Selection of peptide specifically binding to bladder carcinoma by using phage display in vivo / 中国免疫学杂志

Objective:To screen the peptide binding to human bladder carcinoma cells specifically by using phage display technology in vivo.Methods: Nude mice were inoculated with bladder carcinoma cells BIU87 for establishing tumor-bearing mice model.The Ph.D.-C7CTM Peptide Library was injected intravenously via tail vein.Then we screened Phage containing exogenous peptides binding to bladder transitional carcinoma cells specifically.The phage peptide homed to the tumor tissues was obtained after 3 rounds screening in vivo.The phage clones affinity to BIU87 were identified by immunohistochemistry and ELISA.The positive peptide was synthetized by chemical methods after sequencing the positive monoclonal phage DNA.The tumor cell specificity of target peptide was identified by confocal laser scanning microscope and flow cytometry.Results:After 3 rounds screening in vivo,enrichment rate of phage was 4.334×102 times.Immunohistochemistry results showed that the dyeing of the tumor tissue had a rising trend following each round of phage screening,while liver had a lot of non-specific binding phage because the phages were metabolized through liver and kid-ney.The 30 phage clones were identified by ELISA and 10 clones had a strong affinity on BIU87 among 24 positive clones.Three amino acid sequences of positive phage clones were obtained.The highest rate of repeat sequences CSSPIGRHC(8/10) named NYZL1 and the FITC-C6-NYZL1 peptide was synthesized.Our results showed that it could bind to bladder carcinoma cells BIU87 specifically.Conclusion:We obtained the small molecular peptide NYZL1 binding to human bladder carcinoma specifically by means of phage display in vivo,which provide a theoretical basis for bladder carcinoma early diagnosis and targeted therapy.

Prediction of MHC class Ⅰ binding peptides using neural network ensembles / 中国病理生理杂志

AIM: To predict MHC class Ⅰ binding peptides by using neural network ensembles. METHODS: As a combination of neural networks, neural network ensemble (NNE) was here used to improve the predictive performance. Based on a database of 628 nonamers and their classified binding capacities, the generalized NNEs were used to classify peptides respectively with non, low, moderate and high binding capacities to MHC class I molecule encoded by gene HLA-A*0201. The predictive power of NNE was further evaluated by running generalized NNE on a set of actual T-cell epitopes. RESULTS: The generalized NNEs achieved an average predictive hit rate of 0.8 for the above classifications. In addition, NNE was also efficient in the prediction of the potential T-cell epitopes, and about 84% of the actual T-cell epitopes were among the potentially antigenic peptides with high and moderate affinities. CONCLUSION: The NNEs can be applied in the prediction of MHC class Ⅰ binding peptides, and moreover, after proper modifications, they can be conveniently extended to cover peptides with any length and thus suitable for the prediction of peptides binding to other MHC class Ⅰ or even class Ⅱ molecules.