PMID- 34555693
OWN - NLM
STAT- MEDLINE
DCOM- 20211105
LR  - 20211105
IS  - 1872-9142 (Electronic)
IS  - 0161-5890 (Linking)
VI  - 139
DP  - 2021 Nov
TI  - A simple pan-specific RNN model for predicting HLA-II binding peptides.
PG  - 177-183
LID - S0161-5890(21)00272-8 [pii]
LID - 10.1016/j.molimm.2021.09.004 [doi]
AB  - The prediction of human leukocyte antigen (HLA) class II binding peptides plays 
      important roles in understanding the mechanism of immune recognition and 
      developing effective epitope-based vaccines. In this work, gated recurrent unit 
      (GRU)-based recurrent neural network (RNN) was successfully employed to establish 
      a pan-specific prediction model of HLA-II-binding peptides by using only the HLA 
      and peptide sequence information. In comparison with the existing pan-specific 
      models of HLA-II-binding peptides, the GRU-based RNN model covered a broad 
      spectrum of HLA-II molecules including 50 HLA-DR, 47 HLA-DQ, and 19 HLA-DP 
      molecules with peptide lengths varying from 8 to 43 mers. The results 
      demonstrated strong discriminant capabilities of the GRU-based RNN model, of 
      which the AUC values were 0.92, 0.88, and 0.88 for the training, validation, and 
      test sets, respectively. Also, the GRU-based model showed state-of-the-art 
      performances in predicting the binding peptides with the length ranging from 8-32 
      mers, which provides an efficient method for predicting HLA-II-binding peptides 
      of longer lengths in comparison with the available methods. Overall, taking the 
      advantages of the RNN architecture, the established pan-specific GRU model can be 
      used for predicting accurately the HLA-II-binding peptides in a simple and direct 
      manner.
CI  - Copyright (c) 2021. Published by Elsevier Ltd.
FAU - Heng, Yu
AU  - Heng Y
AD  - Key Laboratory of Biorheological Science and Technology (Ministry of Education), 
      Chongqing University, Chongqing, 400044, China; College of Bioengineering, 
      Chongqing University, Chongqing, 400044, China.
FAU - Kuang, Zuyin
AU  - Kuang Z
AD  - Key Laboratory of Biorheological Science and Technology (Ministry of Education), 
      Chongqing University, Chongqing, 400044, China.
FAU - Xie, Wenzhao
AU  - Xie W
AD  - College of Bioengineering, Chongqing University, Chongqing, 400044, China.
FAU - Lan, Haoqi
AU  - Lan H
AD  - College of Bioengineering, Chongqing University, Chongqing, 400044, China.
FAU - Huang, Shuheng
AU  - Huang S
AD  - College of Bioengineering, Chongqing University, Chongqing, 400044, China.
FAU - Chen, Linxin
AU  - Chen L
AD  - College of Bioengineering, Chongqing University, Chongqing, 400044, China.
FAU - Shi, Tingting
AU  - Shi T
AD  - College of Bioengineering, Chongqing University, Chongqing, 400044, China.
FAU - Xu, Lei
AU  - Xu L
AD  - College of Bioengineering, Chongqing University, Chongqing, 400044, China.
FAU - Pan, Xianchao
AU  - Pan X
AD  - Department of Medicinal Chemistry, College of Pharmacy, Southwest Medical 
      University, Luzhou, Sichuan, 646000, China. Electronic address: 
      panxc@swmu.edu.cn.
FAU - Mei, Hu
AU  - Mei H
AD  - Key Laboratory of Biorheological Science and Technology (Ministry of Education), 
      Chongqing University, Chongqing, 400044, China; College of Bioengineering, 
      Chongqing University, Chongqing, 400044, China. Electronic address: 
      meihu@cqu.edu.cn.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20210920
PL  - England
TA  - Mol Immunol
JT  - Molecular immunology
JID - 7905289
RN  - 0 (Histocompatibility Antigens Class II)
SB  - IM
MH  - Antigen Presentation/immunology
MH  - Histocompatibility Antigens Class II/chemistry/*immunology/metabolism
MH  - Humans
MH  - *Neural Networks, Computer
MH  - Protein Binding
OTO - NOTNLM
OT  - Gated recurrent unit
OT  - Human leukocyte antigen
OT  - Peptide
OT  - Prediction
OT  - Recurrent neural network
EDAT- 2021/09/24 06:00
MHDA- 2021/11/06 06:00
CRDT- 2021/09/23 20:24
PHST- 2020/11/27 00:00 [received]
PHST- 2021/08/17 00:00 [revised]
PHST- 2021/09/02 00:00 [accepted]
PHST- 2021/09/24 06:00 [pubmed]
PHST- 2021/11/06 06:00 [medline]
PHST- 2021/09/23 20:24 [entrez]
AID - S0161-5890(21)00272-8 [pii]
AID - 10.1016/j.molimm.2021.09.004 [doi]
PST - ppublish
SO  - Mol Immunol. 2021 Nov;139:177-183. doi: 10.1016/j.molimm.2021.09.004. Epub 2021 
      Sep 20.