PMID- 33952199
OWN - NLM
STAT- MEDLINE
DCOM- 20210510
LR  - 20240402
IS  - 1471-2105 (Electronic)
IS  - 1471-2105 (Linking)
VI  - 22
IP  - 1
DP  - 2021 May 5
TI  - DeepNetBim: deep learning model for predicting HLA-epitope interactions based on 
      network analysis by harnessing binding and immunogenicity information.
PG  - 231
LID - 10.1186/s12859-021-04155-y [doi]
LID - 231
AB  - BACKGROUND: Epitope prediction is a useful approach in cancer immunology and 
      immunotherapy. Many computational methods, including machine learning and network 
      analysis, have been developed quickly for such purposes. However, regarding 
      clinical applications, the existing tools are insufficient because few of the 
      predicted binding molecules are immunogenic. Hence, to develop more potent and 
      effective vaccines, it is important to understand binding and immunogenic 
      potential. Here, we observed that the interactive association constituted by 
      human leukocyte antigen (HLA)-peptide pairs can be regarded as a network in which 
      each HLA and peptide is taken as a node. We speculated whether this network could 
      detect the essential interactive propensities embedded in HLA-peptide pairs. 
      Thus, we developed a network-based deep learning method called DeepNetBim by 
      harnessing binding and immunogenic information to predict HLA-peptide 
      interactions. RESULTS: Quantitative class I HLA-peptide binding data and 
      qualitative immunogenic data (including data generated from T cell activation 
      assays, major histocompatibility complex (MHC) binding assays and MHC ligand 
      elution assays) were retrieved from the Immune Epitope Database database. The 
      weighted HLA-peptide binding network and immunogenic network were integrated into 
      a network-based deep learning algorithm constituted by a convolutional neural 
      network and an attention mechanism. The results showed that the integration of 
      network centrality metrics increased the power of both binding and immunogenicity 
      predictions, while the new model significantly outperformed those that did not 
      include network features and those with shuffled networks. Applied on benchmark 
      and independent datasets, DeepNetBim achieved an AUC score of 93.74% in 
      HLA-peptide binding prediction, outperforming 11 state-of-the-art relevant 
      models. Furthermore, the performance enhancement of the combined model, which 
      filtered out negative immunogenic predictions, was confirmed on neoantigen 
      identification by an increase in both positive predictive value (PPV) and the 
      proportion of neoantigen recognition. CONCLUSIONS: We developed a network-based 
      deep learning method called DeepNetBim as a pan-specific epitope prediction tool. 
      It extracted the attributes of the network as new features from HLA-peptide 
      binding and immunogenic models. We observed that not only did DeepNetBim binding 
      model outperform other updated methods but the combination of our two models 
      showed better performance. This indicates further applications in clinical 
      practice.
FAU - Yang, Xiaoyun
AU  - Yang X
AD  - Department of Bioinformatics and Biostatistics, School of Life Sciences and 
      Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
FAU - Zhao, Liyuan
AU  - Zhao L
AD  - Department of Bioinformatics and Biostatistics, School of Life Sciences and 
      Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
FAU - Wei, Fang
AU  - Wei F
AD  - Sheng Yushou Center of Cell Biology and Immunology, Joint International Research 
      Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and 
      Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
FAU - Li, Jing
AU  - Li J
AUID- ORCID: 0000-0003-4602-3227
AD  - Department of Bioinformatics and Biostatistics, School of Life Sciences and 
      Biotechnology, Shanghai Jiao Tong University, Shanghai, China. 
      jing.li@sjtu.edu.cn.
LA  - eng
GR  - No. 31871329/National Natural Science Foundation of China/
GR  - No. 31271416/National Natural Science Foundation of China/
GR  - Grant No. 2017SHZDZX01/Shanghai Municipal Science and Technology Major Project/
PT  - Journal Article
DEP - 20210505
PL  - England
TA  - BMC Bioinformatics
JT  - BMC bioinformatics
JID - 100965194
RN  - 0 (Epitopes)
RN  - 0 (HLA Antigens)
RN  - 0 (Histocompatibility Antigens Class I)
RN  - 0 (Histocompatibility Antigens Class II)
SB  - IM
MH  - Algorithms
MH  - *Deep Learning
MH  - Epitopes
MH  - HLA Antigens/genetics/metabolism
MH  - Histocompatibility Antigens Class I/metabolism
MH  - Histocompatibility Antigens Class II
MH  - Humans
MH  - Protein Binding
PMC - PMC8097772
OTO - NOTNLM
OT  - Deep learning
OT  - Network analysis
OT  - T cell epitope prediction
COIS- The authors declare that they have no competing interests.
EDAT- 2021/05/07 06:00
MHDA- 2021/05/11 06:00
PMCR- 2021/05/05
CRDT- 2021/05/06 05:41
PHST- 2020/12/11 00:00 [received]
PHST- 2021/04/27 00:00 [accepted]
PHST- 2021/05/06 05:41 [entrez]
PHST- 2021/05/07 06:00 [pubmed]
PHST- 2021/05/11 06:00 [medline]
PHST- 2021/05/05 00:00 [pmc-release]
AID - 10.1186/s12859-021-04155-y [pii]
AID - 4155 [pii]
AID - 10.1186/s12859-021-04155-y [doi]
PST - epublish
SO  - BMC Bioinformatics. 2021 May 5;22(1):231. doi: 10.1186/s12859-021-04155-y.