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Fungus is a kind of living organism and yeast mould and mushrooms are types of fungi. The fungal infections are caused by the fungus. A fungus that invades the tissue can cause a disease that confined to the skin, spread into tissue, bone and organs or affect the whole body. Benzimidazole is a class of heterocyclic aromatic organic compound which posses pharmacological activities including antifungal, antitumor, antiparasitic, analgesic etc. Insilico methods can be used to identify target molecules using bioinformatics tool. The aim of our study was to conduct the insilico drug designing of some benzimidazole derivatives having antifungal activities. In our study the insilico drug design was performed using Biovia discovery studio.

(1) Background: Defects in gene CACNA1C, which encodes the pore-forming subunit of the human Cav1.2 channel (hCav1.2), are associated with cardiac disorders such as atrial fibrillation, long QT syndrome, conduction disorders, cardiomyopathies, and congenital heart defects. Clinical manifestations are known only for 12% of CACNA1C missense variants, which are listed in public databases. Bioinformatics approaches can be used to predict the pathogenic/likely pathogenic status for variants of uncertain clinical significance. Choosing a bioinformatics tool and pathogenicity threshold that are optimal for specific protein families increases the reliability of such predictions. (2) Methods and Results: We used databases ClinVar, Humsavar, gnomAD, and Ensembl to compose a dataset of pathogenic/likely pathogenic and benign variants of hCav1.2 and its 20 paralogues: voltage-gated sodium and calcium channels. We further tested the performance of sixteen in silico tools in predicting pathogenic variants. ClinPred demonstrated the best performance, followed by REVEL and MCap. In the subset of 309 uncharacterized variants of hCav1.2, ClinPred predicted the pathogenicity for 188 variants. Among these, 36 variants were also categorized as pathogenic/likely pathogenic in at least one paralogue of hCav1.2. (3) Conclusions: The bioinformatics tool ClinPred and the paralogue annotation method consensually predicted the pathogenic/likely pathogenic status for 36 uncharacterized variants of hCav1.2. An analogous approach can be used to classify missense variants of other calcium channels and novel variants of hCav1.2.

Background: Garlic (Allium sativum L.) is a common spice with many health benefits, mainly due to its diverse bioactive compounds, (see below) such as organic sulphides, saponins, phenolic compounds, and polysaccharides. Several studies have demonstrated its functions such as anti-inflammatory, antibacterial, and antiviral, antioxidant, cardiovascular protective and anticancer property. In this work we have investigated the main bioactive components of garlic through a bioinformatics approach. Indeed, we are in an era of bioinformatics where we can predict data in the fields of medicine. Approaches with open access in silico tools have revolutionized disease management due to early prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of the chemically designed and eco-friendly next-generation drugs. Methods: This paper encompasses the fundamental functions of open access in silico prediction tools, as PASS database (Prediction of Activity Spectra for Substances) that it estimates the probable biological activity profiles for compounds. This paper also aims to help support new researchers in the field of drug design and to investigate best bioactive compounds in garlic. Results: Screening through each of pharmacokinetic criteria resulted in identification of Garlic compounds that adhere to all the ADMET properties. Conclusions: It was established an open-access database (PASS database, available bioinformatics tool SwissADME, PreADMET pkCSM database) servers were employed to determine the ADMET (metabolism, distribution, excretion, absorption, and toxicity) attributes of garlic molecules and to enable identification of promising molecules that follow ADMET properties.

The bioinformatics tool NovoMIR was used to predict miRNAs in Musa acuminata genome. NovoMIR predicted 85 pre-miRNAs from the 11 chromosomes of Musa acuminata DH Pahang and BLAST analysis of the predicted pre-miRNAs against annotated miRNAs of miRBase identified 52 mature miRNAs belonging to 38 different families. psRNATarget server identified 124 protein-coding sequences as potential targets for 40 mature miRNAs. Based on the role of the target genes in biological processes related to biotic stress, five miRNAs were selected for analyzing their response to Banana bract mosaic virus (BBrMV) infection. Three-month-old in vitro raised banana plants of var. Nendran (Musa AAB) infected with BBrMV showed the presence of all the five selected miRNAs in both healthy and BBrMV infected plants. Expression analysis using RT-qPCR showed changes in the expression of miR-3900-5p, miR-2172-5p, miR-6928-5p and miR-971-5p and their targets during BBrMV infection.

