Protein Data Bank (PDB): The Single Global Macromolecular Structure Archive

Study the Mechanism of Gualou Niubang Decoction in Treating Plasma Cell Mastitis Based on Network Pharmacology and Molecular Docking

Objective

Explore the potential molecular mechanisms behind the therapeutic functions of Gualou Niubang decoction (GLNBD) in the treatment of plasma cell mastitis (PCM) by network pharmacology and molecular docking.

Methods

GLNBD is a formula of Chinese traditional medicine consisting of 12 herbs. The potential active ingredients of GLNBD and their target genes were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database, and PCM-related target genes were obtained from GeneCards, OMIM, and NCBI databases, using R language to obtain intersection targets; then, the STRING database and Cytoscape software were used to establish protein-protein interaction networks and herb ingredient target networks. DAVID was used to perform GO and KEGG pathway enrichment analyses on the intersection target. PyMoL-2.5.0 and AutoDock Tools-1.5.6 were used to verify the molecular docking.

Results

164 ingredients and 58 intersection targets were obtained in the treatment of PCM by GLNBD. Four key active compounds and four key proteins were identified. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses showed that biological functions of potential target genes were associated with negative regulation of the apoptotic process, response to hypoxia, positive regulation of transcription, and DNA-templated, with related pathways involving the pathway in cancer, phosphatidylinositol 3-kinase (PI3K) Akt signaling pathway, and AGE-RAGE signaling pathway in diabetic complications. Moreover, the binding activities of key target genes and essential active compounds of Chinese herbal medicines in GLNBD were further validated by molecular docking. The results showed that the docking results were stable and had good binding ability.

Conclusion

This study suggested that four potential key active components, including quercetin, luteolin, fisetin, and kaempferol, were identified in GLNBD, which could interact with ALB, EGFR, IL-6, and VEGFA modulating the activation of the pathway in cancer, PI3K-Akt pathway, and AGE-RAGE signaling pathway in diabetic complications.

The mechanism of dioscin preventing lung cancer based on network pharmacology and experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Dioscorea nipponica Makino as a Chinese folk medicine has been used for the treatment of chronic bronchitis, cough, and asthma. Several studies have established the antimetastatic potential of Dioscorea nipponica Makino extract. Dioscin is a major bioactive compound in Dioscorea nipponica Makino and has anti-tumor property in lung cancer cell lines. However, the preventive effect of dioscin against lung cancer and its key mechanism haven't been identified yet.

AIM OF STUDY

To identify the prevention effect of dioscin on lung cancer and explore its key mechanism based on network pharmacology and experimental validation.

METHODS

The potential targets of dioscin were obtained from the HERB database. The therapeutic targets of lung cancer were acquired from the GeneCards database. Protein-protein interaction network (PPI) was constructed in the STRING 11.0 database. The David database was used for enrichment analysis. Molecular Docking was finished by the AutoDock Vina. NSCLC cell lines and mouse lung cancer model were used to confirm the prevention effect of dioscin on lung cancer and its key mechanism.

RESULTS

76 potential targets of dioscin were identified to be involved in lung cancer treatment, which refer to 512 biological processes, 47 molecular functions, 77 cellular components and 107 signal pathways. The molecular docking suggested that dioscin might bind to AKT1, Caspase3, TP53, C-JUN and IL-6. The DARTS indicated that dioscin could bind to AKT1. In vitro, dioscin could decrease proliferation, invasion and migration in A549 and PC-9 cells with the significant reduction in the expression of p-AKT, MMP2, and PCNA. In vivo, dioscin could reduce lung nodules, lung injury, and mortality in mouse lung cancer model with reducing the expression of p-AKT, MMP2, PCNA and increasing the expression of active-caspase3.

CONCLUSION

Dioscin could prevent lung cancer and its key target is AKT1 kinase, a center protein of PI3K/AKT signaling pathway.

Integrative illustration of a JCVI-syn3A minimal cell

Data from genomics, proteomics, structural biology and cryo-electron microscopy are integrated into a structural illustration of a cross section through an entire JCVI-syn3.0 minimal cell. The illustration is designed with several goals: to inspire excitement in science, to depict the underlying scientific results accurately, and to be feasible in traditional media. Design choices to achieve these goals include reduction of visual complexity with simplified representations, use of orthographic projection to retain scale relationships, and an approach to color that highlights functional compartments of the cell. Given that this simple cell provides an attractive laboratory for exploring the central processes needed for life, several functional narratives are included in the illustration, including division of the cell and the first depiction of an entire cellular proteome. The illustration lays the foundation for 3D molecular modeling of this cell.

Characterizing the Antitumor Effect of Coptis chinensis and Mume Fructus against Colorectal Cancer Based on Pharmacological Analysis

Colorectal cancer (CRC) is the third most diagnosed cancer worldwide and is a significant cause of cancer-related deaths. Previous studies have observed that Coptis chinensis (CC) and Mume Fructus (MF) are effective against CRC, enteritis, and intestinal dysbiosis, but the chemical and pharmacological mechanisms remain poorly understood. In this study, we employed pharmacological network analysis to reveal mechanisms underlying the therapeutic effect of CC and MF against CRC. All compounds and targeted genes were obtained from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP). Differentially expressed genes (DEGs) were identified based on GSE146587, GSE156720, and GSE184093 datasets. A protein-protein interaction (PPI) network was constructed to identify putative target genes of CC and MF. Ten key targeted genes were identified, including CCND1, ICAM1, IL1B, IL-6, MMP1, MMP3, MMP9, MYC, SERPINE1, and VEGFA. Among these genes, six (ICAM1, IL1B, IL-6, MMP1, MMP3, MMP9, and SERPINE1) were positively correlated with levels of effector memory CD4 T cells and natural killer T cells, and three (CCND1, MYC, and VEGFA) were negatively correlated with type 17 T helper cells and CD56dim natural killer cells. Molecular docking analysis showed that four compounds of CC and MF (kaempferol, oleanolic acid, quercetin, and ursolic acid) could affect CRC by interacting with target genes. Our study proved that pharmacological analysis could reliably assess the mechanism of traditional Chinese medicines for treating cancer.

A Mechanistic Exploratory Study on the Therapeutic Efficacy of Astragaloside IV Against Diabetic Retinopathy Revealed by Network Pharmacology

Purpose: Diabetic retinopathy (DR) is a serious complication of diabetes mellitus, which nearly happens to all the diabetic sufferers. This study aims to identify the preliminary molecular regulation involved in the therapeutic efficacy of astragaloside IV (AS- IV) for DR.

Methods: Diabetic rat models were established and treated with AS-IV. Optical coherence tomography (OCT) and Hematoxylin-eosin (HE) staining was employed to demonstrate the histopathological changes. The main targets of AS-IV were identified by searching from public databases of traditional Chinese medicine (GeneCards, PharmMapper and Swiss Target Prediction). Besides, disease targets of DR were also obtained by integrated data from GEO datasets and predicted from public databases. Protein-protein interaction (PPI) network was constructed by Cytoscape with overlapping genes and 10 core targets were selected, on which Gene Ontology (GO) along with Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted. The interaction between AS-IV and these crucial genes were analyzed using molecular docking. RT-qPCR and western blot were used to verify the expression variation of core targets.

Results: OCT imaging and HE staining demonstrated that AS-IV administration significantly increased retinal thickness in diabetic rats, obviously alleviating DR induced histopathological changes as well as elevated blood glucose levels. 107 common targets of AS-IV and DR were determined after intersection. PPI network analysis filtered 10 hub genes potentially targeted by AS-IV, including VEGFA, CASP3, HIF1α, STAT3, CTNNB1, SRC, AKT1, EGFR, IL1β and IL6. Enrichment analysis indicated that these genes were mainly enriched in biological processes like T cell activation, epithelial cell proliferation and protein kinase B signaling, and involved in oxidative stress, apoptosis and inflammation-related pathways. The molecular docking prediction suggested that AS-IV exhibited stable binding to these core targets. In addition, mRNA levels of core targets in diabetic rats were differentially expressed before and after AS-IV treatment. Western blot further revealed that AS-IV treatment elevated DR-depressed protein levels of PI3K and AKT.

Conclusion: Our study elucidated the effect of AS-IV in attenuating retinopathy induced by diabetes in rats and preliminarily unveiled the therapeutic efficacy of AS-IV in the treatment of DR might be attributed to activation of PI3K-AKT signaling pathway.

Exploring the potential mechanism of emetine against coronavirus disease 2019 combined with lung adenocarcinoma: bioinformatics and molecular simulation analyses

BACKGROUND

Patients with lung adenocarcinoma (LUAD) may be more predisposed to coronavirus disease 2019 (COVID-19) and have a poorer prognosis. Currently, there is still a lack of effective anti-LUAD/COVID-19 drugs. Thus, this study aimed to screen for an effective anti-LUAD/COVID-19 drug and explore the potential mechanisms.

