PMID- 37232386
OWN - NLM
STAT- MEDLINE
DCOM- 20230724
LR  - 20230724
IS  - 1477-4054 (Electronic)
IS  - 1467-5463 (Linking)
VI  - 24
IP  - 4
DP  - 2023 Jul 20
TI  - IEPAPI: a method for immune epitope prediction by incorporating antigen 
      presentation and immunogenicity.
LID - bbad171 [pii]
LID - 10.1093/bib/bbad171 [doi]
AB  - CD8+ T cells can recognize peptides presented by class I human leukocyte antigen 
      (HLA-I) of nucleated cells. Exploring this immune mechanism is essential for 
      identifying T-cell vaccine targets in cancer immunotherapy. Over the past decade, 
      the wealth of data generated by experiments has spawned many computational 
      approaches for predicting HLA-I binding, antigen presentation and T-cell immune 
      responses. Nevertheless, existing HLA-I binding and antigen presentation 
      prediction approaches suffer from low precision due to the absence of T-cell 
      receptor (TCR) recognition. Direct modeling of T-cell immune responses is less 
      effective as TCR recognition's mechanism still remains underexplored. Therefore, 
      directly applying these existing methods to screen cancer neoantigens is still 
      challenging. Here, we propose a novel immune epitope prediction method termed 
      IEPAPI by effectively incorporating antigen presentation and immunogenicity. 
      First, IEPAPI employs a transformer-based feature extraction block to acquire 
      representations of peptides and HLA-I proteins. Second, IEPAPI integrates the 
      prediction of antigen presentation prediction into the input of immunogenicity 
      prediction branch to simulate the connection between the biological processes in 
      the T-cell immune response. Quantitative comparison results on an independent 
      antigen presentation test dataset exhibit that IEPAPI outperformed the current 
      state-of-the-art approaches NetMHCpan4.1 and mhcflurry2.0 on 100 (25/25) and 76% 
      (19/25) of the HLA subtypes, respectively. Furthermore, IEPAPI demonstrates the 
      best precision on two independent neoantigen datasets when compared with existing 
      approaches, suggesting that IEPAPI provides a vital tool for T-cell vaccine 
      design.
CI  - (c) The Author(s) 2023. Published by Oxford University Press. All rights reserved. 
      For Permissions, please email: journals.permissions@oup.com.
FAU - Deng, Juntao
AU  - Deng J
AD  - Department of Automation, Tsinghua University, Beijing, 100084, China.
FAU - Zhou, Xiao
AU  - Zhou X
AD  - Department of Automation, Tsinghua University, Beijing, 100084, China.
FAU - Zhang, Pengyan
AU  - Zhang P
AD  - Department of Automation, Tsinghua University, Beijing, 100084, China.
FAU - Cheng, Weibin
AU  - Cheng W
AD  - Guangdong Second Provincial General Hospital, Guangzhou 510317, China.
FAU - Liu, Min
AU  - Liu M
AD  - Department of Automation, Tsinghua University, Beijing, 100084, China.
FAU - Tian, Junzhang
AU  - Tian J
AD  - Guangdong Second Provincial General Hospital, Guangzhou 510317, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - England
TA  - Brief Bioinform
JT  - Briefings in bioinformatics
JID - 100912837
RN  - 0 (Epitopes)
RN  - 0 (Histocompatibility Antigens Class I)
RN  - 0 (Receptors, Antigen, T-Cell)
RN  - 0 (Peptides)
SB  - IM
MH  - Humans
MH  - *Antigen Presentation
MH  - Epitopes
MH  - *Neoplasms
MH  - Histocompatibility Antigens Class I
MH  - Receptors, Antigen, T-Cell
MH  - Peptides
OTO - NOTNLM
OT  - antigen presentation
OT  - cancer immunotherapy
OT  - deep learning
OT  - immunogenicity
OT  - neoantigen
OT  - transformer
EDAT- 2023/05/26 13:09
MHDA- 2023/07/24 06:43
CRDT- 2023/05/26 06:34
PHST- 2023/01/17 00:00 [received]
PHST- 2023/03/02 00:00 [revised]
PHST- 2023/04/14 00:00 [accepted]
PHST- 2023/07/24 06:43 [medline]
PHST- 2023/05/26 13:09 [pubmed]
PHST- 2023/05/26 06:34 [entrez]
AID - 7179756 [pii]
AID - 10.1093/bib/bbad171 [doi]
PST - ppublish
SO  - Brief Bioinform. 2023 Jul 20;24(4):bbad171. doi: 10.1093/bib/bbad171.