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Expanding functional protein sequence spaces using generative adversarial networks


De novo protein design for catalysis of any desired chemical reaction is a long-standing goal in protein engineering because of the broad spectrum of technological, scientific and medical applications. However, mapping protein sequence to protein function is currently neither computationally nor experimentally tangible. Here, we develop ProteinGAN, a self-attention-based variant of the generative adversarial network that is able to ‘learn’ natural protein sequence diversity and enables the generation of functional protein sequences. ProteinGAN learns the evolutionary relationships of protein sequences directly from the complex multidimensional amino-acid sequence space and creates new, highly diverse sequence variants with natural-like physical properties. Using malate dehydrogenase (MDH) as a template enzyme, we show that 24% (13 out of 55 tested) of the ProteinGAN-generated and experimentally tested sequences are soluble and display MDH catalytic activity in the tested conditions in vitro, including a highly mutated variant of 106 amino-acid substitutions. ProteinGAN therefore demonstrates the potential of artificial intelligence to rapidly generate highly diverse functional proteins within the allowed biological constraints of the sequence space.

A preprint version of the article is available at bioRxiv.

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Fig. 1: ProteinGAN learns the intrinsic relationships between natural protein sequences.
Fig. 2: ProteinGAN expands the functional MDH sequence space.

Data availability

All training data files, including ProteinGAN running examples, have been deposited to the Zenodo repository and are available at Source data are provided with this paper.

Code availability

The implementation of ProteinGAN can be accessed at


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We thank G. Stonyte, J. Nainys and C. Correia-Melo for comments on the manuscript. We also thank A. Repecka and L. Petkevicius for their valuable and constructive suggestions for improving the model. L.K. and R.M. were supported by the Agency for Science, Innovation and Technology (Lithuania) grant no. 31V-59/(1.78)SU-1687. J.Z. and A.Z. were supported by SciLifeLab fellow programme funding. S.V. was supported by VR starting grant no. 2019-05356. The computations were enabled with resources provided by the Swedish National Infrastructure for Computing (SNIC) at C3SE, partially funded by the Swedish Research Council through grant agreement no. 2018-05973. M. Öhman and T. Svedberg at C3SE are acknowledged for technical assistance in making the code run on Vera C3SE resources.

Author information




D.R. implemented the method, contributed with principal analysis and wrote the first draft. V.J. contributed principal analysis, designed experiments and wrote the first draft. L.K. contributed principal analysis, designed experiments and wrote the first draft. E.R. performed laboratory experiments. I.R. contributed principal analysis and wrote the first draft. J.Z. contributed principal analysis, performed laboratory experiments and wrote the first draft. S.P. performed laboratory experiments. A.L. contributed principal analysis. S.V. contributed principal analysis. W.A. performed laboratory experiments. O.S. contributed principal analysis and supervised the mass spectrometry work. R.M. supervised the study and designed the experiments. M.K.M.E. supervised the study, designed experiments, contributed principal analysis and wrote the manuscript. A.Z. supervised the study, designed experiments, contributed principal analysis, financed the experiments and wrote the manuscript. All authors contributed to writing of the paper and read the final manuscript.

Corresponding author

Correspondence to Aleksej Zelezniak.

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Competing interests

L.K., V.J., D.R., I.R. and R.M. are shareholders of the company Biomatter Designs. The company has submitted a patent application for the technology described in the Article. The other authors declare no competing interests.

Additional information

Peer review information Nature Machine Intelligence thanks Frances Arnold and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Methods, Figs. 1–25 and Tables 1–7.

Supplementary Table 2

Supplementary Table 2. Generated sequences and their identities to the closest real sequence.

Source data

Source Data Fig. 1

SDS–PAGE gels of purified proteins. a, Batch 1, protocol 1 (Methods). b, Batches 2 and 3, protocol 1 (Methods). c, Batch 1, protocol 2. T, total lysate; S, soluble lysate; E, elution after affinity column use. d, Batch 2, protocol 2. e, Batch 3, protocol 2 (Methods). The results are summarized in Supplementary Table 3.

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Repecka, D., Jauniskis, V., Karpus, L. et al. Expanding functional protein sequence spaces using generative adversarial networks. Nat Mach Intell (2021).

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