METHODS

Firstly, we performed differentially expressed gene (DEG) analysis on LUAD transcriptome profiling data in The Cancer Genome Atlas (TCGA), where intersections with COVID-19-related genes were screened out. Then, we conducted Cox proportional hazards analyses on these LUAD/COVID-19 DEGs to construct a risk score. Next, LUAD/COVID-19 DEGs were uploaded on Connectivity Map to obtain drugs for anti-LUAD/COVID-19. Finally, we used network pharmacology, molecular docking, and molecular dynamics (MD) simulation to explore the drug's therapeutic targets and potential mechanisms for anti-LUAD/COVID-19.

RESULTS

We identified 230 LUAD/COVID-19 DEGs and constructed a risk score containing 7 genes (BTK, CCL20, FURIN, LDHA, TRPA1, ZIC5, and SDK1) that could classify LUAD patients into two risk groups. Then, we screened emetine as an effective drug for anti-LUAD/COVID-19. Network pharmacology analyses identified 6 potential targets (IL6, DPP4, MIF, PRF1, SERPING1, and SLC6A4) for emetine in anti-LUAD/COVID-19. Molecular docking and MD simulation analyses showed that emetine exhibited excellent binding capacities to DDP4 and the main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

CONCLUSIONS

This study found that emetine may inhibit the entry and replication of SARS-CoV-2 and enhance tumor immunity by bounding to DDP4 and Mpro.

The Anti-Tumor Efficacy of Verbascoside on Ovarian Cancer via Facilitating CCN1-AKT/NF-κB Pathway-Mediated M1 Macrophage Polarization

Background

Ovarian cancer (OC) is the leading cause of gynecological cancer-related mortality. Verbascoside (VB) is a phenylpropanoid glycoside from Chinese herbs, with anti-tumour activities. This study aimed to investigate the effects and mechanism of VB on OC.

Methods

OC cell lines SKOV3 and A2780 were used in this study. Cell viability, proliferation, and migration were measured using CCK-8, clonogenic, and transwell assays, respectively. Apoptosis and M1/M2 macrophages were detected using flow cytometry. The interaction between VB and CCN1 was predicted by molecular docking. The mRNA expression of CCN1 was detected by RT-qPCR. The protein levels of CCN1, AKT, p-AKT, p65, and p-p65 were determined by western blotting. A xenograft mice model was established for in vivo validation.

Results

VB inhibited OC cell proliferation and migration in a dose-dependent manner, and promoted apoptosis and M1 macrophage polarization. VB downregulated CCN1 and inhibited the AKT/NF-κB pathway. LY294002, an AKT inhibitor, potentiated the anti-tumour effects of VB. CCN1 overexpression weakened the anti-tumour effects of VB and VB + LY294002. In vivo experiments verified that VB inhibited tumour growth and promoted M1 polarization, which is regulated by the CCN1-mediated AKT/NF-κB pathway.

Conclusion

VB triggers the CCN1-AKT/NF-κB pathway-mediated M1 macrophage polarization for protecting against OC.

In Vitro and In Silico Antistaphylococcal Activity of Indole Alkaloids Isolated from Tabernaemontana cymosa Jacq (Apocynaceae)

The species of the genus Tabernaemontana have a long tradition of use in different pathologies of infectious origins; the antibacterial, antifungal, and antiviral effects related to the control of the pathologies where the species of this genus are used, have been attributed to the indole monoterpene alkaloids, mainly those of the iboga type. There are more than 1000 alkaloids isolated from different species of Tabernaemontana and other genera of the Apocynaceae family, several of which lack studies related to antibacterial activity. In the present study, four monoterpene indole alkaloids were isolated from the seeds of the species Tabernaemontana cymosa Jacq, namely voacangine (1), voacangine-7-hydroxyindolenine (2), 3-oxovoacangine (3), and rupicoline (4), which were tested in an in vitro antibacterial activity study against the bacteria S. aureus, sensitive and resistant to methicillin, and classified by the World Health Organization as critical for the investigation of new antibiotics. Of the four alkaloids tested, only voacangine was active against S. aureus, with an MIC of 50 µg/mL. In addition, an in silico study was carried out between the four isolated alkaloids and some proteins of this bacterium, finding that voacangine also showed binding to proteins involved in cell wall synthesis, mainly PBP2 and PBP2a.

Exploring the Effect and Mechanism of Si-Miao-Yong-An Decoction on Abdominal Aortic Aneurysm Based on Mice Experiment and Bioinformatics Analysis

Background

Abdominal aortic aneurysm (AAA) is a fatal disease characterized by high morbidity and mortality in old population. Globally, effective drugs for AAA are still limited. Si-Miao-Yong-An decoction (SMYAD), a traditional Chinese medicine (TCM) formula with a high medical value, was reported to be successfully used in an old AAA patient. Thus, we reason that SMYAD may serve as a potential anti-AAA regime.

Objective

The exact effects and detailed mechanisms of SMYAD on AAA were explored by using the experimental study and bioinformatics analysis.

Methods

Firstly, C57BL/6N mice induced by Bap and Ang II were utilized to reproduce the AAA model, and the effects of SMYAD were systematically assessed according to histology, immunohistochemistry, and enzyme-linked immunosorbent assay (ELISA). Then, network pharmacology was applied to identify the biological processes, pathways, and hub targets of SMYAD against AAA; moreover, molecular docking was utilized to identify the binding ability and action targets.

Results

In an animal experiment, SMYAD was found to effectively alleviate the degree of pathological expansion of abdominal aorta and reduce the incidence of Bap/Ang II-induced AAA, along with reducing the damage to elastic lamella, attenuating infiltration of macrophage, and lowering the circulating IL-6 level corresponding to the animal study, and network pharmacology revealed the detailed mechanisms of SMYAD on AAA that were related to pathways of inflammatory response, defense response, apoptotic, cell migration and adhesion, and reactive oxygen species metabolic process. Then, seven targets, IL-6, TNF, HSP90AA1, RELA, PTGS2, ESR1, and MMP9, were identified as hub targets of SMYAD against AAA. Furthermore, molecular docking verification revealed that the active compounds of SMYAD had good binding ability and clear binding site with core targets related to AAA formation.

Conclusion

SMYAD can suppress AAA development through multicompound, multitarget, and multipathway, which provides a research direction for further study.

Identifying molecular structural features by pattern recognition methods

Identification of molecular structural features is a central part of computational chemistry. It would be beneficial if pattern recognition techniques could be incorporated to facilitate the identification. Currently, the quantification of the structural dissimilarity is mainly carried out by root-mean-square-deviation (RMSD) calculations such as in molecular dynamics simulations. However, the RMSD calculation underperforms for large molecules, showing the so-called “curse of dimensionality” problem. Also, it requires consistent ordering of atoms in two comparing structures, which needs nontrivial effort to fulfill. In this work, we propose to take advantage of the point cloud recognition using convex hulls as the basis to recognize molecular structural features. Two advantages of the method can be highlighted. First, the dimension of the input data structure is largely reduced from the number of atoms of molecules to the number of atoms of convex hulls. Therefore, the dimensionality curse problem is avoided, and the atom ordering process is saved. Second, the construction of convex hulls can be used to define new molecular descriptors, such as the contact area of molecular interactions. These new molecular descriptors have different properties from existing ones, therefore they are expected to exhibit different behaviors for certain machine learning studies. Several illustrative applications have been carried out, which provide promising results for structure–activity studies.

Identification of molecular structural features by point clouds and convex hulls.

Comprehensive Bioinformatics Analysis to Reveal Key RNA Targets and Hub Competitive Endogenous RNA Network of Keratoconus

Keratoconus (KC) is the most common corneal ectatic disease, with its pathological mechanisms unclear. We mainly performed bioinformatics approaches to reveal core RNA targets and hub competitive endogenous RNA (ceRNA) network and explored the potential regulatory mechanisms of ceRNA in KC. The high-throughput sequencing datasets GSE77938 and GSE151631 were downloaded from the Gene Expression Omnibus (GEO) database. The differential expression of mRNAs and lncRNAs was identified using the DESeq2 package. Functional enrichment analyses and protein–protein interaction (PPI) were executed. Then, the hub genes were filtered and molecular docking analysis was performed. Moreover, we predicted miRNAs through a website database and validated them using quantitative PCR (qPCR). Eventually, the lncRNA–miRNA–mRNA regulatory network was constructed by Cytoscape. We revealed that 428 intersected differentially expressed mRNA (DEGs) and 68 intersected differentially expressed lncRNA (DELs) were shared between the two datasets. Functional enrichment results innovatively showed that the ubiquitin-dependent protein catabolic process was upregulated in KC. The pathway enrichment showed that DEGs were mainly involved in NF-kB signaling and neurodegenerative diseases. In addition, we uncovered the top 20 hub genes in which FBXW11, FBXO9, RCHY1, and CD36 were validated by qPCR. Particularly, a small-molecule drug triptolide was predicted by molecular docking to be a candidate drug for treating KC. Moreover, we innovatively predicted and validated four core miRNAs (miR-4257, miR-4494, miR-4263, and miR-4298) and constructed a ceRNA network that contained 165 mRNA, eight lncRNAs, and four core miRNAs. Finally, we proposed a potential regulatory mechanism for KC. Overall, we uncovered a hub ceRNA network that might underlie a critical posttranslational regulatory mechanism in KC, in which miR-4257, miR-4494, miR-4263, and miR-4298 could be valuable biomarkers and provided core RNAs therapeutic targets for KC.

