Bioinformatics and Biology Insights最新文献

{"title":"Computational Exploration of <i>Berberis lycium</i> Royle: A Hidden Treasure Trove for Antiviral Development.","authors":"Mamuna Mukhtar, Haris Ahmed Khan, Tope Abraham Ibisanmi, Ayodele Ifeoluwa Faleti, Najam Us Sahar Sadaf Zaidi","doi":"10.1177/11779322241264144","DOIUrl":"10.1177/11779322241264144","url":null,"abstract":"<p><p>Viral infections and associated illnesses account for approximately 3.5 million global fatalities and public health problems. Medicinal plants, with their wide therapeutic range and minimal side effects, have gained limelight particularly in response to growing concerns about drug resistance and sluggish development of antiviral drugs. This study computationally assessed 11 chemical compounds from <i>Berberis lycium</i> along with two antiviral drugs to inhibit SARS CoV 2 (coronavirus disease 2019 [COVID-19]) RNA-dependent RNA polymerase (RdRP), influenza virus RdRP, and two crucial dengue virus (DENV) enzymes (NS2B/NS3 protease and NS5 polymerase). Berberine and oxyberberine passed all pharmacokinetics analysis filters including Lipinski rule, blood-brain barrier permeant, and cytochrome suppression and demonstrated drug-likeness, bioavailability, and a non-toxic profile. Docking of phytochemicals from <i>B lycium</i> returned promising results with selected viral proteins, ie, DENV NS2BNS3 (punjabine -10.9 kcal/mol), DENV NS5 (punjabine -10.4 kcal/mol), COVID-19 RdRP (oxyacanthine -9.5 kcal/mol), and influenza RdRP (punjabine -10.4 kcal/mol). The optimal pharmacokinetics of berberine exhibited good binding energies with NS2BNS3 (-8.0 kcal/mol), NS5 (-8.3 kcal/mol), COVID RdRP (-7.7 kcal/mol), and influenza RdRP (-8.3 kcal/mol), while molecular dynamics simulation of a 50-ns time scale by GROMACS software package provided insights into the flexibility and stability of the complexes. A hidden treasure trove for antiviral research, berberine, berbamine, berbamunine, oxyberberine, oxyacanthine, baluchistanamine, and sindamine has showed encouraging findings as possible lead compounds. Pharmacological analyses provide credence for the proposed study; nevertheless, as the antiviral mechanisms of action of these phytochemicals are not well understood, additional research and clinical trials are required to demonstrate both their efficacy and toxicity through in vitro and in vivo studies.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241264144"},"PeriodicalIF":2.3,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11283669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141787200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Antiviral Activity, Pharmacoinformatics, Molecular Docking, and Dynamics Studies of <i>Azadirachta indica</i> Against Nipah Virus by Targeting Envelope Glycoprotein: Emerging Strategies for Developing Antiviral Treatment.","authors":"Otun Saha, Noimul Hasan Siddiquee, Rahima Akter, Nikkon Sarker, Uditi Paul Bristi, Khandokar Fahmida Sultana, Sm Lutfor Rahman Remon, Afroza Sultana, Tushar Ahmed Shishir, Md Mizanur Rahaman, Firoz Ahmed, Foysal Hossen, Mohammad Ruhul Amin, Mir Salma Akter","doi":"10.1177/11779322241264145","DOIUrl":"10.1177/11779322241264145","url":null,"abstract":"<p><p>The Nipah virus (NiV) belongs to the <i>Henipavirus</i> genus is a serious public health concern causing numerous outbreaks with higher fatality rate. Unfortunately, there is no effective medication available for NiV. To investigate possible inhibitors of NiV infection, we used in silico techniques to discover treatment candidates in this work. As there are not any approved treatments for NiV infection, the NiV-enveloped attachment glycoprotein was set as target for our study, which is responsible for binding to and entering host cells. Our in silico drug design approach included molecular docking, post-docking molecular mechanism generalised born surface area (MM-GBSA), absorption, distribution, metabolism, excretion/toxicity (ADME/T), and molecular dynamics (MD) simulations. We retrieved 418 phytochemicals associated with the neem plant (<i>Azadirachta indica</i>) from the IMPPAT database, and molecular docking was used to ascertain the compounds' binding strength. The top 3 phytochemicals with binding affinities of -7.118, -7.074, and -6.894 kcal/mol for CIDs 5280343, 9064, and 5280863, respectively, were selected for additional study based on molecular docking. The post-docking MM-GBSA of those 3 compounds was -47.56, -47.3, and -43.15 kcal/mol, respectively. As evidence of their efficacy and safety, all the chosen drugs had favorable toxicological and pharmacokinetic (Pk) qualities. We also performed MD simulations to confirm the stability of the ligand-protein complex structures and determine whether the selected compounds are stable at the protein binding site. All 3 phytochemicals, Quercetin (CID: 5280343), Cianidanol (CID: 9064), and Kaempferol (CID: 5280863), appeared to have outstanding binding stability to the target protein than control ribavirin, according to the molecular docking, MM-GBSA, and MD simulation outcomes. Overall, this work offers a viable approach to developing novel medications for treating NiV infection.