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DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor.
Xu, Zhaochun; Luo, Meng; Lin, Weizhong; Xue, Guangfu; Wang, Pingping; Jin, Xiyun; Xu, Chang; Zhou, Wenyang; Cai, Yideng; Yang, Wenyi; Nie, Huan; Jiang, Qinghua.
  • Xu Z; Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China.
  • Luo M; Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China.
  • Lin W; Center for Bioinformatics, Computer Department, Jingdezhen Ceramic Institute, Jingdezhen 333403, China.
  • Xue G; Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China.
  • Wang P; Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China.
  • Jin X; Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China.
  • Xu C; Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China.
  • Zhou W; Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China.
  • Cai Y; Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China.
  • Yang W; Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China.
  • Nie H; Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China.
  • Jiang Q; Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China.
Brief Bioinform ; 22(6)2021 11 05.
Article in English | MEDLINE | ID: covidwho-1367012
ABSTRACT
Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http//jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https//github.com/jiangBiolab/DLpTCR.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Peptides / Receptors, Antigen, T-Cell / SARS-CoV-2 / COVID-19 Drug Treatment / Epitopes Type of study: Experimental Studies / Prognostic study Topics: Vaccines Limits: Humans Language: English Journal subject: Biology / Medical Informatics Year: 2021 Document Type: Article Affiliation country: Bib

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Peptides / Receptors, Antigen, T-Cell / SARS-CoV-2 / COVID-19 Drug Treatment / Epitopes Type of study: Experimental Studies / Prognostic study Topics: Vaccines Limits: Humans Language: English Journal subject: Biology / Medical Informatics Year: 2021 Document Type: Article Affiliation country: Bib