the HM linear model is elucidated at 0.6682, accompanied by an R2 cv of 0.5669 and a residual sum of squares (S2) of 0.0199. The GEP nonlinear model delineates coefficients of determination for the training and validation sets at 0.79 and 0.76, respectively. Empirical modeling outcomes underscore the preeminence of the 3D-QSAR model, succeeded by the GEP nonlinear model, whilst the HM linear model manifested suboptimal efficacy. The 3D paradigm evinced an exemplary fit, characterized by formidable Q2 (0.628) and R2 (0.928) values, complemented by an impressive F-value (12.194) and a minimized standard error of estimate (SEE) at 0.160. The most significant molecular descriptor in the 2D model, which included six descriptors, was identified as "Min exchange energy for a C-N bond" (MECN). By combining the MECN descriptor with the hydrophobic field, suggestions for the creation of novel medications were generated. This led to the identification of compound 21E.153, a novel dihydropteridone derivative, which exhibited outstanding antitumor properties and docking capabilities. Conclusion: The development of 2D and 3D-QSAR models, along with the innovative integration of contour maps and molecular descriptors, offer novel concepts and techniques for the design of glioblastoma chemotherapeutic agents.

Integrated strategy for the screening of cyclooxygenase-2 inhibitors from triterpenoid saponins in Clematis tangutica.

INTRODUCTION: Screening of novel cyclooxygenase-2 (COX-2) inhibitors from complex natural products is not an easy task. OBJECTIVES: To establish an efficient and feasible strategy for screening COX-2 inhibitors from triterpenoid saponins (TPSs) in Clematis tangutica. METHODS: Taking TPSs in C. tangutica as example, an optimized macroporous resin (MR) method was established for the enrichment of TPSs. High-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (HPLC-QTOFMS) was performed to establish the phytochemical profiling of TPSs. Molecular docking was performed to predict the ligand-target interactions and discover the active substances. Chemometric techniques were performed to visualize the structure-effect relationships. High-speed countercurrent chromatography and preparative HPLC were performed to prepare the targets. In vitro activity experiment of COX-2 was performed to verify the virtual screening results. RESULTS: TPSs in C. tangutica were well enriched with the recovery rate of (80.22 ± 2.37)%. Thirty-four kinds of TPSs of oleanane type were deduced by HPLC-QTOFMS. Five TPSs of clematangoside C, clematangoside D, clematangoticoside J, hederoside H1 , and hederasaponin B showed stronger binding abilities with COX-2. The structure with more sugar groups at C-28 may be more conducive to the combination with COX-2. Targets were prepared with purities all above 98%. The IC50 values of target TPSs were 6.03 ± 0.24, 12.44 ± 0.15, 9.36 ± 0.19, 4.78 ± 0.13, and 2.59 ± 0.11 µmol/L, respectively. CONCLUSION: The integrated strategy using MR, HPLC-QTOFMS, molecular docking, chemometrics, target preparation, and in vitro verification was feasible for rapidly screening COX-2 inhibitors from TPSs in C. tangutica.

Isolation and Identification of Herbicidal Active Compounds from Brassica oleracea L. and Exploration of the Binding Sites of Brassicanate A Sulfoxide.

Brassica oleracea L. has strong allelopathic effects on weeds. However, the allelochemicals with herbicidal activity in B. oleracea L. are still unknown. In this study, we evaluated the activity of allelochemicals isolated from Brassica oleracea L. based on the germination and growth of model plant Lactuca sativa Linn., grass weed Panicum miliaceum, and broadleaf weed Chenopodium album. Additionally, we employed molecular docking to predict the binding of brassicanate A sulfoxide to herbicide targets. The results of this study showed that eight compounds with herbicidal activity were isolated from B. oleracea L., and the predicted results indicated that brassicanate A sulfoxide was stably bound to dihydroxyacid dehydratase, hydroxymethylpyruvate dioxygenase, acetolactate synthase, PYL family proteins and transport inhibitor response 1. This research provides compound sources and a theoretical foundation for the development of natural herbicides.

Sitravatinib is a potential EGFR inhibitor and induce a new death phenotype in Glioblastoma.