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241264145"},"PeriodicalIF":2.3,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11283663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141787199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid Targeted Assembly of the Proteome Reveals Evolutionary Variation of GC Content in Avian Lice.","authors":"Avery R Grant, Kevin P Johnson, Edward L Stanley, James Baldwin-Brown, Stanislav Kolenčík, Julie M Allen","doi":"10.1177/11779322241257991","DOIUrl":"10.1177/11779322241257991","url":null,"abstract":"<p><p>Nucleotide base composition plays an influential role in the molecular mechanisms involved in gene function, phenotype, and amino acid composition. GC content (proportion of guanine and cytosine in DNA sequences) shows a high level of variation within and among species. Many studies measure GC content in a small number of genes, which may not be representative of genome-wide GC variation. One challenge when assembling extensive genomic data sets for these studies is the significant amount of resources (monetary and computational) associated with data processing, and many bioinformatic tools have not been optimized for resource efficiency. Using a high-performance computing (HPC) cluster, we manipulated resources provided to the targeted gene assembly program, automated target restricted assembly method (aTRAM), to determine an optimum way to run the program to maximize resource use. Using our optimum assembly approach, we assembled and measured GC content of all of the protein-coding genes of a diverse group of parasitic feather lice. Of the 499 426 genes assembled across 57 species, feather lice were GC-poor (mean GC = 42.96%) with a significant amount of variation within and between species (GC range = 19.57%-73.33%). We found a significant correlation between GC content and standard deviation per taxon for overall GC and GC₃, which could indicate selection for G and C nucleotides in some species. Phylogenetic signal of GC content was detected in both GC and GC₃. This research provides a large-scale investigation of GC content in parasitic lice laying the foundation for understanding the basis of variation in base composition across species.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241257991"},"PeriodicalIF":5.8,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11163934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141299951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>In Silico</i> Study, Protein Kinase Inhibition and Molecular Docking Study of Benzimidazole Derivatives.","authors":"Kamaraj Karthick, Kamaraj Abishek, Ebenezer Angel Jemima","doi":"10.1177/11779322241247635","DOIUrl":"10.1177/11779322241247635","url":null,"abstract":"<p><p>Kinase enzymes play an important role in cellular proliferation, and inhibition of their activity is a major goal of cancer therapy. Protein kinase inhibitors as benzimidazole derivatives can be applied for prevention or treatment of cancers through inhibition of cell proliferation. To evaluate their protein kinase inhibitory effects, as well as the <i>in silico</i> study for active benzimidazole derivatives. Benzimidazole derivatives has presented significant therapeutic potential against several disorders and known to have numerous biological activities (such as antibacterial, antiviral and anti-inflammatory). Benzimidazole derivatives have shown significant potential in the reduction of viral load as well as in enhancing immunity. To forecast absorption, distribution, metabolism, excretion and toxicity, simply known as ADMET and the Lipinski rule of five parameters of the examined substances, the admetSAR and Swiss ADME were used. The ADMET predictions revealed that the compounds had good and safe pharmacokinetic features, making them acceptable for further development as therapeutic candidates in clinical trials. This study primarily focused on blocking 2 key targets of kinase proteins (CDK4/CycD1 and Aurora B). 2-Phenylbenzimidazole has shown the greatest inhibitory potential (with a binding energy of -8.2 kcal/mol) against protein kinase inhibitors. This study results would pave the potential lead medication for anticancer therapeutic strategies.