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Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning

Nature Biomedical Engineering volume 5, pages 600–612 (2021)Cite this article

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Abstract

The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 × 103 variants) expressed in mammalian cells, which typically results in few optimized leads. Here we show that optimized antibody variants can be identified by predicting antigen specificity via deep learning from a massively diverse space of antibody sequences. To produce data for training deep neural networks, we deep-sequenced libraries of the therapeutic antibody trastuzumab (about 1 × 104 variants), expressed in a mammalian cell line through site-directed mutagenesis via CRISPR–Cas9-mediated homology-directed repair, and screened the libraries for specificity to human epidermal growth factor receptor 2 (HER2). We then used the trained neural networks to screen a computational library of approximately 1 × 108 trastuzumab variants and predict the HER2-specific subset (approximately 1 × 106 variants), which can then be filtered for viscosity, clearance, solubility and immunogenicity to generate thousands of highly optimized lead candidates. Recombinant expression and experimental testing of 30 randomly selected variants from the unfiltered library showed that all 30 retained specificity for HER2. Deep learning may facilitate antibody engineering and optimization.

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Fig. 1: Implementing deep learning to predict antibody target specificity.
Fig. 2: Sequence-based analysis of the mutational landscape.
Fig. 3: Deep-learning models accurately predict antigen specificity.
Fig. 4: Neural-network-predicted sequences are experimentally validated to be antigen-specific.
Fig. 5: In silico screening of the predicted binders identifies candidate sequences for further validation.
Fig. 6: Experimental characterization of selected sequences reveals optimal candidates.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are too large to be publicly shared; however, they are available for research purposes from the corresponding author on reasonable request.

Code availability

Deep-learning models were built in Python v3.6.5 using the Keras v2.1.6 Sequential model as a wrapper for TensorFlow v1.8.0. The code and models used to perform the work in this study are available at the following github repository: https://github.com/dahjan/DMS_opt.

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Acknowledgements

We thank the ETH Zurich D-BSSE Single Cell Unit and the ETH Zurich D-BSSE Genomics Facility for support—in particular M. Di Tacchio, A. Gumienny, E. Burcklen and C. Beisel. We also thank the Vendruscolo Laboratory (Cambridge, UK), P. Sormanni in particular, for assistance with implementing the CamSol method on large libraries as well as the group of M. Nielson (DTU, Denmark) for providing an easy-to-use package for MHC class II affinity predictions. Funding was provided by the National Competence Center for Research on Molecular Systems Engineering.

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  1. Derek M. Mason, Simon Friedensohn & Cédric R. Weber

    Present address: deepCDR Biologics, Basel, Switzerland

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  1. Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland

    Derek M. Mason, Simon Friedensohn, Cédric R. Weber, Christian Jordi, Bastian Wagner, Simon M. Meng, Roy A. Ehling, Lucia Bonati, Jan Dahinden & Sai T. Reddy

  2. Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Pablo Gainza & Bruno E. Correia

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Contributions

D.M.M., S.F., C.R.W. and S.T.R. developed the methodology. D.M.M. and S.T.R. designed the experiments and wrote the manuscript. D.M.M., C.R.W., S.F. and J.D. analysed the sequencing data and performed deep-learning analyses. P.G. and B.E.C. designed and performed the structural modelling experiments and analysis. C.J. generated in silico libraries. D.M.M. and R.A.E. performed experiments. B.W., S.M.M. and L.B. performed the cell-line development.

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Correspondence to Sai T. Reddy.

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ETH Zurich has filed for patent protection on the technology described herein, and D.M.M., S.F., C.R.W. and S.T.R. are named as co-inventors on this patent (International Filing Application PCT/IB2020/053370).

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Mason, D.M., Friedensohn, S., Weber, C.R. et al. Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning. Nat Biomed Eng 5, 600–612 (2021). https://doi.org/10.1038/s41551-021-00699-9

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