HDIContact: a novel predictor of residue-residue contacts on hetero-dimer interfaces via sequential information and transfer learning strategy

Proteins maintain the functional order of cell in life by interacting with other proteins. Determination of protein complex structural information gives biological insights for the research of diseases and drugs. Recently, a breakthrough has been made in protein monomer structure prediction. However, due to the limited number of the known protein structure and homologous sequences of complexes, the prediction of residue-residue contacts on hetero-dimer interfaces is still a challenge. In this study, we have developed a deep learning framework for inferring inter-protein residue contacts from sequential information, called HDIContact. We utilized transfer learning strategy to produce Multiple Sequence Alignment (MSA) two-dimensional (2D) embedding based on patterns of concatenated MSA, which could reduce the influence of noise on MSA caused by mismatched sequences or less homology. For MSA 2D embedding, HDIContact took advantage of Bi-directional Long Short-Term Memory (BiLSTM) with two-channel to capture 2D context of residue pairs. Our comprehensive assessment on the Escherichia coli (E. coli) test dataset showed that HDIContact outperformed other state-of-the-art methods, with top precision of 65.96%, the Area Under the Receiver Operating Characteristic curve (AUROC) of 83.08% and the Area Under the Precision Recall curve (AUPR) of 25.02%. In addition, we analyzed the potential of HDIContact for human-virus protein-protein complexes, by achieving top five precision of 80% on O75475-P04584 related to Human Immunodeficiency Virus. All experiments indicated that our method was a valuable technical tool for predicting inter-protein residue contacts, which would be helpful for understanding protein-protein interaction mechanisms.

Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins

Thermophilic proteins (TPPs) are critical for basic research and in the food industry due to their ability to maintain a thermodynamically stable fold at extremely high temperatures. Thus, the expeditious identification of novel TPPs through computational models from protein sequences is very desirable. Over the last few decades, a number of computational methods, especially machine learning (ML)-based methods, for in silico prediction of TPPs have been developed. Therefore, it is desirable to revisit these methods and summarize their advantages and disadvantages in order to further develop new computational approaches to achieve more accurate and improved prediction of TPPs. With this goal in mind, we comprehensively investigate a large collection of fourteen state-of-the-art TPP predictors in terms of their dataset size, feature encoding schemes, feature selection strategies, ML algorithms, evaluation strategies and web server/software usability. To the best of our knowledge, this article represents the first comprehensive review on the development of ML-based methods for in silico prediction of TPPs. Among these TPP predictors, they can be classified into two groups according to the interpretability of ML algorithms employed (i.e., computational black-box methods and computational white-box methods). In order to perform the comparative analysis, we conducted a comparative study on several currently available TPP predictors based on two benchmark datasets. Finally, we provide future perspectives for the design and development of new computational models for TPP prediction. We hope that this comprehensive review will facilitate researchers in selecting an appropriate TPP predictor that is the most suitable one to deal with their purposes and provide useful perspectives for the development of more effective and accurate TPP predictors.

Homology Modeling, de Novo Design of Ligands, and Molecular Docking Identify Potential Inhibitors of Leishmania donovani 24-Sterol Methyltransferase

The therapeutic challenges pertaining to leishmaniasis due to reported chemoresistance and toxicity necessitate the need to explore novel pathways to identify plausible inhibitory molecules. Leishmania donovani 24-sterol methyltransferase (LdSMT) is vital for the synthesis of ergosterols, the main constituents of Leishmania cellular membranes. So far, mammals have not been shown to possess SMT or ergosterols, making the pathway a prime candidate for drug discovery. The structural model of LdSMT was elucidated using homology modeling to identify potential novel 24-SMT inhibitors via virtual screening, scaffold hopping, and de-novo fragment-based design. Altogether, six potential novel inhibitors were identified with binding energies ranging from −7.0 to −8.4 kcal/mol with e-LEA3D using 22,26-azasterol and S1–S4 obtained from scaffold hopping via the ChEMBL, DrugBank, PubChem, ChemSpider, and ZINC15 databases. These ligands showed comparable binding energy to 22,26-azasterol (−7.6 kcal/mol), the main inhibitor of LdSMT. Moreover, all the compounds had plausible ligand efficiency-dependent lipophilicity (LELP) scores above 3. The binding mechanism identified Tyr92 to be critical for binding, and this was corroborated via molecular dynamics simulations and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) calculations. The ligand A1 was predicted to possess antileishmanial properties with a probability of activity (Pa) of 0.362 and a probability of inactivity (Pi) of 0.066, while A5 and A6 possessed dermatological properties with Pa values of 0.205 and 0.249 and Pi values of 0.162 and 0.120, respectively. Structural similarity search via DrugBank identified vabicaserin, daledalin, zanapezil, imipramine, and cefradine with antileishmanial properties suggesting that the de-novo compounds could be explored as potential antileishmanial agents.

Dual transcriptome based reconstruction of Salmonella-human integrated metabolic network to screen potential drug targets

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a highly adaptive pathogenic bacteria with a serious public health concern due to its increasing resistance to antibiotics. Therefore, identification of novel drug targets for S. Typhimurium is crucial. Here, we first created a pathogen-host integrated genome-scale metabolic network by combining the metabolic models of human and S. Typhimurium, which we further tailored to the pathogenic state by the integration of dual transcriptome data. The integrated metabolic model enabled simultaneous investigation of metabolic alterations in human cells and S. Typhimurium during infection. Then, we used the tailored pathogen-host integrated genome-scale metabolic network to predict essential genes in the pathogen, which are candidate novel drug targets to inhibit infection. Drug target prioritization procedure was applied to these targets, and pabB was chosen as a putative drug target. It has an essential role in 4-aminobenzoic acid (PABA) synthesis, which is an essential biomolecule for many pathogens. A structure based virtual screening was applied through docking simulations to predict candidate compounds that eliminate S. Typhimurium infection by inhibiting pabB. To our knowledge, this is the first comprehensive study for predicting drug targets and drug like molecules by using pathogen-host integrated genome-scale models, dual RNA-seq data and structure-based virtual screening protocols. This framework will be useful in proposing novel drug targets and drugs for antibiotic-resistant pathogens.

Exploring the Mechanism of Ling-Gui-Zhu-Gan Decoction in Ventricular Remodeling after Acute Myocardial Infarction Based on UPLC and In Vivo Experiments

Ventricular remodeling (VR) after acute myocardial infarction (AMI) is an important pathophysiological basis for the development of chronic heart failure (CHF). At present, Ling-Gui-Zhu-Gan decoction (LGZGD) has been widely reported in the clinical treatment and basic research of cardiovascular diseases (CVDs), such as myocardial infarction, heart failure, and angina pectoris. However, the mechanism of LGZGD against VR after AMI remains unclear. Ultra-performance liquid chromatography (UPLC) was applied to investigate the major constituents of LGZGD, and molecular docking was used to predict the targets on the NLRP3/Caspase-1/GSDMD signaling pathway. In vivo, histological changes in the myocardium were visualized using HE staining and Masson staining, and cardiomyocyte apoptosis was detected using TUNEL. IL-1β activity in rat serum was determined by ELISA. Finally, NLRP3, Caspase-1, and GSDMD expressions were analyzed through RT-qPCR and Western blotting. The results showed that 8 authentic reference substances have been detected in LGZGD. Molecular docking showed that the major chemical constituents of LGZGD had a good binding activity with NLRP3, Caspase-1, and GSDMD. Our results showed that LGZGD treatment markedly improved cardiac pathology, decreased cardiomyocyte apoptosis, reduced IL-1β activity, and regulated the expression of genes and proteins related to the NLRP3/Caspase-1/GSDMD signal pathway. These results suggest that LGZGD protects against VR after AMI through NLRP3/Caspase-1/GSDMD signal pathway.

Network Pharmacology and Molecular Docking-Based Strategy to Investigate the Multitarget Mechanisms of Shenqi Yizhi Granule on Alzheimer's Disease

Background

Traditional Chinese herbal medicine draws more attention to explore an effective therapeutic strategy for Alzheimer's disease (AD). Shenqi Yizhi granule (SQYG), a Chinese herbal recipe, has been applied to ameliorate cognitive impairment in mild-to-moderate AD patients. However, the overall molecular mechanism of SQYG in treating AD has not been clarified.

