DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity

Wu, Jingcheng; Wang, Wenzhe; Zhang, Jiucheng; Zhou, Binbin; Zhao, Wenyi; Su, Zhixi; Gu, Xun; Wu, Jian; Zhou, Zhan; Chen, Shuqing

doi:10.3389/fimmu.2019.02559

Cited by 107 publications

(87 citation statements)

References 54 publications

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“…We screened multiple layer types in the sequence data embedding block: recurrent layers (bidirectional GRU and LSTM), self‐attention, convolutional layers (simple convolutions and inception‐like), and densely connected layers as a reference. Recurrent layer types and self‐attention layers were previously useful for modeling language (Vaswani et al , ) and epitope (Wu et al , ) data. Convolutional layer types have been useful for modeling epitope (Han & Kim, ; Vang & Xie, ) and image (Szegedy et al , ) data.…”

Section: Methodsmentioning

confidence: 99%

Predicting antigen specificity of single T cells based on TCR CDR 3 regions

Fischer

Schubert

et al. 2020

Molecular Systems Biology

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It has recently become possible to simultaneously assay T‐cell specificity with respect to large sets of antigens and the T‐cell receptor sequence in high‐throughput single‐cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T‐cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single‐cell immune repertoire screens can be mitigated by modeling cell‐specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell‐to‐dextramer binding strength and receptor‐to‐pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single‐cell RNA‐seq studies on T cells without the need for MHC staining.

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Section: Methodsmentioning

confidence: 99%

Predicting antigen specificity of single T cells based on TCR CDR 3 regions

Fischer

Schubert

et al. 2020

Molecular Systems Biology

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“…In addition, we also compared the precision indicator, which was calculated by the ratio of the true positive to the predicted positive peptides. In the test set, the precision indicator of NetMHCpan 4.0 was 54.55%, while the precision indicator of POTN was 67.44%, with 23.63% improvement [the method for improvement rate calculation was referred to ( 68 )].…”

Section: Resultsmentioning

confidence: 99%

POTN: A Human Leukocyte Antigen-A2 Immunogenic Peptides Screening Model and Its Applications in Tumor Antigens Prediction

Meng

Sui

et al. 2020

Front. Immunol.

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Whole genome/exome sequencing data for tumors are now abundant, and many tumor antigens, especially mutant antigens (neoantigens), have been identified for cancer immunotherapy. However, only a small fraction of the peptides from these antigens induce cytotoxic T cell responses. Therefore, efficient methods to identify these antigenic peptides are crucial. The current models of major histocompatibility complex (MHC) binding and antigenic prediction are still inaccurate. In this study, 360 9-mer peptides with verified immunological activity were selected to construct a prediction of tumor neoantigen (POTN) model, an immunogenic prediction model specifically for the human leukocyte antigen-A2 allele. Based on the physicochemical properties of amino acids, such as the residue propensity, hydrophobicity, and organic solvent/water, we found that the predictive capability of POTN is superior to that of the prediction programs SYPEITHI, IEDB, and NetMHCpan 4.0. We used POTN to screen peptides for the cancer-testis antigen located on the X chromosome, and we identified several peptides that may trigger immunogenicity. We synthesized and measured the binding affinity and immunogenicity of these peptides and found that the accuracy of POTN is higher than that of NetMHCpan 4.0. Identifying the properties related to the T cell response or immunogenicity paves the way to understanding the MHC/peptide/T cell receptor complex. In conclusion, POTN is an efficient prediction model for screening high-affinity immunogenic peptides from tumor antigens, and thus provides useful information for developing cancer immunotherapy.

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“…[141] During the past few years, a number of deep learningbased methods have been developed that outperform traditional machine learning methods, including shallow neural networks, for peptide-MHC binding prediction ( Table 4). Among these algorithms, 14 (ConvMHC, [142] HLA-CNN, [143] DeepMHC, [144] DeepSeqPan, [145] MHCSeqNet, [146] MHCflurry, [147] DeepHLApan, [148] ACME, [149] EDGE, [137] CNN-NF, [150] DeepNeo, [151] DeepLigand, [152] MHCherryPan, [153] and DeepAttentionPan [141] ) are specific for MHC class I binding prediction, three (DeepSeqPanII, [154] MARIA, [138] and NeonMHC2 [139] ) are specific for MHC class II binding prediction, and four (AI-MHC, [155] MHCnuggets, [156] PUFFIN, [157] and USMPep [158] ) can make predictions for both classes. All four types of peptide encoding approaches illustrated in Figure 1 are used in these tools, with one-hot encoding and BLOSUM matrix encoding being the most frequently used methods (Table 4).…”

Section: Deep Learning For Mhc-binding Peptide Predictionmentioning

confidence: 99%

Deep Learning in Proteomics

et al. 2020

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Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS) technology, protein expression and post-translational modifications (PTMs) can be studied in a variety of biological systems at the global scale. Sophisticated computational algorithms are needed to translate the vast amount of data into novel biological insights. Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data-rich scientific research domains. Here, a comprehensive overview of deep learning applications in proteomics, including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex-peptide binding prediction, and protein structure prediction, is provided. Limitations and the future directions of deep learning in proteomics are also discussed. This review will provide readers an overview of deep learning and how it can be used to analyze proteomics data.

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DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity

Cited by 107 publications

References 54 publications

Predicting antigen specificity of single T cells based on TCR CDR 3 regions

Predicting antigen specificity of single T cells based on TCR CDR 3 regions

POTN: A Human Leukocyte Antigen-A2 Immunogenic Peptides Screening Model and Its Applications in Tumor Antigens Prediction

Deep Learning in Proteomics

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