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241247635"},"PeriodicalIF":5.8,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11159556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141295567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of Long Non-Coding RNAs s Between Early and Late Stage of Liver Cancers From Non-coding RNA Profiles Using Machine-Learning Approach.","authors":"Songtham Anuntakarun, Jakkrit Khamjerm, Pisit Tangkijvanich, Natthaya Chuaypen","doi":"10.1177/11779322241258586","DOIUrl":"10.1177/11779322241258586","url":null,"abstract":"<p><p>Long non-coding RNAs (lncRNAs), which are RNA sequences greater than 200 nucleotides in length, play a crucial role in regulating gene expression and biological processes associated with cancer development and progression. Liver cancer is a major cause of cancer-related mortality worldwide, notably in Thailand. Although machine learning has been extensively used in analyzing RNA-sequencing data for advanced knowledge, the identification of potential lncRNA biomarkers for cancer, particularly focusing on lncRNAs as molecular biomarkers in liver cancer, remains comparatively limited. In this study, our objective was to identify candidate lncRNAs in liver cancer. We employed an expression data set of lncRNAs from patients with liver cancer, which comprised 40 699 lncRNAs sourced from The CancerLivER database. Various feature selection methods and machine-learning approaches were used to identify these candidate lncRNAs. The results showed that the random forest algorithm could predict lncRNAs using features extracted from the database, which achieved an area under the curve (AUC) of 0.840 for classifying lncRNAs between early (stage 1) and late stages (stages 2, 3, and 4) of liver cancer. Five of 23 significant lncRNAs (WAC-AS1, MAPKAPK5-AS1, ARRDC1-AS1, AC133528.2, and RP11-1094M14.11) were differentially expressed between early and late stage of liver cancer. Based on the Gene Expression Profiling Interactive Analysis (GEPIA) database, higher expression of WAC-AS1, MAPKAPK5-AS1, and ARRDC1-AS1 was associated with shorter overall survival. In conclusion, the classification model could predict the early and late stages of liver cancer using the signature expression of lncRNA genes. The identified lncRNAs might be used as early diagnostic and prognostic biomarkers for patients with liver cancer.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241258586"},"PeriodicalIF":5.8,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11155358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141282936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Immunoinformatics-Based Design of Multi-epitope DNA and mRNA Vaccines Against Zika Virus.","authors":"Juciene de Matos Braz, Marcus Vinicius de Aragão Batista","doi":"10.1177/11779322241257037","DOIUrl":"10.1177/11779322241257037","url":null,"abstract":"<p><p>In this study, we used an immunoinformatics approach to predict antigenic epitopes of Zika virus (ZIKV) proteins to assist in designing a vaccine antigen against ZIKV. We performed the prediction of CD8+ T-lymphocyte and antigenic B-cell epitopes of ZIKV proteins. The binding interactions of T-cell epitopes with major histocompatibility complex class I (MHC-I) proteins were assessed. We selected the antigenic, conserved, nontoxic, and immunogenic epitopes, which indicated significant interactions with the human leucocyte antigen (HLA-A and HLA-B) alleles and worldwide population coverage of 76.35%. The predicted epitopes were joined with the help of linkers and an adjuvant. The vaccine antigen was then analyzed through molecular docking with TLR3 and TLR8, and it was <i>in silico</i> cloned in the pVAX1 vector to be used as a DNA vaccine and designed as a mRNA vaccine.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241257037"},"PeriodicalIF":5.8,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141199314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Targeting decaprenylphosphoryl-β-D-ribose 2'-epimerase for Innovative Drug Development Against Mycobacterium Tuberculosis Drug-Resistant Strains.","authors":"Ghyzlane El Haddoumi, Mariam Mansouri, Jouhaina Kourou, Lahcen Belyamani, Azeddine Ibrahimi, Ilham Kandoussi","doi":"10.1177/11779322241257039","DOIUrl":"10.1177/11779322241257039","url":null,"abstract":"<p><p>Tuberculosis (TB) remains a global health challenge with the emergence of drug-resistant Mycobacterium tuberculosis variants, necessitating innovative drug molecules. One potential target is the cell wall synthesis enzyme decaprenylphosphoryl-β-D-ribose 2'-epimerase (DprE1), crucial for virulence and survival. This study employed virtual screening of 111 Protein Data Bank (PDB) database molecules known for their inhibitory