PMID- 33398286
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231110
DP  - 2020 Dec 24
TI  - DeepImmuno: Deep learning-empowered prediction and generation of immunogenic 
      peptides for T cell immunity.
LID - 2020.12.24.424262 [pii]
LID - 10.1101/2020.12.24.424262 [doi]
AB  - T-cells play an essential role in the adaptive immune system by seeking out, 
      binding and destroying foreign antigens presented on the cell surface of diseased 
      cells. An improved understanding of T-cell immunity will greatly aid in the 
      development of new cancer immunotherapies and vaccines for life threatening 
      pathogens. Central to the design of such targeted therapies are computational 
      methods to predict non-native epitopes to elicit a T cell response, however, we 
      currently lack accurate immunogenicity inference methods. Another challenge is 
      the ability to accurately simulate immunogenic peptides for specific human 
      leukocyte antigen (HLA) alleles, for both synthetic biological applications and 
      to augment real training datasets. Here, we proposed a beta-binomial distribution 
      approach to derive epitope immunogenic potential from sequence alone. We 
      conducted systematic benchmarking of five traditional machine learning 
      (ElasticNet, KNN, SVM, Random Forest, AdaBoost) and three deep learning models 
      (CNN, ResNet, GNN) using three independent prior validated immunogenic peptide 
      collections (dengue virus, cancer neoantigen and SARS-Cov-2). We chose the CNN 
      model as the best prediction model based on its adaptivity for small and large 
      datasets, and performance relative to existing methods. In addition to 
      outperforming two highly used immunogenicity prediction algorithms, DeepHLApan 
      and IEDB, DeepImmuno-CNN further correctly predicts which residues are most 
      important for T cell antigen recognition. Our independent generative adversarial 
      network (GAN) approach, DeepImmuno-GAN, was further able to accurately simulate 
      immunogenic peptides with physiochemical properties and immunogenicity 
      predictions similar to that of real antigens. We provide DeepImmuno-CNN as source 
      code and an easy-to-use web interface. DATA AVAILABILITY: DeepImmuno Python3 code 
      is available at https://github.com/frankligy/DeepImmuno . The DeepImmuno web 
      portal is available from https://deepimmuno.herokuapp.com . The data in this 
      article is available in GitHub and supplementary materials.
FAU - Li, Guangyuan
AU  - Li G
AD  - Division of Biomedical Informatics, Cincinnati Children's Hospital Medical 
      Center, Cincinnati, OH, USA.
AD  - Department of Biomedical Informatics, College of Medicine, University of 
      Cincinnati, OH, 45267 USA.
FAU - Iyer, Balaji
AU  - Iyer B
AD  - Division of Biomedical Informatics, Cincinnati Children's Hospital Medical 
      Center, Cincinnati, OH, USA.
AD  - Department of Electrical Engineering and Computer Science, University of 
      Cincinnati, OH 45221 USA.
FAU - Prasath, V B Surya
AU  - Prasath VBS
AD  - Division of Biomedical Informatics, Cincinnati Children's Hospital Medical 
      Center, Cincinnati, OH, USA.
AD  - Department of Pediatrics, University of Cincinnati School of Medicine, 
      Cincinnati, Ohio, USA.
AD  - Department of Biomedical Informatics, College of Medicine, University of 
      Cincinnati, OH, 45267 USA.
AD  - Department of Electrical Engineering and Computer Science, University of 
      Cincinnati, OH 45221 USA.
FAU - Ni, Yizhao
AU  - Ni Y
AD  - Division of Biomedical Informatics, Cincinnati Children's Hospital Medical 
      Center, Cincinnati, OH, USA.
AD  - Department of Pediatrics, University of Cincinnati School of Medicine, 
      Cincinnati, Ohio, USA.
AD  - Department of Biomedical Informatics, College of Medicine, University of 
      Cincinnati, OH, 45267 USA.
FAU - Salomonis, Nathan
AU  - Salomonis N
AD  - Division of Biomedical Informatics, Cincinnati Children's Hospital Medical 
      Center, Cincinnati, OH, USA.
AD  - Department of Pediatrics, University of Cincinnati School of Medicine, 
      Cincinnati, Ohio, USA.
AD  - Department of Biomedical Informatics, College of Medicine, University of 
      Cincinnati, OH, 45267 USA.
AD  - Department of Electrical Engineering and Computer Science, University of 
      Cincinnati, OH 45221 USA.
LA  - eng
GR  - R01 CA226802/CA/NCI NIH HHS/United States
GR  - U24 HL148865/HL/NHLBI NIH HHS/United States
PT  - Preprint
DEP - 20201224
PL  - United States
TA  - bioRxiv
JT  - bioRxiv : the preprint server for biology
JID - 101680187
UIN - Brief Bioinform. 2021 May 03;:. PMID: 34009266
PMC - PMC7781330
COIS- Conflict of interests The authors report no significant conflicts of interest.
EDAT- 2021/01/06 06:00
MHDA- 2021/01/06 06:01
PMCR- 2021/01/04
CRDT- 2021/01/05 06:26
PHST- 2021/01/05 06:26 [entrez]
PHST- 2021/01/06 06:00 [pubmed]
PHST- 2021/01/06 06:01 [medline]
PHST- 2021/01/04 00:00 [pmc-release]
AID - 2020.12.24.424262 [pii]
AID - 10.1101/2020.12.24.424262 [doi]
PST - epublish
SO  - bioRxiv [Preprint]. 2020 Dec 24:2020.12.24.424262. doi: 
      10.1101/2020.12.24.424262.