Dataset Information

Pathway-Centric Structure-Based Multi-Target Compound Screening for Anti-Virulence Drug Repurposing.

ABSTRACT: The emergence of superbugs that are resistant to last-resort antibiotics poses a serious threat to human health, and we are in a "race against time to develop new antibiotics." New approaches are urgently needed to control drug-resistant pathogens, and to reduce the emergence of new drug-resistant microbes. Targeting bacterial virulence has emerged as an important strategy for combating drug-resistant pathogens. It has been shown that pyocyanin, which is produced by the phenazine biosynthesis pathway, plays a key role in the virulence of Pseudomonas aeruginosa infection, making it an attractive target for anti-infective drug discovery. In order to discover efficient therapeutics that inhibit the phenazine biosynthesis in a timely fashion, we screen 2004 clinical and pre-clinical drugs to target multiple enzymes in the phenazine biosynthesis pathway, using a novel procedure of protein-ligand docking. Our detailed analysis suggests that kinase inhibitors, notably Lifirafenib, are promising lead compounds for inhibiting aroQ, phzG, and phzS enzymes that are involved in the phenazine biosynthesis, and merit further experimental validations. In principle, inhibiting multiple targets in a pathway will be more effective and have less chance of the emergence of drug resistance than targeting a single protein. Our multi-target structure-based drug design strategy can be applied to other pathways, as well as provide a systematic approach to polypharmacological drug repositioning.

SUBMITTER: Xie L

PROVIDER: S-EPMC6678309 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Publications

Pathway-Centric Structure-Based Multi-Target Compound Screening for Anti-Virulence Drug Repurposing.

Xie Li L Xie Lei L

International journal of molecular sciences 20190717 14

The emergence of superbugs that are resistant to last-resort antibiotics poses a serious threat to human health, and we are in a "race against time to develop new antibiotics." New approaches are urgently needed to control drug-resistant pathogens, and to reduce the emergence of new drug-resistant microbes. Targeting bacterial virulence has emerged as an important strategy for combating drug-resistant pathogens. It has been shown that pyocyanin, which is produced by the phenazine biosynthesis pa ...[more]

PMID: 31319464

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Project description:Chinese medicine is a complex system guided by traditional Chinese medicine (TCM) theories, which has proven to be especially effective in treating chronic and complex diseases. However, the underlying modes of action (MOA) are not always systematically investigated. Herein, a systematic study was designed to elucidate the multi-compound, multi-target and multi-pathway MOA of a Chinese medicine ,QSYQ, on myocardial infarction. Male Sprague Dawley rat model of myocardial infarction were administered QSYQ intragastrically for 7 days while the control group was not treated. The differentially expressed genes (DEGs) were identified from myocardial infarction rat model treated with QSYQ, followed by constructing a cardiovascular disease (CVD)-related multilevel compound-target-pathway network connecting main compounds to those DEGs supported by literature evidences and the pathways that are functionally enriched. Three conditions were compared with three replicates each: (1) sham, i.e. without left anterior descending coronary artery (LAD) ligation; (2) model, with LAD ligation; (3) QSYQ, with LAD ligation and treated with QSYQ intragastrically for 7 days, the dosage was 105.6 mg/kg once a day. Rats were sacrificed after 7 days of i.g. administration under 10% chloral hydrate anesthesia (300mg/kg). Three tissue samples on the border between infarct and non-infarct area were dissected from left ventricles of each group. The tissue samples were stored at -80M-bM-^DM-^C refrigerator. Total RNA was extracted using TRIZol Reagent (Invitrogen) and purified using RNeasy Mini kit (QIAGEN), following manufacturersM-bM-^@M-^Y protocols. RNA quality was evaluated using an Agilent 2100 Bioanalyzer and electrophoresis in 2% (w/v) agarose gels. Only RNA with RNA integrity numbers (RINs) greater than 7.0 and 28SrRNA/18S rRNA ratio greater than 0.7 was used for microarray analyses. Whole genome microarray analysis was performed using Affymetrix rat Genome 230 2.0 chips.

Project description:Drug repurposing plays an important role in screening old drugs for new therapeutic efficacy. The existing methods commonly treat prediction of drug-target interaction as a problem of binary classification, in which a large number of randomly sampled drug-target pairs accounting for over 50% of the entire training dataset are necessarily required. Such a large number of negative examples that do not come from experimental observations inevitably decrease the credibility of predictions. In this study, we propose a multi-label learning framework to find new uses for old drugs and discover new drugs for known target genes. In the framework, each drug is treated as a class label and its target genes are treated as the class-specific training data to train a supervised learning model of l2-regularized logistic regression. As such, the inter-drug associations are explicitly modelled into the framework and all the class-specific training data come from experimental observations. In addition, the data constraint is less demanding, for instance, the chemical substructures of a drug are no longer needed and the novel target genes are inferred only from the underlying patterns of the known genes targeted by the drug. Stratified multi-label cross-validation shows that 84.9% of known target genes have at least one drug correctly recognized, and the proposed framework correctly recognizes 86.73% of the independent test drug-target interactions (DTIs) from DrugBank. These results show that the proposed framework could generalize well in the large drug/class space without the information of drug chemical structures and target protein structures. Furthermore, we use the trained model to predict new drugs for the known target genes, identify new genes for the old drugs, and infer new associations between old drugs and new disease phenotypes via the OMIM database. Gene ontology (GO) enrichment analyses and the disease associations reported in recent literature provide supporting evidences to the computational results, which potentially shed light on new clinical therapies for new and/or old disease phenotypes.

Dataset Information

Pathway-Centric Structure-Based Multi-Target Compound Screening for Anti-Virulence Drug Repurposing.

Publications

Pathway-Centric Structure-Based Multi-Target Compound Screening for Anti-Virulence Drug Repurposing.

Similar Datasets

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