biological activity against DprE1 with known IC50 values. Six compounds, PubChem ID: 390820, 86287492, 155294899, 155522922, 162651615, and 162665075, exhibited promising attributes as drug candidates and validated against clinical trial inhibitors BTZ043, TBA-7371, PBTZ169, and OPC-167832. Concurrently, this research focused on DprE1 mutation effects using molecular dynamic simulations. Among the 10 mutations tested, C387N significantly influenced protein behavior, leading to structural alterations observed through root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), and solvent-accessible surface area (SASA) analysis. Ligand 2 (ID: 390820) emerged as a promising candidate through ligand-based pharmacophore analysis, displaying enhanced binding compared with reference inhibitors. Molecular dynamic simulations highlighted ligand 2's interaction with the C387N mutation, reducing fluctuations, augmenting hydrogen bonding, and influencing solvent accessibility. These collective findings emphasize ligand 2's efficacy, particularly against severe mutations, in enhancing protein-ligand complex stability. Integrated computational and pharmacophore methodologies offer valuable insights into drug candidates and their interactions within intricate protein environments. This research lays a strategic foundation for targeted interventions against drug-resistant TB, highlighting ligand 2's potential for advanced drug development strategies.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241257039"},"PeriodicalIF":5.8,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11135120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141174826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Molecular Mechanisms of Ameloblastoma and Drug Repositioning by Integration of Bioinformatics Analysis and Molecular Docking Simulation.","authors":"Suthipong Chujan, Nutsira Vajeethaveesin, Jutamaad Satayavivad, Nakarin Kitkumthorn","doi":"10.1177/11779322241256459","DOIUrl":"10.1177/11779322241256459","url":null,"abstract":"<p><strong>Background: </strong>Ameloblastoma (AM) is a benign tumor locally originated from odontogenic epithelium that is commonly found in the jaw. This tumor makes aggressive invasions and has a high recurrence rate. This study aimed to investigate the differentially expressed genes (DEGs), biological function alterations, disease targets, and existing drugs for AM using bioinformatics analysis.</p><p><strong>Methods: </strong>The data set of AM was retrieved from the GEO database (GSE132474) and identified the DEGs using bioinformatics analysis. The biological alteration analysis was applied to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Protein-protein interaction (PPI) network analysis and hub gene identification were screened through NetworkAnalyst. The transcription factor-protein network was constructed via OmicsNet. We also identified candidate compounds from L1000CDS2 database. The target of AM and candidate compounds were verified using docking simulation.</p><p><strong>Results: </strong>Totally, 611 DEGs were identified. The biological function enrichment analysis revealed glycosaminoglycan and GABA (γ-aminobutyric acid) signaling were most significantly up-regulated and down-regulated in AM, respectively. Subsequently, hub genes and transcription factors were screened via the network and showed FOS protein was found in both networks. Furthermore, we evaluated FOS protein to be a therapeutic target in AMs. Candidate compounds were screened and verified using docking simulation. Tanespimycin showed the greatest affinity binding value to bind FOS protein.</p><p><strong>Conclusions: </strong>This study presented the underlying molecular mechanisms of disease pathogenesis, biological alteration, and important pathways of AMs and provided a candidate compound, Tanespimycin, targeting FOS protein for the treatment of AMs.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241256459"},"PeriodicalIF":5.8,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11135093/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141174874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers.","authors":"Francisca G Vieira, Regina Bispo, Marta B Lopes","doi":"10.1177/11779322241249563","DOIUrl":"10.1177/11779322241249563","url":null,"abstract":"<p><p>Glioma is currently one of the most prevalent types of primary brain cancer. Given its high level of heterogeneity along with the complex biological molecular markers, many efforts have been made to accurately classify the type of glioma in each patient, which, in turn, is critical to improve early diagnosis and increase survival. Nonetheless, as a result of the fast-growing technological advances in high-throughput