Cloning and Prokaryotic Expression of P23 Major Surface Protein Gene from Theileria sergenti

[Objective] The aim was to study cloning and prokaryotic expression of P23 major surface protein gene of Theileria sergenti. [Method] A pair of specific primers was designed according to the sequence of P23 major surface protein of T. sergenti (D84447).The P23 gene was amplified by PCR from genomic DNA of T. sergenti and cloned into pMD18-T vector to construct recombinant clonal vector pMD18-P23. Positive clones were identified by PCR screening and restriction digestion. A recombinant expression plasmid pGEX-4T-P23 was constructed by subcloning the cloned P23 gene into the linearized pGEX-4T-1 vector and transformed into E. coli BL21. After introduction by IPTG,the expressed fusion protein was identified by SDS-PAGE and Western-blotting. [Result] The cloned gene has a total length of 507 bp. Sequencing result showed that the nucleotide sequence of the cloned P23 gene shared 99.4% identity with that of P23 published in GenBank (D84447). The expressed fusion protein was 46 ku in molecular mass. Induction opportunity of zhours after culture inoculation was the best,the induction time of 6 h was the best,and induction temperature of 34 ℃ was the best as well,IPTG of 1 mmol/L had little effect on the expression. Western-blotting indicated that recombinant protein was recognized by specific antibody. [Conclusion] This study would lay a foundation for further research on the prevention and diagnose of T. sergenti.

Prediction of Protein Subcellular Localization Based on Hilbert-Huang Transform

Using Hilbert-Huang transform, subcellular localization tbr prokaryotic and eukaryotic proteins was predicted and tested with Reinhart and Hubbard＇s dataset. The prediction accu- racy of prokaryotic and eukaryotic protein sequences concentrated in the dataset all reached 100% by self-consistency, 91.8% for the former and 88% for the latter by the five fold cross-validation test. A significant improvement in prediction quality by incorporating the spectrum parameters with the conventional amino acid composition was observed. One of the crucial merits of this approach is that many existing tools in mathematics and engineering can be easily applied in the predicting process. It is anticipated that digital signal processing may serve as a useful vehicle for many other protein science areas.