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Molecular cloning, expression and characterization of enolase from adult Haemonchus contortus.

Kaikai Han | Lixin Xu | Ruofeng Yan | Xiaokai Song | Xiangrui Li
Research in veterinary science | 2012

Enolase represents a multifunctional protein involved in basic energy metabolism. In the present research, the enolase gene of Haemonchus contortus (HcENO) was cloned and characterized. Specific primers for the rapid amplification of cDNA ends (RACE) were designed based on the expression sequence tag (EST, GenBank Accession No. BF422728) to amplify the 3'- and 5'-ends of HcENO. The full length of cDNA from this gene was obtained by overlapping the sequences of 3'- and 5'-extremities and amplification by reverse transcription PCR. The biochemical activities of the recombinant protein HcENO, which was expressed in prokaryotic cells and purified by affinity chromatography, were analyzed by assays of enzymatic activity, stability to pH. The results showed that the cloned full length cDNA comprised 1583 bp and encoded a peptide with 434 amino acid residues which showed sequence similarity to several known enolases. The biochemical assay showed that the protein encoded by the HcENO exhibited enzymatic activity, whilst the HcENO was stable between pH 6 and 8. The natural enolase of H. contortus detected by immunoblot assay was approximately 49 kDa in size, and the recombinant HcENO was recognized strongly by serum from experimentally infected goats.

Pubmed ID: 21444098

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NCBI BLAST (tool)

RRID:SCR_004870

Web search tool to find regions of similarity between biological sequences. Program compares nucleotide or protein sequences to sequence databases and calculates statistical significance. Used for identifying homologous sequences.

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SignalP (tool)

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Web 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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