sequencing and evolving molecular understanding of glioma biology, its classification has been recently subject to significant alterations. In this study, we integrate multiple glioma omics modalities (including mRNA, DNA methylation, and miRNA) from The Cancer Genome Atlas (TCGA), while using the revised glioma reclassified labels, with a supervised method based on sparse canonical correlation analysis (DIABLO) to discriminate between glioma types. We were able to find a set of highly correlated features distinguishing glioblastoma from lower-grade gliomas (LGGs) that were mainly associated with the disruption of receptor tyrosine kinases signaling pathways and extracellular matrix organization and remodeling. Concurrently, the discrimination of the LGG types was characterized primarily by features involved in ubiquitination and DNA transcription processes. Furthermore, we could identify several novel glioma biomarkers likely helpful in both diagnosis and prognosis of the patients, including the genes <i>PPP1R8, GPBP1L1, KIAA1614, C14orf23, CCDC77, BVES, EXD3, CD300A</i>, and <i>HEPN1</i>. Collectively, this comprehensive approach not only allowed a highly accurate discrimination of the different TCGA glioma patients but also presented a step forward in advancing our comprehension of the underlying molecular mechanisms driving glioma heterogeneity. Ultimately, our study also revealed novel candidate biomarkers that might constitute potential therapeutic targets, marking a significant stride toward personalized and more effective treatment strategies for patients with glioma.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241249563"},"PeriodicalIF":5.8,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11135104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141174821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Network Pharmacology and Molecular Docking Approach to Elucidate the Mechanism of <i>Commiphora wightii</i> for the Treatment of Rheumatoid Arthritis.","authors":"Mostafa A Abdel-Maksoud, Mostafa A Askar, Ibrahim Y Abdel-Rahman, Mustafa Gharib, Mohammed Aufy","doi":"10.1177/11779322241247634","DOIUrl":"10.1177/11779322241247634","url":null,"abstract":"<p><strong>Background: </strong>Rheumatoid arthritis (RA) is considered a notable prolonged inflammatory condition with no proper cure. Synovial inflammation and synovial pannus are crucial in the onset of RA. The \"tumor-like\" invading proliferation of new arteries is a keynote of RA. Commiphora wightii (<i>C wightii</i>) is a perennial, deciduous, and trifoliate plant used in several areas of southeast Asia to cure numerous ailments, including arthritis, diabetes, obesity, and asthma. Several <i>in vitro</i> investigations have indicated <i>C wightii's</i> therapeutic efficacy in the treatment of arthritis. However, the precise molecular action is yet unknown.</p><p><strong>Material and methods: </strong>In this study, a network pharmacology approach was applied to uncover potential targets, active therapeutic ingredients and signaling pathways in <i>C wightii</i> for the treatment of arthritis. In the groundwork of this research, we examined the active constituent-compound-target-pathway network and evaluated that (Guggulsterol-V, Myrrhahnone B, and Campesterol) decisively donated to the development of arthritis by affecting tumor necrosis factor (TNF), PIK3CA, and MAPK3 genes. Later on, docking was employed to confirm the active components' efficiency against the potential targets.</p><p><strong>Results: </strong>According to molecular-docking research, several potential targets of RA bind tightly with the corresponding key active ingredient of <i>C wightii.</i> With the aid of network pharmacology techniques, we conclude that the signaling pathways and biological processes involved in <i>C wightii</i> had an impact on the prevention of arthritis. The outcomes of molecular docking also serve as strong recommendations for future research. In the context of this study, network pharmacology combined with molecular docking analysis showed that <i>C wightii</i> acted on arthritis-related signaling pathways to exhibit a promising preventive impact on arthritis.</p><p><strong>Conclusion: </strong>These results serve as the basis for grasping the mechanism of the antiarthritis activity of <i>C wightii</i>. However, further <i>in vivo</i>/<i>in vitro</i> study is needed to verify the reliability of these targets for the treatment of arthritis.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241247634"},"PeriodicalIF":5.8,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11102677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141064722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}