title: Proteasomal cleavage site prediction of protein antigen using BP neural network based on a new set of amino acid descriptor.
abstract: The accurate identification of cytotoxic T lymphocyte epitopes is becoming increasingly important in peptide vaccine design. The ubiquitin-proteasome system plays a key role in processing and presenting major histocompatibility complex class I restricted epitopes by degrading the antigenic protein. To enhance the specificity and efficiency of epitope prediction and identification, the recognition mode between the ubiquitin-proteasome complex and the protein antigen must be considered. Hence, a model that accurately predicts proteasomal cleavage must be established. This study proposes a new set of parameters to characterize the cleavage window and uses a backpropagation neural network algorithm to build a model that accurately predicts proteasomal cleavage. The accuracy of the prediction model, which depends on the window sizes of the cleavage, reaches 95.454% for the N-terminus and 95.011% for the C-terminus. The results show that the identification of proteasomal cleavage sites depends on the sequence next to it and that the prediction performance of the C-terminus is better than that of the N-terminus on average. Thus, models based on the properties of amino acids can be highly reliable and reflect the structural features of interactions between proteasomes and peptide sequences.
authors.affiliation: College of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400050, People's Republic of China.
authors.forename: Yuanqiang
authors.initials: Y
authors.lastname: Wang
authors.forename: Yong
authors.initials: Y
authors.lastname: Lin
authors.forename: Mao
authors.initials: M
authors.lastname: Shu
authors.forename: Rui
authors.initials: R
authors.lastname: Wang
authors.forename: Yong
authors.initials: Y
authors.lastname: Hu
authors.forename: Zhihua
authors.initials: Z
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D030562:Databases, Protein; D018984:Epitopes, T-Lymphocyte / Q000737:chemistry* / Q000276:immunology / Q000378:metabolism; D015789:HLA-A2 Antigen / Q000737:chemistry* / Q000276:immunology / Q000378:metabolism; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010455:Peptides / Q000737:chemistry* / Q000276:immunology / Q000378:metabolism; D046988:Proteasome Endopeptidase Complex / Q000378:metabolism; D017434:Protein Structure, Tertiary; D059748:Proteolysis; D012680:Sensitivity and Specificity; D013602:T-Lymphocytes, Cytotoxic / Q000737:chemistry* / Q000276:immunology / Q000378:metabolism; D025801:Ubiquitin / Q000378:metabolism
chemical_list: D018984:Epitopes, T-Lymphocyte; C435939:HLA-A*02:01 antigen; D015789:HLA-A2 Antigen; D010455:Peptides; D025801:Ubiquitin; D046988:Proteasome Endopeptidase Complex