DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor.
Brief Bioinform
; 22(6)2021 11 05.
Article
in En
| MEDLINE
| ID: mdl-34415016
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Peptides
/
Receptors, Antigen, T-Cell
/
SARS-CoV-2
/
COVID-19 Drug Treatment
/
Epitopes
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Brief Bioinform
Journal subject:
BIOLOGIA
/
INFORMATICA MEDICA
Year:
2021
Document type:
Article
Affiliation country:
China