Objective

This study aimed to investigate the molecular mechanism of SQYG on AD using an integration strategy of network pharmacology and molecular docking.

Methods

The active compounds of SQYG and common targets between SQYG and AD were screened from databases. The herb-compound network, compound-target network, and protein-protein interaction network were constructed. The enrichment analysis of common targets and molecular docking were performed.

Results

816 compounds and 307 common targets between SQYG and AD were screened. KEGG analysis revealed that common targets were mainly enriched in lipid metabolism, metal ion metabolism, IL-17 signaling pathway, GABA receptor signaling, and neuroactive ligand-receptor interaction. Molecular docking analysis showed high binding affinity between ginsenoside Rg1 and Aβ _1-42, tanshinone IIA and BACE1, baicalin, and AchE.

Conclusions

The therapeutic mechanisms of SQYG on AD were associated with regulating lipid metabolism, metal ion metabolism, IL-17 signaling pathway, and GABA receptor signaling. Ginsenoside Rg1, tanshinone IIA, baicalin, astragaloside IV, and folic acid may play an important role in AD treatment.

Wangzaozin A, a potent novel microtubule stabilizer, targets both the taxane and laulimalide sites on β-tubulin through molecular dynamics simulations

Wangzaozin A, an ent-kaurene diterpenoid isolated from Isodon racemosa (Hemsl) Hara, promotes the polymerization of intracellular microtubules as well as purified tubulin, which is similar to other known microtubule stabilizers. Our pharmacological results showed that wangzaozin A induced G2/M cell cycle arrest and the significant inhibition of cancer cell proliferation. A molecular docking study indicated that wangzaozin A could bind to both the taxane and laulimalide (lau) sites on β-tubulin, which is a novel binding mode that differs from that of known microtubule stabilizers. Furthermore, molecular dynamics simulation and binding free energy calculations demonstrated that wangzaozin A could stably bind to taxane and lau sites simultaneously and form a double-bonded complex. The binding mode of wangzaozin A to the taxane site was more similar to that of epothilone A than paclitaxel. Our results demonstrate that wangzaozin A represents a novel class of microtubule stabilizers, and may serve as a potential microtubule-targeting lead compound for further structural optimization.

Membranome 3.0: Database of single-pass membrane proteins with AlphaFold models

The Membranome database provides comprehensive structural information on single‐pass (i.e., bitopic) membrane proteins from six evolutionarily distant organisms, including protein–protein interactions, complexes, mutations, experimental structures, and models of transmembrane α‐helical dimers. We present a new version of this database, Membranome 3.0, which was significantly updated by revising the set of 5,758 bitopic proteins and incorporating models generated by AlphaFold 2 in the database. The AlphaFold models were parsed into structural domains located at the different membrane sides, modified to exclude low‐confidence unstructured terminal regions and signal sequences, validated through comparison with available experimental structures, and positioned with respect to membrane boundaries. Membranome 3.0 was re‐developed to facilitate visualization and comparative analysis of multiple 3D structures of proteins that belong to a specified family, complex, biological pathway, or membrane type. New tools for advanced search and analysis of proteins, their interactions, complexes, and mutations were included. The database is freely accessible at https://membranome.org.

HORDB a comprehensive database of peptide hormones

Peptide hormones (also known as hormone peptides and polypeptide hormones) are hormones composed of peptides and are signal transduction molecules produced by a class of multicellular organisms. It plays an important role in the physiological and behavioral regulation of animals and humans as well as in the growth of plants. In order to promote the research on peptide hormones, we constructed HORDB database. The database currently has a total of 6024 entries, including 5729 peptide hormones, 40 peptide drugs and 255 marketed pharmaceutical preparations information. Each entry provided comprehensive information related to the peptide, including general information, sequence, activity, structure, physical information and literature information. We also added information on IC₅₀, EC₅₀, ED₅₀, target, and whether or not the blood-brain barrier was crossed to the activity information note. In addition, HORDB integrates search and sequence analysis to facilitate user browsing and data analysis. We believe that the peptide hormones information collected by HORDB will promote the design and discovery of peptide hormones, All data are hosted and available in figshare 10.6084/m9.figshare.c.5522241.

Measurement(s)	peptide hormone
Technology Type(s)	Comprehensive data website service
Factor Type(s)	peptide hormone

Novel adamantyl clubbed iminothiazolidinones as promising elastase inhibitors: design, synthesis, molecular docking, ADMET and DFT studies

Porcine Pancreatic Elastase (PPE) is a serine protease that is homologous to trypsin and chymotrypsin that are involved in various pathologies like inflammatory disease, Chronic Obstructive Pulmonary Disease (COPD), acute respiratory distress syndrome, cystic fibrosis, and atherosclerosis. PPE if remained uninhibited would lead to digestion of important connective tissue. We developed new structurally diverse series of adamantyl-iminothiazolidinone hybrids to divulge elastase inhibition assay. To identify potent derivatives, in silico screening was conducted and in vitro studies disclosed that the compounds 5a, 5f, 5g, and 5h showed excellent binding energies and low IC₅₀ values. In silico studies including molecular docking, DFT studies (using the B3LYP/SVP basis set in the gas phase) drug likeness scores and molecular dynamic simulation studies were conducted to evaluate protein–ligand interactions and to determine the stability of top ranked conformation. In silico studies further supported the results of in vitro experiments and suggest these derivatives as novel inhibitors of elastase enzyme.

Structurally diverse adamantyl-iminothiazolidinone conjugates were synthesized, evaluated for elastase inhibition, and subjected to in silico ADMET prediction. The inhibition studies revealed compounds 5a, 5f, 5g, and 5h to show significant activity.

A Network Pharmacology Approach to Reveal the Underlying Mechanisms of Rhizoma Dioscoreae Nipponicae in the Treatment of Asthma

Background

In this study, network pharmacological methods were used to analyze the targets of Rhizoma Dioscoreae Nipponicae (RDN) and investigate the potential underlying mechanism of RDN in the treatment of asthma.

Methods

Asthma-related targets were obtained from the GeneCards and DisGeNET databases. The bioactive components of RDN were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database, and the targets of these compounds were predicted using the BATMAN-TCM database. The network of RDN component targets was constructed using Cytoscape. A protein-protein interaction (PPI) network was constructed in Cytoscape to determine the potential targets of RDN for the treatment of asthma. The hub genes of RDN in the treatment of asthma were screened using network topological parameters. Gene ontology (GO) and the KEGG pathways were analyzed. Molecular docking and in vivo experiments were performed to validate the network pharmacology results.

Results

A total of four bioactive components and 55 targets were identified. The results of the enrichment analysis suggested that the treatment of asthma with RDN involved signaling pathways, such as those related to systemic lupus erythematosus, alcoholism, viral carcinogenesis, the cell cycle, prostate cancer, transcriptional misregulation in cancer, hepatitis B, thyroid hormone signaling, and PI3K-AKT signaling, as well as other signaling pathways. Molecular docking showed that the active components of RDN could stably bind to the predicted target. In vivo experiments showed that RDN could regulate the expression of target genes and inhibit the activation of the PI3K-AKT signaling pathway.

Conclusion

To a certain extent, this study reveals the potential bioactive components and molecular mechanisms of RDN in the treatment of asthma and provides new insights for the development of new drugs for asthma.

The Mechanism of Ginseng and Astragalus Decoction in the Treatment of Malignant Pleural Effusion Based on Network Pharmacology and Molecular Docking Technology

Introduction

The objective of our study is to explore the potential active ingredients and activity of Ginseng and Astragalus decoction (GAD) in the treatment of malignant pleural effusion (MPE) by using network pharmacology and molecular docking technologies.

Methods

The active ingredients and corresponding targets of Ginseng and Astragalus were extracted from the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform. The relevant targets of malignant pleural effusion (MPE) were searched in the disease databases. Overlapping targets of Ginseng and Astragalus and the corresponding targets of MPE were obtained to define the effective target of GAD for the treatment of MPE. The STRING database was applied to construct a predicted protein-protein interaction network for intersected targets. The Cytoscape software was used to screen key targets with a therapeutic potential. Using the Metascape database, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis on the targets identified in the study. PyMOL and AutoDock Vina were used to molecularly dock the selected key components to their respective key targets for MPE treatment.

Results

The core target network revealed 22 main active ingredients, 26 main targets, and 16 signaling pathways in GAD. Molecular docking revealed 6 targets (AKT serine/threonine kinase 1, intercellular adhesion molecule, Jun proto-oncogene, peroxisome proliferator activated receptor gamma, prostaglandin-endoperoxide synthase 2, and tumor necrosis factor) that could partially dock with kaempferol, frutinone A, ginsenoside RH2, formononetin, and quercetin.

Conclusions

Several components, targets, and signaling pathways of GAD contribute to the treatment of MPE, which suggests a rationale for further investigation on GAD's active molecule and mechanism of action in the clinical application of MPE.

