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Showing 991 to 994 of 5186 entries
Published on February 11, 2022
The molecular mechanism of Ligusticum wallichii for improving idiopathic pulmonary fibrosis: A network pharmacology and molecular docking study.
Abstract: BACKGROUND: At present, there was no evidence that any drugs other than lung transplantation can effectively treat Idiopathic Pulmonary Fibrosis (IPF). Ligusticum wallichii, or Chinese name Chuan xiong has been widely used in different fibrosis fields. Our aim is to use network pharmacology and molecular docking to explore the pharmacological mechanism of the Traditional Chinese medicine (TCM) Ligusticum wallichii to improve IPF. MATERIALS AND METHODS: The main chemical components and targets of Ligusticum wallichii were obtained from TCMSP, Swiss Target Prediction and Phammapper databases, and the targets were uniformly regulated in the Uniprot protein database after the combination. The main targets of IPF were obtained through Gencards, OMIM, TTD and DRUGBANK databases, and protein interaction analysis was carried out by using String to build PPI network. Metascape platform was used to analyze its involved biological processes and pathways, and Cytoscape3.8.2 software was used to construct "component-IPF target-pathway" network. And molecular docking verification was conducted through Auto Dock software. RESULTS: The active ingredients of Ligusticum wallichii were Myricanone, Wallichilide, Perlolyrine, Senkyunone, Mandenol, Sitosterol and FA. The core targets for it to improve IPF were MAPK1, MAPK14, SRC, BCL2L1, MDM2, PTGS2, TGFB2, F2, MMP2, MMP9, and so on. The molecular docking verification showed that the molecular docking affinity of the core active compounds in Ligusticum wallichii (Myricanone, wallichilide, Perlolyrine) was <0 with MAPK1, MAPK14, and SRC. Perlolyrine has the strongest molecular docking ability, and its docking ability with SRC (-6.59 kJ/mol) is particularly prominent. Its biological pathway to improve IPF was mainly acted on the pathways in cancer, proteoglycans in cancer, and endocrine resistance, etc. CONCLUSIONS: This study preliminarily identified the various molecular targets and multiple pathways of Ligusticum wallichii to improve IPF.
Published on February 10, 2022
Development of Gene Expression-Based Random Forest Model for Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer.
Abstract: Neoadjuvant chemotherapy (NAC) response is an important indicator of patient survival in triple negative breast cancer (TNBC), but predicting chemosensitivity remains a challenge in clinical practice. We developed an 86-gene-based random forest (RF) classifier capable of predicting neoadjuvant chemotherapy response (pathological Complete Response (pCR) or Residual Disease (RD)) in TNBC patients. The performance of pCR classification of the proposed model was evaluated by Receiver Operating Characteristic (ROC) curve and Precision Recall (PR) curve. The AUROC and AUPRC of the proposed model on the test set were 0.891 and 0.829, respectively. At a predefined specificity (>90%), the proposed model shows a superior sensitivity compared to the best performing reported NAC response prediction model (69.2% vs. 36.9%). Moreover, the predicted pCR status by the model well explains the distance recurrence free survival (DRFS) of TNBC patients. In addition, the pCR probabilities of the proposed model using the expression profiles of the CCLE TNBC cell lines show a high Spearman rank correlation with cyclophosphamide sensitivity in the TNBC cell lines (SRCC =0.697, p-value =0.031). Associations between the 86 genes and DNA repair/cell cycle mechanisms were provided through function enrichment analysis. Our study suggests that the random forest-based prediction model provides a reliable prediction of the clinical response to neoadjuvant chemotherapy and may explain chemosensitivity in TNBC.
Published on February 9, 2022
DDPD 1.0: a manually curated and standardized database of digital properties of approved drugs for drug-likeness evaluation and drug development.
Abstract: Drug-likeness is a vital consideration when selecting compounds in the early stage of drug discovery. A series of drug-like properties are needed to predict the drug-likeness of a given compound and provide useful guidelines to increase the likelihood of converting lead compounds into drugs. Experimental physicochemical properties, pharmacokinetic/toxicokinetic properties and maximum dosages of approved small-molecule drugs from multiple text-based unstructured data resources have been manually assembled, curated, further digitized and processed into structured data, which are deposited in the Database of Digital Properties of approved Drugs (DDPD). DDPD 1.0 contains 30 212 drug property entries, including 2250 approved drugs and 32 properties, in a standardized value/unit format. Moreover, two analysis tools are provided to examine the drug-likeness features of given molecules based on the collected property data of approved drugs. Additionally, three case studies are presented to demonstrate how users can utilize the database. We believe that this database will be a valuable resource for the drug discovery and development field. Database URL: http://www.inbirg.com/ddpd.
Published on February 9, 2022
A platform for oncogenomic reporting and interpretation.
