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Computational prediction and experimental verification of new MAP kinase docking sites and substrates including Gli transcription factors.

ABSTRACT: In order to fully understand protein kinase networks, new methods are needed to identify regulators and substrates of kinases, especially for weakly expressed proteins. Here we have developed a hybrid computational search algorithm that combines machine learning and expert knowledge to identify kinase docking sites, and used this algorithm to search the human genome for novel MAP kinase substrates and regulators focused on the JNK family of MAP kinases. Predictions were tested by peptide array followed by rigorous biochemical verification with in vitro binding and kinase assays on wild-type and mutant proteins. Using this procedure, we found new 'D-site' class docking sites in previously known JNK substrates (hnRNP-K, PPM1J/PP2Czeta), as well as new JNK-interacting proteins (MLL4, NEIL1). Finally, we identified new D-site-dependent MAPK substrates, including the hedgehog-regulated transcription factors Gli1 and Gli3, suggesting that a direct connection between MAP kinase and hedgehog signaling may occur at the level of these key regulators. These results demonstrate that a genome-wide search for MAP kinase docking sites can be used to find new docking sites and substrates.

SUBMITTER: Whisenant TC

PROVIDER: S-EPMC2928751 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Computational prediction and experimental verification of new MAP kinase docking sites and substrates including Gli transcription factors.

Whisenant Thomas C TC Ho David T DT Benz Ryan W RW Rogers Jeffrey S JS Kaake Robyn M RM Gordon Elizabeth A EA Huang Lan L Baldi Pierre P Bardwell Lee L

PLoS computational biology 20100826 8

In order to fully understand protein kinase networks, new methods are needed to identify regulators and substrates of kinases, especially for weakly expressed proteins. Here we have developed a hybrid computational search algorithm that combines machine learning and expert knowledge to identify kinase docking sites, and used this algorithm to search the human genome for novel MAP kinase substrates and regulators focused on the JNK family of MAP kinases. Predictions were tested by peptide array f ...[more]

PMID: 20865152

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Project description:BackgroundOxidative stress (OS) is one of the major causes of ovarian aging and dysfunction. Indole-3-propionic acid (IPA) is an indole compound derived from tryptophan with free radical scavenging and antioxidant properties, and thus may have potential applications in protecting ovarian function, although the exact mechanisms are unknown. This study aims to preliminarily elucidate the potential mechanisms of IPA that benefit ovarian reserve function through network pharmacology, molecular docking, and experimental verification.MethodsThe related protein targets of IPA were searched on SwissTargetPrediction, TargetNet, BATMAN-TCM, and PharmMapper databases. The potential targets of diminished ovarian reserve (DOR) were identified from OMIM, GeneCards, DrugBank, and DisGeNET databases. The common targets were uploaded directly to the STRING database to construct PPI networks. We then performed GO and KEGG enrichment analysis on the targets. Subsequently, molecular docking and molecular dynamics simulation were used to validate the binding conformation of IPA to candidate targets. Furthermore, we carried out in vitro experiments to validate the prediction results of network pharmacology.ResultsWe identified a total of 61 potential targets for the interaction of IPA with DOR. The PPI network topological parameter analysis yielded 13 hub genes for DOR treatment. The GO biological process enrichment analysis identified 293 entries, mainly enriched in aging, signal transduction, response to hypoxia, negative regulation of apoptotic process, and positive regulation of cell proliferation. The KEGG enrichment analysis mainly included lipid and atherosclerosis, progesterone-mediated oocyte maturation, AGE-RAGE, relaxin, estrogen, and other signaling pathways. The molecular docking further revealed the direct binding of IPA with six hub proteins including NOS3, AKT1, EGFR, PPARA, SRC, and TNF. In vitro experiments showed that IPA pretreatment attenuated H2O2-induced cellular oxidative stress damage, while IPA exerted cytoprotective and antioxidant damage effects by regulating the six hub genes and antioxidant proteins.ConclusionWe systematically illustrated the potential protective effects of IPA against DOR through multiple targets and pathways using network pharmacology, and further verified the cytoprotective effect and antioxidant properties of IPA through in vitro experiments. These findings provide new insights into the targets and molecular mechanisms whereby IPA improves DOR.

Project description:BackgroundGuanine nucleotide binding (G) protein subunit γ 4 (GNG4) is closely related to the malignant progression and poor prognosis of various tumours. However, its role and mechanism in osteosarcoma remain unclear. This study aimed to elucidate the biological role and prognostic value of GNG4 in osteosarcoma.MethodsOsteosarcoma samples in the GSE12865, GSE14359, GSE162454 and TARGET datasets were selected as the test cohorts. The expression level of GNG4 between normal and osteosarcoma was identified in GSE12865 and GSE14359. Based on the osteosarcoma single-cell RNA sequencing (scRNA-seq) dataset GSE162454, differential expression of GNG4 among cell subsets was identified at the single-cell level. As the external validation cohort, 58 osteosarcoma specimens from the First Affiliated Hospital of Guangxi Medical University were collected. Patients with osteosarcoma were divided into high- and low-GNG4 groups. The biological function of GNG4 was annotated using Gene Ontology, gene set enrichment analysis, gene expression correlation analysis and immune infiltration analysis. Kaplan-Meier survival analysis was conducted and receiver operating characteristic (ROC) curves were calculated to determine the reliability of GNG4 in predicting prognostic significance and diagnostic value. Functional in vitro experiments were performed to explore the function of GNG4 in osteosarcoma cells.ResultsGNG4 was generally highly expressed in osteosarcoma. As an independent risk factor, high GNG4 was negatively correlated with both overall survival and event-free survival. Furthermore, GNG4 was a good diagnostic marker for osteosarcoma, with an area under the receiver operating characteristic curve (AUC) of more than 0.9. Functional analysis suggested that GNG4 may promote the occurrence of osteosarcoma by regulating ossification, B-cell activation, the cell cycle and the proportion of memory B cells. In in vitro experiments, silencing of GNG4 inhibited the viability, proliferation and invasion of osteosarcoma cells.ConclusionThrough bioinformatics analysis and experimental verification, high expression of GNG4 in osteosarcoma was identified as an oncogene and reliable biomarker for poor prognosis. This study helps to elucidate the significant potential of GNG4 in carcinogenesis and molecular targeted therapy for osteosarcoma.

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Computational prediction and experimental verification of new MAP kinase docking sites and substrates including Gli transcription factors.

Publications

Computational prediction and experimental verification of new MAP kinase docking sites and substrates including Gli transcription factors.

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