Natural alkaloids targeting EGFR in non-small cell lung cancer: Molecular docking and ADMET predictions

The phytochemicals contribute to the processes of protection and interaction by acting as antioxidants, anti-mutagens, anticarcinogens, and antimicrobial agents. Among the diverse families of phytoconstituents, alkaloids play an essential role in medicine. These are low-molecular-mass compounds containing nitrogen and are generally alkaline. In this study, in silico molecular docking was performed using AutoDock Vina for thirty-one alkaloids against epidermal growth factor receptor (EGFR). Erlotinib was used as a reference ligand for this study. Erlotinib has been linked to various serious side effects over the past decade, including folliculitis, diarrhoea, paronychia, fatigue, conjunctivitis, ectopion, and epiphora of the lower eyelids. This study found sanguinarine (-10.7 kcal mol^-1) to be the most potent inhibitor of EGFR as compared to erlotinib (-7.5 kcal mol^-1). Other alkaloids namely, isocolumbin (-9.3 kcal mol^-1), lunamarine (-9.1 kcal mol^-1), ajmaline (-8.6 kcal mol^-1), magnoflorine (-8.6 kcal mol^-1) and jatrorrhizine (-8.5 kcal mol^-1) also showed potent inhibition against EGFR, but the stability of these molecules with EGFR was less than sanguinarine and more than erlotinib. These were stable and ideal pharmaceutical alkaloids because of their significant interactions, minimal Gibbs free energy, safety, effectiveness and selectivity. Amongst the 31 alkaloids subjected to ADMET prediction, 29 alkaloids followed Lipinski's rule of five. These 29 alkaloids were predicted to have high bioavailability, high lead-likeness score, low toxicity and were easier to synthesize. Compared to erlotinib, other molecules showed less or no inhibition of EGFR. The six named compounds listed above may be potent inhibitors for EGFR mutated cancers, as for example non-small cell lung cancer, colorectal cancer, and pancreatic cancer.

Adipose-derived mesenchymal stem cells may reduce intestinal epithelial damage in ulcerative colitis by communicating with macrophages and blocking inflammatory pathways: an analysis in silico

Ulcerative colitis is a chronic, non-specific inflammatory disease that affects mainly the colonic mucosa and submucosa. The pathogenesis of ulcerative colitis is unclear, which limits the development of effective treatments. In this study, single-cell sequencing data from 18 ulcerative colitis samples and 12 healthy controls were downloaded from the Single Cell Portal database, cell types were defined through cluster analysis, and genes in each cluster that were differentially expressed in ulcerative colitis were identified. These genes were enriched in functional pathways related to apoptosis, immunity and inflammation. Analysis using iTALK software suggested extensive communication among immune cells. Single-cell sequencing data from adipose-derived mesenchymal stem cells from three healthy female donors were obtained from the Sequence Read Archive database. The SingleR package was used to identify different cell types, for each of which a stemness score was calculated. Pseudotime analysis was performed to infer the trajectory of cells. SCENIC software was used to identify the gene regulatory network in adipose-derived mesenchymal stem cells, and iTALK software was performed to explore the relationship among macrophages, adipose-derived mesenchymal stem cells and enterocytes. Molecular docking confirmed the possibility of cell-cell interactions via binding between surface receptors and their ligands. The bulk data were downloaded and analyzed to validate the expression of genes. Our bioinformatics analyses suggest that ulcerative colitis involves communication between macrophages and enterocytes via ligand-receptor pairs. Our results further suggest that adipose-derived mesenchymal stem cells may alleviate ulcerative colitis by communicating with macrophages to block inflammation.

Molecular Mechanism Investigation on Monomer Kaempferol of the Traditional Medicine Dingqing Tablet in Promoting Apoptosis of Acute Myeloid Leukemia HL-60 Cells

The traditional medicine Dingqing Tablet produces effective efficacy in treating acute myeloid leukemia, but its specific mechanism remains to be investigated. Dingqing Tablet consists of Codonopsis, Indigo Naturalis, Cortex Moutan, Radix Notoginseng, Citrus Reticulata, and Eolite. The active components of Dingqing Tablets were screened by the TCMSP database. Meanwhile, the SwissTargetPrediction database was utilized to predict the corresponding targets. Relevant disease targets of acute myeloid leukemia were obtained from GeneCards. The obtained targets of Dingqing Tablets and genes of acute myeloid leukemia were used, and the overlapped genes were presented in the Venn diagram. A drug-component-target network was constructed via Cytoscape 3.6.0 software. Molecular docking methodology was also used with AutoDock Vina 1.1.2. Furthermore, the effects of kaempferol on the proliferation and apoptosis of HL-60 cells were identified using 3-(4,5)-dimethylthiahiazo(-z-y1)-3,5-di-phenytetrazoliumromide (MTT), 5-Ethynyl-2′-deoxyuridine (EDU), flow cytometry, and TdT-mediated dUTP nick-end labeling (TUNEL) assays. The combination of kaempferol and AKT1 was verified using an immunoprecipitation (IP) experiment and the effects of Kaempferol on HL-60 cell apoptosis by western blot (WB) and qPCR. The key component kaempferol and the core target gene AKT1 were sorted out using a drug-component target network diagram. Molecular docking results revealed that the binding energy between kaempferol and AKT1 was lower than -5 kcal/mol. MTT and EDU assays indicated that kaempferol markedly inhibited the proliferation of HL-60 cells. Flow cytometry and TUNEL assays suggested that kaempferol substantially promoted HL-60 cell apoptosis. IP assay results testified that kaempferol could bind to AKT1, thereby reducing the level of P-AKT and promoting HL-60 cell apoptosis. The monomer kaempferol of Dingqing Tablet could promote apoptosis of HL-60 cells, and the mechanism might correlate with the combination of kaempferol and AKT1, reducing the level of P-AKT and promoting the expression of the apoptotic signaling pathway.

Identification of the potential mechanism of Radix pueraria in colon cancer based on network pharmacology

Radix Puerariae (RP), a dry root of Pueraria lobata (Willd.) Ohwi, is used to treat a variety of diseases, including cancer. Several in vitro and in vivo studies have demonstrated the efficacy of RP in the treatment of colon cancer (CC). However, the biological mechanism of RP in the treatment of colon cancer remains unclear. In this study, the active component of RP and its potential molecular mechanism against CC were studied by network pharmacology and enrichment analysis. The methods adopted included screening active ingredients of Chinese medicine, predicting target genes of Chinese medicine and disease, constructing of a protein interaction network, and conducting GO and KEGG enrichment analysis. Finally, the results of network pharmacology were further validated by molecular docking experiments and cell experiments. Eight active constituents and 14 potential protein targets were screened from RP, including EGFR, JAK2 and SRC. The biological mechanism of RP against CC was analysed by studying the relationship between active components, targets, and enrichment pathways. These findings provide a basis for understanding the clinical application of RP in CC.

Effects of Koumiss on Intestinal Immune Modulation in Immunosuppressed Rats

Koumiss is a traditional fermented dairy product with health and medicinal benefits. It is very popular in the Inner Mongolia Autonomous Region of China. The results of relevant studies have shown that koumiss can regulate the gastrointestinal environment, improve the absorption of nutrients, improve the body's intolerance to lactose, enhance the body's immunity, prevent scurvy and atherosclerosis, and aid in the treatment of tuberculosis. However, there are no systematic reports on the effects of koumiss on immunity. In this study, we aimed to decipher the effects of koumiss on intestinal immune modulation. We used liquid chromatography-tandem mass spectrometry (LC-MS) analysis to determine the composition of Koumiss. Using Compound Discoverer software, we compared the mass spectrometry data with the compound information in the online databases ChemSpider and mzCloud to intelligently identify the main chemical components of koumiss. Additionally, we used Mass Frontier small molecule fragmentation library^TM to determine the structure of fragment ions. A total of 21 components were identified, which clarified the chemical basis of koumiss. These 21 compounds were then used to perform molecular docking with immune-related targets, such as TNF, IL2, IL10, etc. The results indicated good docking activity between most of the compounds and the targets. Then, an immunosuppressive rat model was used to determine the therapeutic effect of koumiss. The results of this study showed that koumiss could, to a certain extent, correct the atrophy of the thymus and spleen in immunosuppressed model rats. The number of leukocytes, lymphocytes, and the CD4⁺/CD8⁺ ratio of peripheral blood lymphocytes was also increased. In addition, it could effectively improve the structure of the small intestinal mucosa, which shows that koumiss has a positive effect on the intestinal immune function of immunosuppressed rats. These findings provide an experimental basis for the development and utilization of koumiss as a therapeutic product.

