RT Journal Article
SR Electronic
T1 Epitope Prediction of Antigen Protein using Attention-Based LSTM Network
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.07.27.224121
DO 10.1101/2020.07.27.224121
A1 Noumi, Toshiaki
A1 Inoue, Seiichi
A1 Fujita, Haruka
A1 Sadamitsu, Kugatsu
A1 Sakaguchi, Makoto
A1 Tenma, Akiko
A1 Nakagami, Hironori
YR 2020
UL http://biorxiv.org/content/early/2020/07/29/2020.07.27.224121.abstract
AB B-cells inducing antigen-specific immune responses in vivo produce large amounts of antigen-specific antibodies by recognizing the subregions (epitope regions) of antigen proteins. They can inhibit their functioning by binding antibodies to antigen proteins. Predicting of epitope regions is beneficial for the design and development of vaccines aimed to induce antigen-specific antibody production. However, prediction accuracy requires improvement. The conventional epitope region prediction methods have focused only on the target sequence in the amino acid sequences of an entire antigen protein and have not thoroughly considered its sequence and features as a whole. In the present paper, we propose a deep learning method based on short-term memory with an attention mechanism to consider the characteristics of a whole antigen protein in addition to the target sequence. The proposed method achieves better accuracy compared with the conventional method in the experimental prediction of epitope regions using the data from the immune epitope database.Competing Interest StatementThe authors have declared no competing interest.