Sequence Analysis and B Cell Epitope Prediction of Duck Hepatitis A Virus 1 VP1 Gene

2013 ◽  
Vol 647 ◽  
pp. 214-219
Author(s):  
Xing Jian Wen ◽  
An Chun Cheng ◽  
Ming Shu Wang

Molecular characterization and phylogenetic analysis of the DHAV-1 VP1 gene, structure and biological function of the VP1 protein were analyzed by using the programs of the DNAstar software ,MEGA software and other bioinformatics software on the line. The nucleotide sequence homologies among and the amino acids homologies of the VP1 gene of DHAV H stain with other 13 DHAV-1 strains in the GenBank were 92.54%-99.86% and 93.75%-99.57%.The putative secondary structure of the VP1 protein contained 10.08% H(alpha helix), 38.24% E(beta-sheet), 51.68%L(loop/coil), Protein can be classified as mixed. The results showed that the B cell epitopes of the VP1 protein were located at the C-terminal 131-136, and 209-218 regions according to Kyte-Doolitte method, Emini method,Karlus-Schulz method, and Jameson-Wolf method implemented in Protean program of DNAStar 7.The VP1 protein in the surface of the virus most exposed, it is to decide the major components of the effects of the virus of Picornaviridae. The VP1 protein of DHAV is a good biomaterial for vaccine research, and genetic and phylogenetic analyses of the VP1 and the B cell epitope prediction can help us to develop multi-epitope vaccine against DHAV variant viruses infections.

2015 ◽  
Vol 180 (3-4) ◽  
pp. 196-204 ◽  
Author(s):  
Ruihua Zhang ◽  
Guomei Zhou ◽  
Yinghao Xin ◽  
Junhao Chen ◽  
Shaoli Lin ◽  
...  

PLoS ONE ◽  
2015 ◽  
Vol 10 (2) ◽  
pp. e0118041 ◽  
Author(s):  
Xiaoying Wu ◽  
Xiaojun Li ◽  
Qingshan Zhang ◽  
Shaozhou Wulin ◽  
Xiaofei Bai ◽  
...  

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Li Cen Lim ◽  
Yee Ying Lim ◽  
Yee Siew Choong

Abstract B-cell epitope will be recognized and attached to the surface of receptors in B-lymphocytes to trigger immune response, thus are the vital elements in the field of epitope-based vaccine design, antibody production and therapeutic development. However, the experimental approaches in mapping epitopes are time consuming and costly. Computational prediction could offer an unbiased preliminary selection to reduce the number of epitopes for experimental validation. The deposited B-cell epitopes in the databases are those with experimentally determined positive/negative peptides and some are ambiguous resulted from different experimental methods. Prior to the development of B-cell epitope prediction module, the available dataset need to be handled with care. In this work, we first pre-processed the B-cell epitope dataset prior to B-cell epitopes prediction based on pattern recognition using support vector machine (SVM). By using only the absolute epitopes and non-epitopes, the datasets were classified into five categories of pathogen and worked on the 6-mers peptide sequences. The pre-processing of the datasets have improved the B-cell epitope prediction performance up to 99.1 % accuracy and showed significant improvement in cross validation results. It could be useful when incorporated with physicochemical propensity ranking in the future for the development of B-cell epitope prediction module.


Author(s):  
Yasser EL-Manzalawy ◽  
Vasant Honavar

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