Hepatoprotective mechanism of Silybum marianum on nonalcoholic fatty liver disease based on network pharmacology and experimental verification

The study aimed to identify the key active components in Silybum marianum (S. marianum) and determine how they protect against nonalcoholic fatty liver disease (NAFLD). TCMSP, DisGeNET, UniProt databases, and Venny 2.1 software were used to identify 11 primary active components, 92 candidate gene targets, and 30 core hepatoprotective gene targets in this investigation, respectively. The PPI network was built using a string database and Cytoscape 3.7.2. The KEGG pathway and GO biological process enrichment, biological annotation, as well as the identified hepatoprotective core gene targets were analyzed using the Metascape database. The effect of silymarin on NAFLD was determined using H&E on pathological alterations in liver tissues. The levels of liver function were assessed using biochemical tests. Western blot experiments were used to observe the proteins that were expressed in the associated signaling pathways on the hepatoprotective effect, which the previous network pharmacology predicted. According to the KEGG enrichment study, there are 35 hepatoprotective signaling pathways. GO enrichment analysis revealed that 61 biological processes related to the hepatoprotective effect of S. marianum were identified, which mainly involved in response to regulation of biological process and immune system process. Silymarin was the major ingredient derived from S. marianum, which exhibited the hepatoprotective effect by reducing the levels of ALT, AST, TC, TG, HDL-C, LDL-C, decreasing protein expressions of IL-6, MAPK1, Caspase 3, p53, VEGFA, increasing protein expression of AKT1. The present study provided new sights and a possible explanation for the molecular mechanisms of S. marianum against NAFLD.

Discovery and Characterization of the Naturally Occurring Inhibitors Against Human Pancreatic Lipase in Ampelopsis grossedentata

Pancreatic lipase (PL) inhibitor therapy has been validated as an efficacious way for preventing and treating obesity and overweight. In the past few decades, porcine PL (pPL) is widely used as the enzyme source for screening the PL inhibitors, which generates a wide range of pPL inhibitors. By contrast, the efficacious inhibitors against human PL (hPL) are rarely reported. This study aims to discover the naturally occurring hPL inhibitors from edible herbal medicines (HMs) and to characterize the inhibitory mechanisms of the newly identified hPL inhibitors. Following the screening of the inhibition potentials of more than 100 HMs against hPL, Ampelopsis grossedentata extract (AGE) displayed the most potent hPL inhibition activity. After that, the major constituents in AGE were identified and purified, while their anti-hPL effects were assayed in vitro. The results clearly showed that two abundant constituents in AGE (dihydromyricetin and iso-dihydromyricetin) were moderate hPL inhibitors, while myricetin and quercetin were strong hPL inhibitors [half-maximal inhibitory concentration (IC50) values were around 1.5 μM]. Inhibition kinetic analyses demonstrated that myricetin and quercetin potently inhibited hPL-catalyzed near-infrared fluorogenic substrate of human pancreatic lipase (DDAO-ol) hydrolysis in a non-competitive inhibition manner, with Ki values of 2.04 and 2.33 μM, respectively. Molecular dynamics simulations indicated that myricetin and quercetin could stably bind on an allosteric site of hPL. Collectively, this study reveals the key anti-obesity constituents in AGE and elucidates their inhibitory mechanisms against hPL, which offers convincing evidence to support the anti-obesity and lipid-lowering effects of this edible herb.

Network pharmacology analysis and molecular docking to unveil the potential mechanisms of San-Huang-Chai-Zhu formula treating cholestasis

Objective

Chinese medicine formulae possess the potential for cholestasis treatment. This study aimed to explore the underlying mechanisms of San-Huang-Chai-Zhu formula (SHCZF) against cholestasis.

Methods

The major chemical compounds of SHCZF were identified by high-performance liquid chromatography. The bioactive compounds and targets of SHCZF, and cholestasis-related targets were obtained from public databases. Intersected targets of SHCZF and cholestasis were visualized by Venn diagram. The protein-protein interaction and compound-target networks were established by Cytoscape according to the STRING database. The biological functions and pathways of potential targets were characterized by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. The biological process-target-pathway network was constructed by Cytoscape. Finally, the interactions between biological compounds and hub target proteins were validated via molecular docking.

Results

There 7 major chemical compounds in SHCZF. A total of 141 bioactive compounds and 83 potential targets were screened for SHCZF against cholestasis. The process of SHCZF against cholestasis was mainly involved in AGE-RAGE signaling pathway in diabetic complications, fluid shear stress and atherosclerosis, and drug metabolism-cytochrome P450. ALB, IL6, AKT1, TP53, TNF, MAPK3, APOE, IL1B, PPARG, and PPARA were the top 10 hub targets. Molecular docking showed that bioactive compounds of SHCZF had a good binding affinity with hub targets.

Conclusions

This study predicted that the mechanisms of SHCZF against cholestasis mainly involved in AGE-RAGE signaling pathway in diabetic complications, fluid shear stress and atherosclerosis, and drug metabolism-cytochrome P450. Moreover, APOE, AKT1, and TP53 were the critical hub targets for bioactive compounds of SHCZF.

In Silico Screening of Bioactive Compounds of Representative Seaweeds to Inhibit SARS-CoV-2 ACE2-Bound Omicron B.1.1.529 Spike Protein Trimer

Omicron is an emerging SARS-CoV-2 variant, evolved from the Indian delta variant B.1.617.2, which is currently infecting worldwide. The spike glycoprotein, an important molecule in the pathogenesis and transmissions of SARS-CoV-2 variants, especially omicron B.1.1.529, shows 37 mutations distributed over the trimeric protein domains. Notably, fifteen of these mutations reside in the receptor-binding domain of the spike glycoprotein, which may alter transmissibility and infectivity. Additionally, the omicron spike evades neutralization more efficiently than the delta spike. Most of the therapeutic antibodies are ineffective against the omicron variant, and double immunization with BioNTech-Pfizer (BNT162b2) might not adequately protect against severe disease induced by omicron B.1.1.529. So far, no efficient antiviral drugs are available against omicron. The present study identified the promising inhibitors from seaweed’s bioactive compounds to inhibit the omicron variant B.1.1.529. We have also compared the seaweed’s compounds with the standard drugs ceftriaxone and cefuroxime, which were suggested as beneficial antiviral drugs in COVID-19 treatment. Our molecular docking analysis revealed that caffeic acid hexoside (−6.4 kcal/mol; RMSD = 2.382 Å) and phloretin (−6.3 kcal/mol; RMSD = 0.061 Å) from Sargassum wightii (S. wightii) showed the inhibitory effect against the crucial residues ASN417, SER496, TYR501, and HIS505, which are supported for the inviolable omicron and angiotensin-converting enzyme II (ACE2) receptor interaction. Cholestan-3-ol, 2-methylene-, (3beta, 5 alpha) (CMBA) (−6.0 kcal/mol; RMSD = 3.074 Å) from Corallina officinalis (C. officinalis) manifested the strong inhibitory effect against the omicron RBD mutated residues LEU452 and ALA484, was magnificently observed as the essential residues in Indian delta variant B.1.617.2 previously. The standard drugs (ceftriaxone and cefuroxime) showed no or less inhibitory effect against RBD of omicron B.1.1.529. The present study also emphasized the pharmacological properties of the considered chemical compounds. The results could be used to develop potent seaweed-based antiviral drugs and/or dietary supplements to treat omicron B.1.1529-infected patients.

Classification Model for the Second Extracellular Loop of Class A GPCRs

The extracellular loop 2 (ECL2) is the longest and the most diverse loop among class A G protein-coupled receptors (GPCRs). It connects the transmembrane (TM) helices 4 and 5 and contains a highly conserved cysteine through which it is bridged with TM3. In this paper, experimental ECL2 structures were analyzed based on their sequences, shapes, and intramolecular contacts. To take into account the flexibility, we incorporated into our analyses information from the molecular dynamics trajectories available on the GPCRmd website. Despite the high sequence variability, shapes of the analyzed structures, defined by the backbone volume overlaps, can be clustered into seven main groups. Conformational differences within the clusters can be then identified by intramolecular interactions with other GPCR structural domains. Overall, our work provides a reorganization of the structural information of the ECL2 of class A GPCR subfamilies, highlighting differences and similarities on sequence and conformation levels.

Prediction and Modeling of Protein–Protein Interactions Using “Spotted” Peptides with a Template-Based Approach

Protein–peptide interactions (PpIs) are a subset of the overall protein–protein interaction (PPI) network in the living cell and are pivotal for the majority of cell processes and functions. High-throughput methods to detect PpIs and PPIs usually require time and costs that are not always affordable. Therefore, reliable in silico predictions represent a valid and effective alternative. In this work, a new algorithm is described, implemented in a freely available tool, i.e., “PepThreader”, to carry out PPIs and PpIs prediction and analysis. PepThreader threads multiple fragments derived from a full-length protein sequence (or from a peptide library) onto a second template peptide, in complex with a protein target, “spotting” the potential binding peptides and ranking them according to a sequence-based and structure-based threading score. The threading algorithm first makes use of a scoring function that is based on peptides sequence similarity. Then, a rerank of the initial hits is performed, according to structure-based scoring functions. PepThreader has been benchmarked on a dataset of 292 protein–peptide complexes that were collected from existing databases of experimentally determined protein–peptide interactions. An accuracy of 80%, when considering the top predicted 25 hits, was achieved, which performs in a comparable way with the other state-of-art tools in PPIs and PpIs modeling. Nonetheless, PepThreader is unique in that it is able at the same time to spot a binding peptide within a full-length sequence involved in PPI and model its structure within the receptor. Therefore, PepThreader adds to the already-available tools supporting the experimental PPIs and PpIs identification and characterization.

