Prediction of MHC II antigen peptide-T cell receptors binding based on foundation model_Journal of Biomedical Engineering

Authors：

XU Minrui ¹ , ZHANG Siwen ³ , LU Manman ² , GAO Yuan ¹ , ZHANG Menghuan ⁴ ,  LIN Yong ¹ ,  XIE Lu ^2,3

1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
2. Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, P. R. China;
3. Shanghai Institute for Biomedical and Pharmaceutical Technologies, Fudan University, Shanghai 200237, P. R. China;
4. School of Life Science and Technology, Tongji University, Shanghai 200092, P. R. China;

Corresponding author：

LIN Yong, Email: yong_lynn@usst.edu.cn; XIE Lu, Email: xielu@sibpt.com

Keywords：

Prediction of MHC Ⅱ antigen peptide-T cell receptors binding; Foundation model; ProtT5 model; Immunotherapy

DOI：

10.7507/1001-5515.202405024

Video：

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Abstract Full text Figures/Tables Video References Cited by

The specific binding of T cell receptors (TCRs) to antigenic peptides plays a key role in the regulation and mediation of the immune process and provides an essential basis for the development of tumour vaccines. In recent years, studies have mainly focused on TCR prediction of major histocompatibility complex (MHC) class I antigens, but TCR prediction of MHC class II antigens has not been sufficiently investigated and there is still much room for improvement. In this study, the combination of MHC class II antigen peptide and TCR prediction was investigated using the ProtT5 grand model to explore its feature extraction capability. In addition, the model was fine-tuned to retain the underlying features of the model, and a feed-forward neural network structure was constructed for fusion to achieve the prediction model. The experimental results showed that the method proposed in this study performed better than the traditional methods, with a prediction accuracy of 0.96 and an AUC of 0.93, which verifies the effectiveness of the model proposed in this paper.

1.	Xie N, Shen G, Gao W, et al. Neoantigens: promising targets for cancer therapy. Signal Transduct Target Ther, 2023, 8(1): 9.
2.	Joglekar A V, Li G. T cell antigen discovery. Nat Methods, 2021, 18(8): 873-880.
3.	Chen J, Zhao B, Lin S, et al. TEPCAM: Prediction of T-cell receptor-epitope binding specificity via interpretable deep learning. Protein Sci, 2024, 33(1): e4841.
4.	Gielis S, Moris P, Bittremieux W, et al. Detection of enriched T cell epitope specificity in full T cell receptor sequence repertoires. Front Immunol, 2019, 10: 2820.
5.	De Neuter N, Bittremieux W, Beirnaert C, et al. On the feasibility of mining CD8+ T cell receptor patterns underlying immunogenic peptide recognition. Immunogenetics, 2018, 70(3): 159-168.
6.	Montemurro A, Schuster V, Povlsen H R, et al. NetTCR-2. 0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data. Commun Biol, 2021, 4(1): 1060.
7.	Springer I, Besser H, Tickotsky-Moskovitz N, et al. Prediction of specific TCR-peptide binding from large dictionaries of TCR-peptide pairs. Front Immunol, 2020, 11: 1803.
8.	Zhang Y, Jian X, Xu L, et al. iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features. Front Genet, 2023, 14: 1141535.
9.	Shugay M, Bagaev D V, Zvyagin I V, et al. VDJdb: a curated database of T-cell receptor sequences with known antigen specificity. Nucleic Acids Res, 2018, 46(D1): D419-D427.
10.	Tickotsky N, Sagiv T, Prilusky J, et al. McPAS-TCR: a manually curated catalogue of pathology-associated T cell receptor sequences. Bioinformatics, 2017, 33(18): 2924-2929.
11.	Chen S Y, Yue T, Lei Q, et al. TCRdb: a comprehensive database for T-cell receptor sequences with powerful search function. Nucleic Acids Res, 2021, 49(D1): D468-D474.
12.	Elnaggar A, Heinzinger M, Dallago C, et al. ProtTrans: Toward understanding the language of life through self-supervised learning. IEEE Trans Pattern Anal Mach Intell, 2022, 44(10): 7112-7127.
13.	刘桂霞, 裴志尧, 宋佳智. 基于深度学习的蛋白质-ATP结合位点预测. 吉林大学学报(工学版), 2022, 52(01): 187-194.
14.	Kidera A, Konishi Y, Oka M, et al. Statistical analysis of the physical properties of the 20 naturally occurring amino acids. J Protein Chem, 1985, 4(1): 23-55.
15.	Bi J, Zheng Y, Yan F, et al. Prediction of epitope-associated TCR by using network topological similarity based on deepwalk. IEEE Access, 2019, 7: 151273-151281.
16.	Xu Y, Qian X, Tong Y, et al. AttnTAP: A dual-input framework incorporating the attention mechanism for accurately predicting TCR-peptide binding. Front Genet, 2022, 13: 942491.
17.	Cai M, Bang S-J, Zhang P, et al. ATM-TCR: TCR-epitope binding affinity prediction using a multi-head self-attention model. Front Immun, 2022, 13.
18.	Alspach E, Lussier D M, Miceli A P, et al. MHC-II neoantigens shape tumour immunity and response to immunotherapy. Nature, 2019, 574(7780): 696-701.
19.	Marty Pyke R, Thompson W K, Salem R M, et al. Evolutionary pressure against MHC class II binding cancer mutations. Cell, 2018, 175(2): 416-428. e13.
20.	王广志, 李雨雨, 谢鹭. 个性化肿瘤新抗原疫苗中抗原肽预测研究进展. 生物化学与生物物理进展, 2019(5): 8.
21.	Lu M, Xu L, Jian X, et al. dbPepNeo2. 0: A database for human tumor neoantigen peptides from mass spectrometry and TCR recognition. Front Immunol, 2022, 13: 855976.
22.	赵静静, 简星星, 王广志, 等. TCR组库及其在肿瘤免疫中的应用前景与挑战. 生命科学, 2021, 33(4): 6.

