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Systematic, network-based characterization of therapeutic target inhibitors.

ABSTRACT: A large fraction of the proteins that are being identified as key tumor dependencies represent poor pharmacological targets or lack clinically-relevant small-molecule inhibitors. Availability of fully generalizable approaches for the systematic and efficient prioritization of tumor-context specific protein activity inhibitors would thus have significant translational value. Unfortunately, inhibitor effects on protein activity cannot be directly measured in systematic and proteome-wide fashion by conventional biochemical assays. We introduce OncoLead, a novel network based approach for the systematic prioritization of candidate inhibitors for arbitrary targets of therapeutic interest. In vitro and in vivo validation confirmed that OncoLead analysis can recapitulate known inhibitors as well as prioritize novel, context-specific inhibitors of difficult targets, such as MYC and STAT3. We used OncoLead to generate the first unbiased drug/regulator interaction map, representing compounds modulating the activity of cancer-relevant transcription factors, with potential in precision medicine.

SUBMITTER: Shen Y

PROVIDER: S-EPMC5638208 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Systematic, network-based characterization of therapeutic target inhibitors.

Shen Yao Y Alvarez Mariano J MJ Bisikirska Brygida B Lachmann Alexander A Realubit Ronald R Pampou Sergey S Coku Jorida J Karan Charles C Califano Andrea A

PLoS computational biology 20171012 10

A large fraction of the proteins that are being identified as key tumor dependencies represent poor pharmacological targets or lack clinically-relevant small-molecule inhibitors. Availability of fully generalizable approaches for the systematic and efficient prioritization of tumor-context specific protein activity inhibitors would thus have significant translational value. Unfortunately, inhibitor effects on protein activity cannot be directly measured in systematic and proteome-wide fashion by ...[more]

PMID: 29023443

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Project description:BackgroundObesity is a medical problem with an increased risk for other metabolic disorders like diabetes, heart problem, arthritis, etc. Leptin is an adipose tissue-derived hormone responsible for food intake, energy expenditure, etc., and leptin resistance is one of the significant causes of obesity. Excess leptin secretion by poor diet habits and impaired hypothalamic leptin signaling leads to LR. Melatonin a sleep hormone; also possess antioxidant and anti-inflammatory properties. The melatonin can attenuate the complications of obesity by regulating its targets towards LR induced obesity.AimThe aim of this study includes molecular pathway and network analysis by using a systems pharmacology approach to identify a potential therapeutic mechanism of melatonin on leptin resistance-induced obesity.MethodsThe bioinformatic methods are used to find therapeutic targets of melatonin in the treatment of leptin resistance-induced obesity. It includes target gene identification using public databases, Gene ontology, and KEGG pathway enrichment by 'ClusterProfiler' using the R language, network analysis by Cytoscape, and molecular Docking by Autodock.ResultsWe obtained the common top 33 potential therapeutic targets of melatonin and LR-induced obesity from the total melatonin targets 254 and common LR obesity targets 212 using the data screening method. They are involved in biological processes related to sleep and obesity, including the cellular response to external stimulus, chemical stress, and autophagy. From a total of 180 enriched pathways, we took the top ten pathways for further analysis, including lipid and atherosclerosis, endocrine, and AGE-RAGE signaling pathway in diabetic complications. The top 10 pathways interacted with the common 33 genes and created two functional modules. Using Cytoscape network analysis, the top ten hub genes (TP53, AKT1, MAPK3, PTGS2, TNF, IL6, MAPK1, ERBB2, IL1B, MTOR) were identified by the MCC algorithm of the CytoHubba plugin. From a wide range of pathway classes, melatonin can reduce LR-induced obesity risks by regulating the major six classes. It includes signal transduction, endocrine system, endocrine and metabolic disease, environmental adaptation, drug resistance antineoplastic, and cardiovascular disease.ConclusionThe pharmacological mechanism of action in this study shows the ten therapeutic targets of melatonin in LR-induced obesity.

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Systematic, network-based characterization of therapeutic target inhibitors.

Publications

Systematic, network-based characterization of therapeutic target inhibitors.

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