Merging NMR Data and Computation Facilitates Data-Centered Research

The Biological Magnetic Resonance Data Bank (BMRB) has served the NMR structural biology community for 40 years, and has been instrumental in the development of many widely-used tools. It fosters the reuse of data resources in structural biology by embodying the FAIR data principles (Findable, Accessible, Inter-operable, and Re-usable). NMRbox is less than a decade old, but complements BMRB by providing NMR software and high-performance computing resources, facilitating the reuse of software resources. BMRB and NMRbox both facilitate reproducible research. NMRbox also fosters the development and deployment of complex meta-software. Combining BMRB and NMRbox helps speed and simplify workflows that utilize BMRB, and enables facile federation of BMRB with other data repositories. Utilization of BMRB and NMRbox in tandem will enable additional advances, such as machine learning, that are poised to become increasingly powerful.

Structural Prediction of Peptide-MHC Binding Modes

The immune system is constantly protecting its host from the invasion of pathogens and the development of cancer cells. The specific CD8⁺ T-cell immune response against virus-infected cells and tumor cells is based on the T-cell receptor recognition of antigenic peptides bound to class I major histocompatibility complexes (MHC) at the surface of antigen presenting cells. Consequently, the peptide binding specificities of the highly polymorphic MHC have important implications for the design of vaccines, for the treatment of autoimmune diseases, and for personalized cancer immunotherapy. Evidence-based machine-learning approaches have been successfully used for the prediction of peptide binders and are currently being developed for the prediction of peptide immunogenicity. However, understanding and modeling the structural details of peptide/MHC binding is crucial for a better understanding of the molecular mechanisms triggering the immunological processes, estimating peptide/MHC affinity using universal physics-based approaches, and driving the design of novel peptide ligands. Unfortunately, due to the large diversity of MHC allotypes and possible peptides, the growing number of 3D structures of peptide/MHC (pMHC) complexes in the Protein Data Bank only covers a small fraction of the possibilities. Consequently, there is a growing need for rapid and efficient approaches to predict 3D structures of pMHC complexes. Here, we review the key characteristics of the 3D structure of pMHC complexes before listing databases and other sources of information on pMHC structures and MHC specificities. Finally, we discuss some of the most prominent pMHC docking software.

OUP accepted manuscript

Motivation

Cryo-electron microscopy (cryo-EM) is a widely used technology for ultrastructure determination, which constructs the 3D structures of protein and macromolecular complex from a set of 2D micrographs. However, limited by the electron beam dose, the micrographs in cryo-EM generally suffer from the extremely low signal-to-noise ratio (SNR), which hampers the efficiency and effectiveness of downstream analysis. Especially, the noise in cryo-EM is not simple additive or multiplicative noise whose statistical characteristics are quite different from the ones in natural image, extremely shackling the performance of conventional denoising methods.

Results

Here, we introduce the Noise-Transfer2Clean (NT2C), a denoising deep neural network (DNN) for cryo-EM to enhance image contrast and restore specimen signal, whose main idea is to improve the denoising performance by correctly learning the noise distribution of cryo-EM images and transferring the statistical nature of noise into the denoiser. Especially, to cope with the complex noise model in cryo-EM, we design a contrast-guided noise and signal re-weighted algorithm to achieve clean-noisy data synthesis and data augmentation, making our method authentically achieve signal restoration based on noise’s true properties. Our work verifies the feasibility of denoising based on mining the complex cryo-EM noise patterns directly from the noise patches. Comprehensive experimental results on simulated datasets and real datasets show that NT2C achieved a notable improvement in image denoising, especially in background noise removal, compared with the commonly used methods. Moreover, a case study on the real dataset demonstrates that NT2C can greatly alleviate the obstacles caused by the SNR to particle picking and simplify the identifying of particles.

Availabilityand implementation

The code is available at https://github.com/Lihongjia-ict/NoiseTransfer2Clean/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Network Pharmacological Study on Mechanism of the Therapeutic Effect of Modified Duhuo Jisheng Decoction in Osteoporosis

BACKGROUND

Modified Duhuo Jisheng Decoction (MDHJSD) is a traditional Chinese medicine prescription for the treatment of osteoporosis (OP), but its mechanism of action has not yet been clarified. This study aims to explore the mechanism of MDHJSD in OP through a combination of network pharmacology analysis and experimental verification.

METHODS

The active ingredients and corresponding targets of MDHJSD were acquired from the Traditional Chinese Medicine System Pharmacology (TCMSP) database. OP-related targets were acquired from databases, including Genecards, OMIM, Drugbank, CTD, and PGKB. The key compounds, core targets, major biological processes, and signaling pathways of MDHJSD that improve OP were identified by constructing and analysing the relevant networks. The binding affinities between key compounds and core targets were verified using AutoDock Vina software. A rat model of ovariectomized OP was used for the experimental verification.

RESULTS

A total of 100 chemical constituents, 277 targets, and 4734 OP-related targets of MDHJSD were obtained. Subsequently, five core components and eight core targets were identified in the analysis. Pathway enrichment analysis revealed that overlapping targets were significantly enriched in the tumour necrosis factor-alpha (TNF-α) signaling pathway, an inflammation signaling pathway, which contained six of the eight core targets, including TNF-α, interleukin 6 (IL-6), transcription factor AP-1, mitogen-activated protein kinase 3, RAC-alpha serine/threonine-protein kinase, and caspase-3 (CASP3). Molecular docking analysis revealed close binding of the six core targets of the TNF signaling pathway to the core components. The results of experimental study show that MDHJSD can protect bone loss, inhibit the inflammatory response, and downregulate the expression levels of TNF-α, IL-6, and CASP3 in ovariectomized rats.

CONCLUSION

The mechanism of MDHJSD in the treatment of OP may be related to the regulation of the inflammatory response in the bone tissue.

OUP accepted manuscript

Biogenesis of spliceosomal small nuclear ribonucleoproteins (snRNPs) and their recycling after splicing require numerous assembly/recycling factors whose modes of action are often poorly understood. The intrinsically disordered TSSC4 protein has been identified as a nuclear-localized U5 snRNP and U4/U6-U5 tri-snRNP assembly/recycling factor, but how TSSC4’s intrinsic disorder supports TSSC4 functions remains unknown. Using diverse interaction assays and cryogenic electron microscopy-based structural analysis, we show that TSSC4 employs four conserved, non-contiguous regions to bind the PRPF8 Jab1/MPN domain and the SNRNP200 helicase at functionally important sites. It thereby inhibits SNRNP200 helicase activity, spatially aligns the proteins, coordinates formation of a U5 sub-module and transiently blocks premature interaction of SNRNP200 with at least three other spliceosomal factors. Guided by the structure, we designed a TSSC4 variant that lacks stable binding to the PRPF8 Jab1/MPN domain or SNRNP200 in vitro. Comparative immunoprecipitation/mass spectrometry from HEK293 nuclear extract revealed distinct interaction profiles of wild type TSSC4 and the variant deficient in PRPF8/SNRNP200 binding with snRNP proteins, other spliceosomal proteins as well as snRNP assembly/recycling factors and chaperones. Our findings elucidate molecular strategies employed by an intrinsically disordered protein to promote snRNP assembly, and suggest multiple TSSC4-dependent stages during snRNP assembly/recycling.

Computational design of a cutinase for plastic biodegradation by mining molecular dynamics simulations trajectories

Polyethylene terephthalate (PET) has caused serious environmental concerns but could be degraded at high temperature. Previous studies show that cutinase from Thermobifida fusca KW3 (TfCut2) is capable of degrading and upcycling PET but is limited by its thermal stability. Nowadays, Popular protein stability modification methods rely mostly on the crystal structures, but ignore the fact that the actual conformation of protein is complex and constantly changing. To solve these problems, we developed a computational approach to design variants with enhanced protein thermal stability by mining Molecular Dynamics simulation trajectories using Machine Learning methods (MDL). The optimal classification accuracy and the optimal Pearson correlation coefficient of MDL model were 0.780 and 0.716, respectively. And we successfully designed variants with high ΔT_m values using MDL method. The optimal variant S121P/D174S/D204P had the highest ΔT_m value of 9.3 °C, and the PET degradation ratio increased by 46.42-fold at 70℃, compared with that of wild type TfCut2. These results deepen our understanding on the complex conformations of proteins and may enhance the plastic recycling and sustainability at glass transition temperature.

