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In insects, P450s are responsible for the oxidative metabolism of structurally diverse endogenous and exogenous compounds including plant allelochemicals and insecticides. A novel full-length P450 cDNA, CYP6B47, was cloned from Spodoptera litura (Lepidoptera: Noctuidae). The sequence is 1718 bp in length with an ORF of 1509 bp encoding 503 amino acid residues. The phylogenetic analysis indicated that CYP6B47 belongs to CYP3 clan and second clade of CYP6Bs which contain 11 P450s from Noctuidae. Quantitative real-time PCR showed that CYP6B47 was expressed only in larvae stages and had a high level of transcription in the midgut and fat body. In addition, we cloned a 2141-bp 5'-flanking regions and presented the basal luciferase activities of promoter. We also predicted multiple putative elements for transcription factors binding in the 5'-flanking region. Interestingly, the expression of CYP6B47 significantly increased in the midgut and fat body after lead (Pb) exposure for 5 generations. Larvae tolerance to the alpha-cypermethrin (35% increased in LC(50)) and fenvalerate (52% increased in LC(50)) were improved after pre-exposure to 50 mg/kg Pb. These dates suggested that lead increased tolerance of larvae to insecticides mainly through transcriptional induction of detoxification genes including CYP6B47.
Pubmed ID: 22391394
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Database of protein families and domains that is based on the observation that, while there is a huge number of different proteins, most of them can be grouped, on the basis of similarities in their sequences, into a limited number of families. Proteins or protein domains belonging to a particular family generally share functional attributes and are derived from a common ancestor. It is complemented by ProRule, a collection of rules based on profiles and patterns, which increases the discriminatory power of profiles and patterns by providing additional information about functionally and/or structurally critical amino acids. ScanProsite finds matches of your protein sequences to PROSITE signatures. PROSITE currently contains patterns and profiles specific for more than a thousand protein families or domains. Each of these signatures comes with documentation providing background information on the structure and function of these proteins. The database is available via FTP.
View all literature mentionsTESS is a web tool for predicting transcription factor binding sites in DNA sequences. It can identify binding sites using site or consensus strings and positional weight matrices from the TRANSFAC, JASPAR, IMD, and our CBIL-GibbsMat database. You can use TESS to search a few of your own sequences or for user-defined CRMs genome-wide near genes throughout genomes of interest. Search for CRMs Genome-wide: TESS now has the ability to search whole genomes for user defined CRMs. Try a search in the AnGEL CRM Searches section of the navigation bar.. You can search for combinations of consensus site sequences and/or PWMs from TRANSFAC or JASPAR. Search DNA for Binding Sites: TESS also lets you search through your own sequence for TFBS. You can include your own site or consensus strings and/or weight matrices in the search. Use the Combined Search under ''Site Searches'' in the menu or use the box for a quick search. TESS assigns a TESS job number to all sequence search jobs. The job results are stored on our server for a period of time specified in the search submit form. During this time you may recall the search results using the form on this page. TESS can also email results to you as a tab-delimited file suitable for loading into a spreadsheet program. Query for Transcription Factor Info: TESS also has data browsing and querying capabilities to help you learn about the factors that were predicted to bind to your sequence. Use the Query TRANSFAC or Query Matrices links above or use the search interface provided from the home page.
View all literature mentionsWeb application for prediction of the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.
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