1. Xie N, Shen G, Gao W, et al. Neoantigens: promising targets for cancer therapy. Signal Transduct Target Ther, 2023, 8(1): 9.
2. Joglekar A V, Li G. T cell antigen discovery. Nat Methods, 2021, 18(8): 873-880.
3. Chen J, Zhao B, Lin S, et al. TEPCAM: Prediction of T-cell receptor-epitope binding specificity via interpretable deep learning. Protein Sci, 2024, 33(1): e4841.
4. Gielis S, Moris P, Bittremieux W, et al. Detection of enriched T cell epitope specificity in full T cell receptor sequence repertoires. Front Immunol, 2019, 10: 2820.
5. De Neuter N, Bittremieux W, Beirnaert C, et al. On the feasibility of mining CD8+ T cell receptor patterns underlying immunogenic peptide recognition. Immunogenetics, 2018, 70(3): 159-168.
6. Montemurro A, Schuster V, Povlsen H R, et al. NetTCR-2. 0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data. Commun Biol, 2021, 4(1): 1060.
7. Springer I, Besser H, Tickotsky-Moskovitz N, et al. Prediction of specific TCR-peptide binding from large dictionaries of TCR-peptide pairs. Front Immunol, 2020, 11: 1803.
8. Zhang Y, Jian X, Xu L, et al. iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features. Front Genet, 2023, 14: 1141535.
9. Shugay M, Bagaev D V, Zvyagin I V, et al. VDJdb: a curated database of T-cell receptor sequences with known antigen specificity. Nucleic Acids Res, 2018, 46(D1): D419-D427.
10. Tickotsky N, Sagiv T, Prilusky J, et al. McPAS-TCR: a manually curated catalogue of pathology-associated T cell receptor sequences. Bioinformatics, 2017, 33(18): 2924-2929.
11. Chen S Y, Yue T, Lei Q, et al. TCRdb: a comprehensive database for T-cell receptor sequences with powerful search function. Nucleic Acids Res, 2021, 49(D1): D468-D474.
12. Elnaggar A, Heinzinger M, Dallago C, et al. ProtTrans: Toward understanding the language of life through self-supervised learning. IEEE Trans Pattern Anal Mach Intell, 2022, 44(10): 7112-7127.
13. 刘桂霞, 裴志尧, 宋佳智. 基于深度学习的蛋白质-ATP结合位点预测. 吉林大学学报(工学版), 2022, 52(01): 187-194.
14. Kidera A, Konishi Y, Oka M, et al. Statistical analysis of the physical properties of the 20 naturally occurring amino acids. J Protein Chem, 1985, 4(1): 23-55.
15. Bi J, Zheng Y, Yan F, et al. Prediction of epitope-associated TCR by using network topological similarity based on deepwalk. IEEE Access, 2019, 7: 151273-151281.
16. Xu Y, Qian X, Tong Y, et al. AttnTAP: A dual-input framework incorporating the attention mechanism for accurately predicting TCR-peptide binding. Front Genet, 2022, 13: 942491.
17. Cai M, Bang S-J, Zhang P, et al. ATM-TCR: TCR-epitope binding affinity prediction using a multi-head self-attention model. Front Immun, 2022, 13.
18. Alspach E, Lussier D M, Miceli A P, et al. MHC-II neoantigens shape tumour immunity and response to immunotherapy. Nature, 2019, 574(7780): 696-701.
19. Marty Pyke R, Thompson W K, Salem R M, et al. Evolutionary pressure against MHC class II binding cancer mutations. Cell, 2018, 175(2): 416-428. e13.
20. 王广志, 李雨雨, 谢鹭. 个性化肿瘤新抗原疫苗中抗原肽预测研究进展. 生物化学与生物物理进展, 2019(5): 8.
21. Lu M, Xu L, Jian X, et al. dbPepNeo2. 0: A database for human tumor neoantigen peptides from mass spectrometry and TCR recognition. Front Immunol, 2022, 13: 855976.
22. 赵静静, 简星星, 王广志, 等. TCR组库及其在肿瘤免疫中的应用前景与挑战. 生命科学, 2021, 33(4): 6.

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