Spatial arrangement of proteins in planar and curved membranes by PPM 3.0

Cellular protrusions, invaginations, and many intracellular organelles have strongly curved membrane regions. Transmembrane and peripheral membrane proteins that induce, sense, or stabilize such regions cannot be properly fitted into a single flat bilayer. To treat such proteins, we developed a new method and a web tool, PPM 3.0, for positioning proteins in curved or planar, single or multiple membranes. This method determines the energetically optimal spatial position, the hydrophobic thickness, and the radius of intrinsic curvature of a membrane-deforming protein structure by arranging it in a single or several sphere-shaped or planar membrane sections. In addition, it can define the lipid-embedded regions of a protein that simultaneously spans several membranes or determine the optimal position of a peptide in a spherical micelle. The PPM 3.0 web server operates with 17 types of biological membranes and 4 types of artificial bilayers. It is publicly available at https://opm.phar.umich.edu/ppm_server3. PPM 3.0 was applied to identify and characterize arrangements in membranes of 128 proteins with a significant intrinsic curvature, such as BAR domains, annexins, Piezo, and MscS mechanosensitive channels, cation-chloride cotransporters, as well as mitochondrial ATP synthases, calcium uniporters, and TOM complexes. These proteins form large complexes that are mainly localized in mitochondria, plasma membranes, and endosomes. Structures of bacterial drug efflux pumps, AcrAB-TolC, MexAB-OrpM, and MacAB-TolC, were positioned in both membranes of the bacterial cell envelop, while structures of multimeric gap-junction channels were arranged in two opposed cellular membranes.

Structure-Based Virtual Screening of Benzaldehyde Thiosemicarbazone Derivatives against DNA Gyrase B of Mycobacterium tuberculosis

Emergence of antibiotic-resistant Mycobacterium tuberculosis (M. tuberculosis) restricts the availability of drugs for the treatment of tuberculosis, which leads to the increased morbidity and mortality of the disease worldwide. There are many intrinsic and extrinsic factors that have been reported for the resistance mechanism. To overcome such mechanisms, chemically synthesized benzaldehyde thiosemicarbazone derivatives were screened against M. tuberculosis to find potential inhibitor for tuberculosis. Such filtering process resulted in compound 13, compound 21, and compound 20 as the best binding energy compounds against DNA gyrase B, an important protein in the replication process. The ADMET prediction has shown the oral bioavailability of the novel compounds.

A computational biology approach for the identification of potential SARS-CoV-2 main protease inhibitors from natural essential oil compounds

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has fomented a climate of fear worldwide due to its rapidly spreading nature, and high mortality rate. The World Health Organization (WHO) declared it as a global pandemic on 11th March, 2020. Many endeavors have been made to find appropriate medications to restrain the SARS CoV-2 infection from spreading but there is no specific antiviral therapy to date. However, a computer-aided drug design approach can be an alternative to identify probable drug candidates within a short time. SARS-CoV-2 main protease is a proven drug target, and it plays a pivotal role in viral replication and transcription. Methods: In this study, we identified a total of 114 essential oil compounds as a feasible anti-SARS-CoV-2 agent from several online reservoirs. These compounds were screened by incorporating ADMET profiling, molecular docking, and 50 ns of molecular dynamics simulation to identify potential drug candidates against the SARS-CoV-2 main protease. The crystallized SARS-CoV-2 main protease structure was collected from the RCSB PDB database (PDB ID 6LU7). Results: According to the results of the ADMET study, none of the compounds have any side effects that could reduce their druglikeness or pharmacokinetic properties. Out of 114 compounds, we selected bisabololoxide B, eremanthin, and leptospermone as our top drug candidates based on their higher binding affinity scores, and strong interaction with the Cys 145-His 41 catalytic dyad. Finally, the molecular dynamics simulation was implemented to evaluate the structural stability of the ligand-receptor complex. MD simulations disclosed that all the hits showed conformational stability compared to the positive control α-ketoamide. Conclusions: Our study showed that the top three hits might work as potential anti-SARS-CoV-2 agents, which can pave the way for discovering new drugs, but for experimental validation, they will require more in vivo trials.

Probing In Silico the Benzimidazole Privileged Scaffold for the Development of Drug-like Anti-RSV Agents

Targeting the fusion (F) protein has been recognized as a fruitful strategy for the development of anti-RSV agents. Despite the considerable efforts so far put into the development of RSV F protein inhibitors, the discovery of adequate therapeutics for the treatment of RSV infections is still awaiting a positive breakthrough. Several benzimidazole-containing derivatives have been discovered and evaluated in clinical trials, with only some of them being endowed with a promising pharmacokinetic profile. In this context, we applied a computational study based on a careful analysis of a number of X-ray crystallographic data of the RSV F protein, in the presence of different clinical candidates. A deepen comparison of the related electrostatic features and H-bonding motifs allowed us to pave the way for the following molecular dynamic simulation of JNJ-53718678 and then to perform docking studies of the in-house library of potent benzimidazole-containing anti-RSV agents. The results revealed not only the deep flexibility of the biological target but also the most relevant and recurring key contacts supporting the benzimidazole F protein inhibitor ability. Among them, several hydrophobic interactions and π-π stacking involving F140 and F488 proved to be mandatory, as well as H-bonding to D486. Specific requirements turning in RSV F protein binding ability were also explored thanks to structure-based pharmacophore analysis. Along with this, in silico prediction of absorption, distribution, metabolism, excretion (ADME) properties, and also of possible off-target events was performed. The results highlighted once more that the benzimidazole ring represents a privileged scaffold whose properties deserve to be further investigated for the rational design of novel and orally bioavailable anti-RSV agents.

A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides

Owing to their ability to maintain a thermodynamically stable fold at extremely high temperatures, thermophilic proteins (TTPs) play a critical role in basic research and a variety of applications in the food industry. As a result, the development of computation models for rapidly and accurately identifying novel TTPs from a large number of uncharacterized protein sequences is desirable. In spite of existing computational models that have already been developed for characterizing thermophilic proteins, their performance and interpretability remain unsatisfactory. We present a novel sequence-based thermophilic protein predictor, termed SCMTPP, for improving model predictability and interpretability. First, an up-to-date and high-quality dataset consisting of 1853 TPPs and 3233 non-TPPs was compiled from published literature. Second, the SCMTPP predictor was created by combining the scoring card method (SCM) with estimated propensity scores of g-gap dipeptides. Benchmarking experiments revealed that SCMTPP had a cross-validation accuracy of 0.883, which was comparable to that of a support vector machine-based predictor (0.906-0.910) and 2-17% higher than that of commonly used machine learning models. Furthermore, SCMTPP outperformed the state-of-the-art approach (ThermoPred) on the independent test dataset, with accuracy and MCC of 0.865 and 0.731, respectively. Finally, the SCMTPP-derived propensity scores were used to elucidate the critical physicochemical properties for protein thermostability enhancement. In terms of interpretability and generalizability, comparative results showed that SCMTPP was effective for identifying and characterizing TPPs. We had implemented the proposed predictor as a user-friendly online web server at http://pmlabstack.pythonanywhere.com/SCMTPP in order to allow easy access to the model. SCMTPP is expected to be a powerful tool for facilitating community-wide efforts to identify TPPs on a large scale and guiding experimental characterization of TPPs.

Identification of Novel Potential Anti-Diabetic Candidates targeting Human Pancreatic ɑ-Amylase and Human ɑ-Glycosidase: An Exhaustive Structure-based Screening

Diabetes is a major health issue that half a billion people affected worldwide. It is a serious, long-term medical condition majorly impacting the lives and well-being of individuals, families, and societies at large. It is amongst the top 10 diseases responsible for the death amongst adults with an expected rise to 10.2% (578 million) by 2030 and 10.9% (700 million) by 2045. The carbohydrates absorbed into the body are hydrolyzed by pancreatic α-amylase and other enzymes, human α-glucosidase. The α-amylase and α-glucosidase are validated therapeutic targets in the treatment of Type II diabetes (T2DM) as they play a vital role in modulating the blood glucose post meal. Herein, we report novel and diverse molecules as potential candidates, with predicted affinity for α-amylase and α-glucosidase. These molecules have been identified via hierarchical multistep docking of small molecules database with the estimated binding free energies. A Glide XP Score cutoff −8.00 kcal/mol was implemented to filter out non potential molecules. Four molecules viz. amb22034702, amb18105639, amb17153304, and amb9760832 have been identified after an exhaustive computational study involving, evaluation of binding interactions and assessment of the pharmacokinetics and toxicity profiles. The in-depth analysis of protein– ligand interactions was performed using a 100ns molecular dynamics (MD) simulation to establish the dynamic stability. Furthermore MM-GBSA based binding free energies were computed for 1000 trajectory snapshots to ascertain the strong binding affinity of these molecules for α-amylase and αglucosidase. The identified molecules can be considered as promising candidates for further drug development through necessary experimental assessments.