Abstract: Manual interpretation of variants remains rate limiting in precision oncology. The increasing scale and complexity of molecular data generated from comprehensive sequencing of cancer samples requires advanced interpretative platforms as precision oncology expands beyond individual patients to entire populations. To address this unmet need, we introduce a Platform for Oncogenomic Reporting and Interpretation (PORI), comprising an analytic framework that facilitates the interpretation and reporting of somatic variants in cancer. PORI integrates reporting and graph knowledge base tools combined with support for manual curation at the reporting stage. PORI represents an open-source platform alternative to commercial reporting solutions suitable for comprehensive genomic data sets in precision oncology. We demonstrate the utility of PORI by matching 9,961 pan-cancer genome atlas tumours to the graph knowledge base, calculating therapeutically informative alterations, and making available reports describing select individual samples.
Published on February 8, 2022
Study on the Active Constituents and Molecular Mechanism of Zhishi Xiebai Guizhi Decoction in the Treatment of CHD Based on UPLC-UESI-Q Exactive Focus, Gene Expression Profiling, Network Pharmacology, and Experimental Validation.
Abstract: As one of the most common clinical cardiovascular diseases (CVDs), coronary heart disease (CHD) is the most common cause of death in the world. It has been confirmed that Zhishi Xiebai Guizhi decoction (ZXGD), a classical prescription of the traditional Chinese medicine (TCM), has achieved certain effects in the treatment of CHD; however, the mechanism still remains controversial. In this paper, an integrated approach, including UPLC-UESI-Q Exactive Focus, gene expression profiling, network pharmacology, and experimental validation, was introduced to systematically investigate the mechanism of ZXGD in the treatment of CHD. First, UPLC-UESI-Q Exactive Focus was applied to identify the chemical compounds of ZXGD. Then, the targets of the components for ZXGD were predicted by MedChem Studio software embed in the integrative pharmacology-based research platform of TCM, and the differentially expressed genes (DEGs) of CHD were obtained by gene expression profiling in gene expression omnibus database. The common genes of the above two genes were obtained by Venn analysis as the targets of GXGD in treatment with CHD. Third, the core targets were screened out by protein-protein interaction network analysis, and the kyoto encyclopedia of genes and genomes pathway enrichment analysis was performed by the database for annotation, visualization, and integrated discovery bioinformatics resources. After that, the formula-herb-compound-target-pathway network was constructed to explore the mechanism of ZXGD in the treatment of CHD. Finally, molecular docking and the vitro experiment were carried out to validate some key targets. As a result, a total of 39 compounds, 12 core targets, and 4 pathways contributed to ZXGD for the treatment of CHD. This study preliminarily provided a foundation for the study on the mechanism against CHD for ZXGD and may be a reference for the compatibility mechanism and the extended application of TCM compound prescription.
Published on February 8, 2022
Sequence-based prediction of protein binding regions and drug-target interactions.
Abstract: Identifying drug-target interactions (DTIs) is important for drug discovery. However, searching all drug-target spaces poses a major bottleneck. Therefore, recently many deep learning models have been proposed to address this problem. However, the developers of these deep learning models have neglected interpretability in model construction, which is closely related to a model's performance. We hypothesized that training a model to predict important regions on a protein sequence would increase DTI prediction performance and provide a more interpretable model. Consequently, we constructed a deep learning model, named Highlights on Target Sequences (HoTS), which predicts binding regions (BRs) between a protein sequence and a drug ligand, as well as DTIs between them. To train the model, we collected complexes of protein-ligand interactions and protein sequences of binding sites and pretrained the model to predict BRs for a given protein sequence-ligand pair via object detection employing transformers. After pretraining the BR prediction, we trained the model to predict DTIs from a compound token designed to assign attention to BRs. We confirmed that training the BRs prediction model indeed improved the DTI prediction performance. The proposed HoTS model showed good performance in BR prediction on independent test datasets even though it does not use 3D structure information in its prediction. Furthermore, the HoTS model achieved the best performance in DTI prediction on test datasets. Additional analysis confirmed the appropriate attention for BRs and the importance of transformers in BR and DTI prediction. The source code is available on GitHub ( https://github.com/GIST-CSBL/HoTS ).
Published on February 5, 2022
A Genome-Scale Metabolic Model for the Human Pathogen Candida Parapsilosis and Early Identification of Putative Novel Antifungal Drug Targets.
