Abstract
T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR recognition and constitutes a key step for immune response. Optimizing TCR sequences for TCR recognition represents a fundamental step towards the development of personalized treatments to trigger immune responses killing cancerous or virus-infected cells. In this paper, we formulated the search for these optimized TCRs as a reinforcement learning (\(\mathop {\texttt{RL}}\limits \)) problem, and presented a framework \(\mathop {\texttt{TCRPPO}}\limits \) with a mutation policy using proximal policy optimization. \(\mathop {\texttt{TCRPPO}}\limits \) mutates TCRs into effective ones that can recognize given peptides. \(\mathop {\texttt{TCRPPO}}\limits \) leverages a reward function that combines the likelihoods of mutated sequences being valid TCRs measured by a new scoring function based on deep autoencoders, with the probabilities of mutated sequences recognizing peptides from a peptide-TCR interaction predictor. We compared \(\mathop {\texttt{TCRPPO}}\limits \) with multiple baseline methods and demonstrated that \(\mathop {\texttt{TCRPPO}}\limits \) significantly outperforms all the baseline methods to generate positive binding and valid TCRs. These results demonstrate the potential of \(\mathop {\texttt{TCRPPO}}\limits \) for both precision immunotherapy and peptide-recognizing TCR motif discovery.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The code is available at https://github.com/ninglab/TCRPPO.
- 2.
References
Abati, D., Porrello, A., Calderara, S., Cucchiara, R.: Latent space autoregression for novelty detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
Angermüller, C., Dohan, D., Belanger, D., Deshpande, R., Murphy, K., Colwell, L.: Model-based reinforcement learning for biological sequence design. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, 26–30 April 2020 (2020)
Arnold, F.H.: Design by directed evolution. Acc. Chem. Res. 31(3), 125–131 (1998)
Cai, M., Bang, S., Zhang, P., Lee, H.: ATM-TCR: TCR-epitope binding affinity prediction using a multi-head self-attention model. Front. Immunol. 13 (2022)
Chen, S.Y., Yue, T., Lei, Q., Guo, A.Y.: TCRdb: a comprehensive database for t-cell receptor sequences with powerful search function. Nucleic Acids Res. 49(D1), D468–D474 (2020)
Chen, Z., Min, M.R., Ning, X.: Ranking-based convolutional neural network models for peptide-MHC class i binding prediction. Front. Mol. Biosci. 8 (2021)
Coulom, R.: Efficient selectivity and backup operators in Monte-Carlo tree search. In: van den Herik, H.J., Ciancarini, P., Donkers, H.H.L.M.J. (eds.) CG 2006. LNCS, vol. 4630, pp. 72–83. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75538-8_7
Craiu, A., Akopian, T., Goldberg, A., Rock, K.L.: Two distinct proteolytic processes in the generation of a major histocompatibility complex class i-presented peptide. Proc. Natl. Acad. Sci. 94(20), 10850–10855 (1997)
Esfahani, K., Roudaia, L., Buhlaiga, N., Rincon, S.D., Papneja, N., Miller, W.: A review of cancer immunotherapy: From the past, to the present, to the future. Curr. Oncol. 27(12), 87–97 (2020)
Glanville, J., et al.: Identifying specificity groups in the t cell receptor repertoire. Nature 547(7661), 94–98 (2017)
Gómez-Bombarelli, R., et al.: Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Sci. 4(2), 268–276 (2018)
González, J., Longworth, J., James, D.C., Lawrence, N.D.: Bayesian optimization for synthetic gene design (2015)
Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5–6), 602–610 (2005)
Gupta, A., Zou, J.: Feedback GAN for DNA optimizes protein functions. Nat. Mach. Intell. 1(2), 105–111 (2019)
Hou, X., et al.: Analysis of the repertoire features of TCR beta chain CDR3 in human by high-throughput sequencing. Cell. Physiol. Biochem. 39(2), 651–667 (2016)
Killoran, N., Lee, L.J., Delong, A., Duvenaud, D., Frey, B.J.: Generating and designing DNA with deep generative models. CoRR abs/1712.06148 (2017)
La Gruta, N.L., Gras, S., Daley, S.R., Thomas, P.G., Rossjohn, J.: Understanding the drivers of MHC restriction of t cell receptors. Nat. Rev. Immunol. 18(7), 467–478 (2018)
Mnih, V., et al.: Asynchronous methods for deep reinforcement learning. In: Proceedings of The 33rd International Conference on Machine Learning, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (20–22 June 2016)
Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: a review. ACM Comput. Surv. 54(2) (2021)
Platt, J.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classif. 10(3), 61–74 (1999)
Rossjohn, J., Gras, S., Miles, J.J., Turner, S.J., Godfrey, D.I., McCluskey, J.: T cell antigen receptor recognition of antigen-presenting molecules. Annu. Rev. Immunol. 33, 169–200 (2015)
Sadelain, M., Rivière, I., Riddell, S.: Therapeutic t cell engineering. Nature 545(7655), 423–431 (2017)
Schulman, J., Moritz, P., Levine, S., Jordan, M., Abbeel, P.: High-dimensional continuous control using generalized advantage estimation. In: Proceedings of the International Conference on Learning Representations (ICLR) (2016)
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. CoRR abs/1707.06347 (2017)
Shugay, M., Bagaev, D.V., Zvyagin, I.V., Vroomans, R.M., Crawford, J.C., Dolton, G., et al.: VDJdb: a curated database of t-cell receptor sequences with known antigen specificity. Nucleic Acids Res. 46(D1), D419–D427 (2017)
Skwark, M.J., et al.: Designing a prospective COVID-19 therapeutic with reinforcement learning. CoRR abs/2012.01736 (2020)
Snover, M., Dorr, B., Schwartz, R., Micciulla, L., Makhoul, J.: A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp. 223–231. Cambridge, Massachusetts, USA (8–12 August 2006)
Springer, I., Besser, H., Tickotsky-Moskovitz, N., Dvorkin, S., Louzoun, Y.: Prediction of specific TCR-peptide binding from large dictionaries of TCR-peptide pairs. Front. Immunol. 11 (2020)
Tickotsky, N., Sagiv, T., Prilusky, J., Shifrut, E., Friedman, N.: McPAS-TCR: a manually curated catalogue of pathology-associated t cell receptor sequences. Bioinformatics 33(18), 2924–2929 (2017)
Verdegaal, E.M.E., et al.: Neoantigen landscape dynamics during human melanoma–t cell interactions. Nature 536(7614), 91–95 (2016)
Waldman, A.D., Fritz, J.M., Lenardo, M.J.: A guide to cancer immunotherapy: from t cell basic science to clinical practice. Nat. Rev. Immunol. 20(11), 651–668 (2020)
Weber, A., Born, J., Martínez, M.R.: TITAN: T-cell receptor specificity prediction with bimodal attention networks. Bioinformatics 37(Supplement_1), i237–i244 (2021)
Whitley, D.: A genetic algorithm tutorial. Stat. Comput. 4(2) (1994)
Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 1(2), 270–280 (1989)
Zong, B., et al.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations (2018)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, Z., Min, M.R., Guo, H., Cheng, C., Clancy, T., Ning, X. (2023). T-Cell Receptor Optimization with Reinforcement Learning and Mutation Polices for Precision Immunotherapy. In: Tang, H. (eds) Research in Computational Molecular Biology. RECOMB 2023. Lecture Notes in Computer Science(), vol 13976. Springer, Cham. https://doi.org/10.1007/978-3-031-29119-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-29119-7_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-29118-0
Online ISBN: 978-3-031-29119-7
eBook Packages: Computer ScienceComputer Science (R0)