BackgroundNon-small cell lung carcinoma (NSCLC) is a type lung cancer with high malignant behaviors. MicroRNAs (miRNAs) are known to be involved in progression of NSCLC. In order to explore potential targets for the treatment of NSCLC, bioinformatics tool was used to analyze differential expressed miRNAs between NSCLC and adjacent normal tissues.MethodsBioinformatics tool was used to find potential targets for NSCLC. Cell proliferation was investigated by Ki67 staining. Cell apoptosis was measured by flow cytometry. mRNA and protein expression in NSCLC cells were detected by RT-qPCR and Western-blot, respectively. Transwell assay was performed to test the cell migration and invasion. In order to investigate the function of exosomal miRNA in NSCLC, in vivo model of NSCLC was constructed.ResultsMiR-770 was identified to be downregulated in NSCLC, and miR-770 agomir could significantly inhibit NSCLC cell proliferation through inducing the apoptosis. Additionally, the metastasis of NSCLC cells was decreased by miR-770 agomir. MAP3K1 was identified to be the target mRNA of miR-770. Meanwhile, tumor cell-derived exosomal miR-770 inhibited M2 macrophage polarization via downregulation of MAP3K1, which in turn suppressed NSCLC cell invasion. Besides, tumor cell-derived exosomal miR-770 markedly decreased NSCLC tumor growth in vivo through suppressing M2 macrophage polarization.ConclusionTumor cell-derived exosomal miR-770 inhibits M2 macrophage polarization to inhibit the invasion of NSCLC cells via targeting MAP3K1. Thus, this study provided a new strategy for the treatment of NSCLC.

AbstractAdvances in mass-spectrometry have generated increasingly large-scale proteomics datasets containing tens of thousands of phosphorylation sites (phosphosites) that require prioritization. We develop a bioinformatics tool called HotPho and systematically discover 3D co-clustering of phosphosites and cancer mutations on protein structures. HotPho identifies 474 such hybrid clusters containing 1255 co-clustering phosphosites, including RET p.S904/Y928, the conserved HRAS/KRAS p.Y96, and IDH1 p.Y139/IDH2 p.Y179 that are adjacent to recurrent mutations on protein structures not found by linear proximity approaches. Hybrid clusters, enriched in histone and kinase domains, frequently include expression-associated mutations experimentally shown as activating and conferring genetic dependency. Approximately 300 co-clustering phosphosites are verified in patient samples of 5 cancer types or previously implicated in cancer, including CTNNB1 p.S29/Y30, EGFR p.S720, MAPK1 p.S142, and PTPN12 p.S275. In summary, systematic 3D clustering analysis highlights nearly 3,000 likely functional mutations and over 1000 cancer phosphosites for downstream investigation and evaluation of potential clinical relevance.

Metabolites have been shown to be closely related to the occurrence and development of many complex human diseases by a large number of biological experiments; investigating their correlation mechanisms is thus an important topic, which attracts many researchers. In this work, we propose a computational method named LGBMMDA, which is based on the Light Gradient Boosting Machine (LightGBM) to predict potential metabolite–disease associations. This method extracts the features from statistical measures, graph theoretical measures, and matrix factorization results, utilizing the principal component analysis (PCA) process to remove noise or redundancy. We evaluated our method compared with other used methods and demonstrated the better areas under the curve (AUCs) of LGBMMDA. Additionally, three case studies deeply confirmed that LGBMMDA has obvious superiority in predicting metabolite–disease pairs and represents a powerful bioinformatics tool.