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Proteasomal cleavage site prediction of protein antigen using BP neural network based on a new set of amino acid descriptor

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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.

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Acknowledgments

This study was supported by grants from the National Basic Research Program of China (No. 2012CB11460), National Natural Science Foundation of China (No. 81171508, 31170747) and Natural Science Foundation Project of CQ CSTC (No. cstc2013jjb10004).

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Authors and Affiliations

  1. College of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400050, People’s Republic of China

    Yuanqiang Wang, Yong Lin, Mao Shu, Rui Wang, Yong Hu & Zhihua Lin

  2. State Key Laboratory of Silkworm Genome Biology, Southwest University, Chongqing, 400715, People’s Republic of China

    Yuanqiang Wang

  3. School of Chemistry and Chemical Engineering, Chongqing University, Chongqing, 400044, People’s Republic of China

    Zhihua Lin

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  1. Yuanqiang Wang
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  2. Yong Lin
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  3. Mao Shu
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  4. Rui Wang
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Correspondence to Zhihua Lin.

Electronic supplementary material

The supplementary lists the epitope, peso-epitope and their protein.

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Wang, Y., Lin, Y., Shu, M. et al. Proteasomal cleavage site prediction of protein antigen using BP neural network based on a new set of amino acid descriptor. J Mol Model 19, 3045–3052 (2013). https://doi.org/10.1007/s00894-013-1827-7

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  • DOI: https://doi.org/10.1007/s00894-013-1827-7

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