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Self-play reinforcement learning guides protein engineering

Nature Machine Intelligence volume 5, pages 845–860 (2023)Cite this article

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An Author Correction to this article was published on 08 August 2023

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Abstract

Designing protein sequences towards desired properties is a fundamental goal of protein engineering, with applications in drug discovery and enzymatic engineering. Machine learning-guided directed evolution has shown success in expediting the optimization cycle and reducing experimental burden. However, efficient sampling in the vast design space remains a challenge. To address this, we propose EvoPlay, a self-play reinforcement learning framework based on the single-player version of AlphaZero. In this work, we mutate a single-site residue as an action to optimize protein sequences, analogous to playing pieces on a chessboard. A policy-value neural network reciprocally interacts with look-ahead Monte Carlo tree search to guide the optimization agent with breadth and depth. We extensively evaluate EvoPlay on a suite of in silico directed evolution tasks over full-length sequences or combinatorial sites using functional surrogates. EvoPlay also supports AlphaFold2 as a structural surrogate to design peptide binders with high affinities, validated by binding assays. Moreover, we harness EvoPlay to prospectively engineer luciferase, resulting in the discovery of variants with 7.8-fold bioluminescence improvement beyond wild type. In sum, EvoPlay holds great promise for facilitating protein design to tackle unmet academic, industrial and clinical needs.

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Fig. 1: Overview of EvoPlay.
Fig. 2: PAB1 and GFP design evaluated by TAPE oracle.
Fig. 3: High-quality peptide binder designed by EvoPlay.
Fig. 4: EvoPlay-assisted MLDE.
Fig. 5: Prospective GLuc engineering.

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Data availability

Datasets used in all benchmark studies have been published previously. The PAB1 (UniProt P04147) and GFP (UniProt P42212) datasets used for EvoPlay surrogate training have been archived97. The four-site library GB1 (UniProt P19909) and PhoQ (UniProt P23837) datasets have been provided98. Peptide and receptor sequences of the WT for 1ssc, 2cnz, 3r7g and 6seo (PDB) have been referenced99. The MSA and logo used in GLuc design are presented in Supplementary Data 1 and Extended Data Fig. 1. Our in-house GLuc library can be found in Supplementary Data 2, and experimentally validated variants proposed by EvoPlay are presented in Supplementary Data 3. Source data are provided with this paper100.

Code availability

EvoPlay is written in Python (v3.8.10) using the PyTorch (v1.12.1) library. The source code is available on GitHub (https://github.com/melobio/EvoPlay) under the GPLv3 license. The doi of the GitHub repository for EvoPlay is provided with the Zenodo link101. The Code Ocean capsule for EvoPlay is also published102.

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Acknowledgements

This research is supported by the Ministry of Science and Technology of the People’s Republic of China’s programme titled ‘National Key Research and Development Program of China’ (2022YFF1202200, 2022YFF1202203) and the Science, Technology and Innovation Commission of Shenzhen Municipality under grant JSGGZD20220822095802006.

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Authors and Affiliations

  1. MGI, Shenzhen, China

    Yi Wang, Hui Tang, Lichao Huang, Lixiang Yang, Feng Mu & Meng Yang

  2. MGI-QingDao, Qingdao, China

    Lulu Pan

  3. Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China

    Huanming Yang

  4. James D. Watson Institute of Genome Sciences, Hangzhou, China

    Huanming Yang

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Contributions

M.Y. conceived the problem and designed all detailed studies. Y.W., H.T., L.H. and L.Y. performed analysis. L.P. conducted luciferase validation experiments. F.M. and H.Y. provided strategic guidance. M.Y. wrote the manuscript and Y.W. made modifications.

Corresponding authors

Correspondence to Feng Mu or Meng Yang.

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Competing interests

29 newly designed and validated GLuc mutants are undergoing patent filing (PCT/CN2023/087445). F.M. declares stock holdings in MGI. The remaining authors declare no competing interests.

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Nature Machine Intelligence thanks Christian Dallago, Dominik Niopek and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Logo indicating the frequencies of amino acids from 30 MSAs of GLuc.

The sequence logo represents each column of the MSA alignment as a stack of letters, where the height of each letter is proportional to its frequency, and the overall height of each stack is proportional to the entropy of the letter distribution.

Supplementary information

Supplementary Information

Supplementary Notes, Figs. 1–15 and Tables 1–19.

Reporting Summary

Supplementary Data 1

30-sequence MSA including the WT GLuc sequence, obtained from the online DeepMSA v1 pipeline.

Supplementary Data 2

Starting library containing 164 variants constructed from random in-house mutant libraries—L2, L3 and L4. This constructed library was used for EvoPlay surrogate training and was the starting sequence pool for EvoPlay optimization.

Supplementary Data 3

Top predicted variants, validated by bioluminescence assay, resulting from ten independent runs of EvoPlay.

Source data

Source Data Fig. 2

Source data for Fig. 2a–d.

Source Data Fig. 3

Source data for Fig. 3a–g,i.

Source Data Fig. 4

Source data for Fig. 4b–d.

Source Data Fig. 5

Source data for Fig. 5b,c.

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Wang, Y., Tang, H., Huang, L. et al. Self-play reinforcement learning guides protein engineering. Nat Mach Intell 5, 845–860 (2023). https://doi.org/10.1038/s42256-023-00691-9

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