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CVN (cyanovirin-N) is an anti-HIV protein. CVNH (cyanovirin-N homology) represents its homology. In a previous study, we first reported the full-length sequences of the CVNH gene cloned from Ceratopteris thalictroides. Based on the finding, the coding sequence of CtCVNH was optimized in the study, and then a pET prokaryotic expression vector was constructed. The purification and identification of CtCVNH protein were investigated, as well. SDS-PAGE analysis indicated that a 31 kDa protein was overexpressed and mainly accumulated in the soluble fraction. Only a single protein was obtained after the Ni- nitrilotriacetic acid (NTA) affinity chromatography. The purified protein was identified to be the recombinant CtCVNH by both Western blot and peptide mass fingerprinting analysis.
Pubmed ID: 23567268
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The Center for Biological Sequence Analysis of the Technical University of Denmark conducts basic research in the field of bioinformatics and systems biology and directs its research primarily towards topics related to the elucidation of the functional aspects of complex biological mechanisms. A large number of computational methods have been produced, which are offered to others via WWW servers. Several data sets are also available. The center also has experimental efforts in gene expression analysis using DNA chips and data generation in relation to the physical and structural properties of DNA. The on-line prediction services at CBS are available as interactive input forms. Most of the servers are also available as stand-alone software packages with the same functionality. In addition, for some servers, programmatic access is provided in the form of SOAP-based Web Services. The center also educates engineering students in biotechnology and systems biology and offers a wide range of courses in bioinformatics, systems biology, human health, microbiology and nutrigenomics.
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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