Abstract: Candida parapsilosis is an emerging human pathogen whose incidence is rising worldwide, while an increasing number of clinical isolates display resistance to first-line antifungals, demanding alternative therapeutics. Genome-Scale Metabolic Models (GSMMs) have emerged as a powerful in silico tool for understanding pathogenesis due to their systems view of metabolism, but also to their drug target predictive capacity. This study presents the construction of the first validated GSMM for C. parapsilosis-iDC1003-comprising 1003 genes, 1804 reactions, and 1278 metabolites across four compartments and an intercompartment. In silico growth parameters, as well as predicted utilisation of several metabolites as sole carbon or nitrogen sources, were experimentally validated. Finally, iDC1003 was exploited as a platform for predicting 147 essential enzymes in mimicked host conditions, in which 56 are also predicted to be essential in C. albicans and C. glabrata. These promising drug targets include, besides those already used as targets for clinical antifungals, several others that seem to be entirely new and worthy of further scrutiny. The obtained results strengthen the notion that GSMMs are promising platforms for drug target discovery and guide the design of novel antifungal therapies.
Published on February 5, 2022
Statins and Gliomas: A Systematic Review of the Preclinical Studies and Meta-Analysis of the Clinical Literature.
Abstract: BACKGROUND: Gliomas represent most common primary brain tumors. Glioblastoma (GBM) is the most common subtype and carries a poor prognosis. There is growing interest in the anti-glioma properties of statins. The aim of this study was to conduct a systematic review of the preclinical literature and to meta-analyze existing clinical studies to determine what benefit, if any, statins may confer in the context of glioma. METHODS: The PubMed, Embase, Cochrane, and Web of Science libraries were queried in May 2021. Preclinical studies were included if they investigated the anti-cancer effects of statins in glioma in vitro and in vivo. Clinical studies were included if they reported incidence rates of glioma by statin use, or mortality outcomes among GBM patients by statin use. Pooled point estimates were calculated using a random-effects model. RESULTS: In total, 64 publications, 51 preclinical and 13 clinical, were included. Preclinical studies indicated that statins inhibited glioma cell proliferation, migration, and invasion. These effects were time- and concentration-dependent. Synergistic anti-glioma effects were observed when statins were combined with other anti-cancer therapies. Clinical observational studies showed an inverse, albeit non-statistically significant, association between statin use and incidence rate of glioma (HR = 0.84, 95% CI 0.62-1.13, I(2) = 72%, p-heterogeneity = 0.003, 6 studies). Statin use was not associated with better overall survival following GBM surgery (HR = 1.05, 95% CI 0.85-1.30, I(2) = 30%, p-heterogeneity = 0.23, 4 studies). CONCLUSION: Statins were potent anti-cancer drugs that suppressed glioma growth through various mechanisms in vitro; these effects have translated into the clinical realm, clinically but not statistically, in terms of glioma incidence but not GBM survival.
Published on February 3, 2022
Simplified quality assessment for small-molecule ligands in the Protein Data Bank.
Abstract: More than 70% of the experimentally determined macromolecular structures in the Protein Data Bank (PDB) contain small-molecule ligands. Quality indicators of approximately 643,000 ligands present in approximately 106,000 PDB X-ray crystal structures have been analyzed. Ligand quality varies greatly with regard to goodness of fit between ligand structure and experimental data, deviations in bond lengths and angles from known chemical structures, and inappropriate interatomic clashes between the ligand and its surroundings. Based on principal component analysis, correlated quality indicators of ligand structure have been aggregated into two largely orthogonal composite indicators measuring goodness of fit to experimental data and deviation from ideal chemical structure. Ranking of the composite quality indicators across the PDB archive enabled construction of uniformly distributed composite ranking score. This score is implemented at RCSB.org to compare chemically identical ligands in distinct PDB structures with easy-to-interpret two-dimensional ligand quality plots, allowing PDB users to quickly assess ligand structure quality and select the best exemplars.
Published on February 3, 2022
Worldwide Protein Data Bank (wwPDB): A virtual treasure for research in biotechnology.
Abstract: The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RSCB PDB) provides a wide range of digital data regarding biology and biomedicine. This huge internet resource involves a wide range of important biological data, obtained from experiments around the globe by different scientists. The Worldwide Protein Data Bank (wwPDB) represents a brilliant collection of 3D structure data associated with important and vital biomolecules including nucleic acids (RNAs and DNAs) and proteins. Moreover, this database accumulates knowledge regarding function and evolution of biomacromolecules which supports different disciplines such as biotechnology. 3D structure, functional characteristics and phylogenetic properties of biomacromolecules give a deep understanding of the biomolecules' characteristics. An important advantage of the wwPDB database is the data updating time, which is done every week. This updating process helps users to have the newest data and information for their projects. The data and information in wwPDB can be a great support to have an accurate imagination and illustrations of the biomacromolecules in biotechnology. As demonstrated by the SARS-CoV-2 pandemic, rapidly reliable and accessible biological data for microbiology, immunology, vaccinology, and drug development are critical to address many healthcare-related challenges that are facing humanity. The aim of this paper is to introduce the readers to wwPDB, and to highlight the importance of this database in biotechnology, with the expectation that the number of scientists interested in the utilization of Protein Data Bank's resources will increase substantially in the coming years.
Showing 991 to 994 of 5186 entries