Glioblastoma (GBM) is a highly lethal neurological tumor that presents significant challenge for clinicians due to its heterogeneity and high mortality rate. Despite extensive research, there is currently no effective drug treatment available for GBM. Research evidence has consistently demonstrated that the epidermal growth factor receptor (EGFR) promotes tumor progression and is associated with poor prognosis in several types of cancer. In glioma, EGFR abnormal amplification is reported in approximately 40% of GBM patients, with overexpression observed in 60% of cases, and deletion or mutation in 24% to 67% of patients. In our study, Sitravatinib, a potential EGFR inhibitor, was identified through molecular docking screening based on protein structure. The targeting of EGFR and the tumor inhibitory effect of Sitravatinib on glioma were verified through cellular and in vivo experiments, respectively. Our study also revealed that Sitravatinib effectively inhibited GBM invasive and induced DNA damage and cellular senescence. Furthermore, we observed a novel cell death phenotype induced by Sitravatinib, which differed from previously reported programmed death patterns such as apoptosis, pyroptosis, ferroptosis, and necrosis.

3D,2D-QSAR study and docking of novel quinazolines as potential target drugs for osteosarcoma.

Background: Quinazolines are an important class of benzopyrimidine heterocyclic compounds with a promising antitumor activity that can be used for the design and development of osteosarcoma target compounds. Objective: To predict the compound activity of quinazoline compounds by constructing 2D- and 3D-QSAR models, and to design new compounds according to the main influencing factors of compound activity in the two models. Methods: First, heuristic method and GEP (gene expression programming) algorithm were used to construct linear and non-linear 2D-QSAR models. Then a 3D-QSAR model was constructed using CoMSIA method in SYBYL software package. Finally, new compounds were designed according to molecular descriptors of 2D-QSAR model and contour maps of 3D-QSAR model. Several compounds with optimal activity were used for docking experiments with osteosarcoma related targets (FGFR4). Results: The non-linear model constructed by GEP algorithm was more stable and predictive than the linear model constructed by heuristic method. A 3D-QSAR model with high Q2 (0.63) and R 2 (0.987) values and low error values (0.05) was obtained in this study. The success of the model fully passed the external validation formula, proving that the model is very stable and has strong predictive power. 200 quinazoline derivatives were designed according to molecular descriptors and contour maps, and docking experiments were carried out for the most active compounds. Compound 19g.10 has the best compound activity with good target binding capability. Conclusion: To sum up, the two novel QSAR models constructed were very reliable. The combination of descriptors in 2D-QSAR with COMSIA contour maps provides new design ideas for future compound design in osteosarcoma.

Lentinuses A-B, two alkaloids from the marine-derived fungus Lentinus sajor-caju with potent anti-pulmonary fibrosis activity.

By adding natural amino acids into the medium as sole nitrogen source, twenty-four compounds, including two new alkaloids lentinuses A-B (1-2) with a rare oxazinone core in marine natural products, one new natural product 3-acetamido-4-phenylfurazan (3), 9ß-ergosterol (22) were firstly discovered from a marine fungus, and twenty known compounds (4-21, 23-24) were isolated from the marine-derived fungus Lentinus sajor-caju. The chemical structures of all these compounds were elucidated by HRMS, NMR spectroscopy and X-ray diffraction. Compounds 1-24 were evaluated for their inhibitory activity against TGF-ß1-induced collagen accumulation in human fetal lung fibroblasts (HFL1). Compounds 2, 3, 12, 22, and 23 showed potent activity against TGF-ß1-induced collagen accumulation and low toxicity to HFL1 cells. The binding mode of lentinus B (2) with TGF-ß1 receptor was then performed by using Schrödinger software, and the result showed that lentinus B possesses a strong binding force such as hydrogen bonding and hydrophobic interactions to the protein, which may provide a theoretical basis to design more potent anti-fibrotic drugs in the future.

From Genome Mining to Protein Engineering: A Structural Bioinformatics Route.

This chapter outlines applications in genome mining, along with computational methods to predict protein structure and protein-ligand docking. It offers a simple computational route to rapidly identify proteins of interest from genomic and proteomic data, to accurately predict their three-dimensional structures, and to dock small molecules to their binding pockets and strategies to improve their biophysical properties depending on the needs of the experimental researcher.