AttnTAP: A Dual-input Framework Incorporating the Attention Mechanism for Accurately Predicting TCR-peptide Binding
T-cell receptors (TCRs) are formed by random recombination of genomic precursor elements, some of which mediate the recognition of cancer-associated antigens. Due to the complicated process of T-cell immune response and limited biological empirical evidence, the practical strategy for identifying TC...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.942491/full |
| _version_ | 1817999185966792704 |
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| author | Ying Xu Xinyang Qian Yao Tong Fan Li Ke Wang Ke Wang Xuanping Zhang Tao Liu Tao Liu Jiayin Wang |
| author_facet | Ying Xu Xinyang Qian Yao Tong Fan Li Ke Wang Ke Wang Xuanping Zhang Tao Liu Tao Liu Jiayin Wang |
| author_sort | Ying Xu |
| collection | DOAJ |
| description | T-cell receptors (TCRs) are formed by random recombination of genomic precursor elements, some of which mediate the recognition of cancer-associated antigens. Due to the complicated process of T-cell immune response and limited biological empirical evidence, the practical strategy for identifying TCRs and their recognized peptides is the computational prediction from population and/or individual TCR repertoires. In recent years, several machine/deep learning-based approaches have been proposed for TCR-peptide binding prediction. However, the predictive performances of these methods can be further improved by overcoming several significant flaws in neural network design. The interrelationship between amino acids in TCRs is critical for TCR antigen recognition, which was not properly considered by the existing methods. They also did not pay more attention to the amino acids that play a significant role in antigen-binding specificity. Moreover, complex networks tended to increase the risk of overfitting and computational costs. In this study, we developed a dual-input deep learning framework, named AttnTAP, to improve the TCR-peptide binding prediction. It used the bi-directional long short-term memory model for robust feature extraction of TCR sequences, which considered the interrelationships between amino acids and their precursors and postcursors. We also introduced the attention mechanism to give amino acids different weights and pay more attention to the contributing ones. In addition, we used the multilayer perceptron model instead of complex networks to extract peptide features to reduce overfitting and computational costs. AttnTAP achieved high areas under the curves (AUCs) in TCR-peptide binding prediction on both balanced and unbalanced datasets (higher than 0.838 on McPAS-TCR and 0.908 on VDJdb). Furthermore, it had the highest average AUCs in TPP-I and TPP-II tasks compared with the other five popular models (TPP-I: 0.84 on McPAS-TCR and 0.894 on VDJdb; TPP-II: 0.837 on McPAS-TCR and 0.893 on VDJdb). In conclusion, AttnTAP is a reasonable and practical framework for predicting TCR-peptide binding, which can accelerate identifying neoantigens and activated T cells for immunotherapy to meet urgent clinical needs. |
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| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-14T03:04:53Z |
| publishDate | 2022-08-01 |
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| series | Frontiers in Genetics |
| spelling | doaj.art-75eb91435f7641ebbd3adc8bca17789b2022-12-22T02:15:48ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-08-011310.3389/fgene.2022.942491942491AttnTAP: A Dual-input Framework Incorporating the Attention Mechanism for Accurately Predicting TCR-peptide BindingYing Xu0Xinyang Qian1Yao Tong2Fan Li3Ke Wang4Ke Wang5Xuanping Zhang6Tao Liu7Tao Liu8Jiayin Wang9Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaDepartment of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaDepartment of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaDepartment of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaDepartment of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaGeneplus Beijing Institute, Beijing, ChinaDepartment of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaDepartment of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaGeneplus Beijing Institute, Beijing, ChinaDepartment of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaT-cell receptors (TCRs) are formed by random recombination of genomic precursor elements, some of which mediate the recognition of cancer-associated antigens. Due to the complicated process of T-cell immune response and limited biological empirical evidence, the practical strategy for identifying TCRs and their recognized peptides is the computational prediction from population and/or individual TCR repertoires. In recent years, several machine/deep learning-based approaches have been proposed for TCR-peptide binding prediction. However, the predictive performances of these methods can be further improved by overcoming several significant flaws in neural network design. The interrelationship between amino acids in TCRs is critical for TCR antigen recognition, which was not properly considered by the existing methods. They also did not pay more attention to the amino acids that play a significant role in antigen-binding specificity. Moreover, complex networks tended to increase the risk of overfitting and computational costs. In this study, we developed a dual-input deep learning framework, named AttnTAP, to improve the TCR-peptide binding prediction. It used the bi-directional long short-term memory model for robust feature extraction of TCR sequences, which considered the interrelationships between amino acids and their precursors and postcursors. We also introduced the attention mechanism to give amino acids different weights and pay more attention to the contributing ones. In addition, we used the multilayer perceptron model instead of complex networks to extract peptide features to reduce overfitting and computational costs. AttnTAP achieved high areas under the curves (AUCs) in TCR-peptide binding prediction on both balanced and unbalanced datasets (higher than 0.838 on McPAS-TCR and 0.908 on VDJdb). Furthermore, it had the highest average AUCs in TPP-I and TPP-II tasks compared with the other five popular models (TPP-I: 0.84 on McPAS-TCR and 0.894 on VDJdb; TPP-II: 0.837 on McPAS-TCR and 0.893 on VDJdb). In conclusion, AttnTAP is a reasonable and practical framework for predicting TCR-peptide binding, which can accelerate identifying neoantigens and activated T cells for immunotherapy to meet urgent clinical needs.https://www.frontiersin.org/articles/10.3389/fgene.2022.942491/fullT-cell receptorTCR-peptide binding predictiondeep learning frameworkBiLSTM modelattention mechanism |
| spellingShingle | Ying Xu Xinyang Qian Yao Tong Fan Li Ke Wang Ke Wang Xuanping Zhang Tao Liu Tao Liu Jiayin Wang AttnTAP: A Dual-input Framework Incorporating the Attention Mechanism for Accurately Predicting TCR-peptide Binding Frontiers in Genetics T-cell receptor TCR-peptide binding prediction deep learning framework BiLSTM model attention mechanism |
| title | AttnTAP: A Dual-input Framework Incorporating the Attention Mechanism for Accurately Predicting TCR-peptide Binding |
| title_full | AttnTAP: A Dual-input Framework Incorporating the Attention Mechanism for Accurately Predicting TCR-peptide Binding |
| title_fullStr | AttnTAP: A Dual-input Framework Incorporating the Attention Mechanism for Accurately Predicting TCR-peptide Binding |
| title_full_unstemmed | AttnTAP: A Dual-input Framework Incorporating the Attention Mechanism for Accurately Predicting TCR-peptide Binding |
| title_short | AttnTAP: A Dual-input Framework Incorporating the Attention Mechanism for Accurately Predicting TCR-peptide Binding |
| title_sort | attntap a dual input framework incorporating the attention mechanism for accurately predicting tcr peptide binding |
| topic | T-cell receptor TCR-peptide binding prediction deep learning framework BiLSTM model attention mechanism |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.942491/full |
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