MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction

Computationally predicting MHC-peptide binding affinity is an important problem in immunological bioinformatics. Recent cutting-edge deep learning-based methods for this problem are unable to achieve satisfactory performance for MHC class II molecules. This is because such methods generate the input by simply concatenating the two given sequences: (the estimated binding core of) a peptide and (the pseudo sequence of) an MHC class II molecule, ignoring the biological knowledge behind the interactions of the two molecules. We thus propose a binding core-aware deep learning-based model, DeepMHCII, with binding interaction convolution layer (BICL), which allows integrating all potential binding cores (in a given peptide) and the MHC pseudo (binding) sequence, through modeling the interaction with multiple convolutional kernels. Extensive empirical experiments with four large-scale datasets demonstrate that DeepMHCII significantly outperformed four state-of-the-art methods under numerous settings, such as five-fold cross-validation, leave one molecule out, validation with independent testing sets, and binding core prediction. All these results with visualization of the predicted binding cores indicate the effectiveness and importance of properly modeling biological facts in deep learning for high performance and knowledge discovery. DeepMHCII is publicly available at https://weilab.sjtu.edu.cn/DeepMHCII/.

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Contribution of T Cell Receptor Alpha and Beta CDR3, MHC Typing, V and J Genes to Peptide Binding Prediction

Frontiers in Immunology ◽

10.3389/fimmu.2021.664514 ◽

2021 ◽

Vol 12 ◽

Author(s):

Ido Springer ◽

Nili Tickotsky ◽

Yoram Louzoun

Keyword(s):

T Cell ◽

T Cell Receptor ◽

Language Processing ◽

Peptide Binding ◽

Cell Receptor ◽

Main Element ◽

Beta Chain ◽

Binding Prediction ◽

Alpha Chain ◽

Peptide Binding Prediction

IntroductionPredicting the binding specificity of T Cell Receptors (TCR) to MHC-peptide complexes (pMHCs) is essential for the development of repertoire-based biomarkers. This affinity may be affected by different components of the TCR, the peptide, and the MHC allele. Historically, the main element used in TCR-peptide binding prediction was the Complementarity Determining Region 3 (CDR3) of the beta chain. However, recently the contribution of other components, such as the alpha chain and the other V gene CDRs has been suggested. We use a highly accurate novel deep learning-based TCR-peptide binding predictor to assess the contribution of each component to the binding.MethodsWe have previously developed ERGO-I (pEptide tcR matchinG predictiOn), a sequence-based T-cell receptor (TCR)-peptide binding predictor that employs natural language processing (NLP) -based methods. We improved it to create ERGO-II by adding the CDR3 alpha segment, the MHC typing, V and J genes, and T cell type (CD4+ or CD8+) as to the predictor. We then estimate the contribution of each component to the prediction.Results and DiscussionERGO-II provides for the first time high accuracy prediction of TCR-peptide for previously unseen peptides. For most tested peptides and all measures of binding prediction accuracy, the main contribution was from the beta chain CDR3 sequence, followed by the beta chain V and J and the alpha chain, in that order. The MHC allele was the least contributing component. ERGO-II is accessible as a webserver at http://tcr2.cs.biu.ac.il/ and as a standalone code at https://github.com/IdoSpringer/ERGO-II.

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