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Latent generative landscapes as maps of functional diversity in protein sequence space

Author

Listed:
  • Cheyenne Ziegler

    (University of Texas at Dallas)

  • Jonathan Martin

    (University of Texas at Dallas)

  • Claude Sinner

    (University of Texas at Dallas)

  • Faruck Morcos

    (University of Texas at Dallas
    University of Texas at Dallas
    University of Texas at Dallas)

Abstract

Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.

Suggested Citation

  • Cheyenne Ziegler & Jonathan Martin & Claude Sinner & Faruck Morcos, 2023. "Latent generative landscapes as maps of functional diversity in protein sequence space," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37958-z
    DOI: 10.1038/s41467-023-37958-z
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    References listed on IDEAS

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    1. Hugo Jacquin & Amy Gilson & Eugene Shakhnovich & Simona Cocco & Rémi Monasson, 2016. "Benchmarking Inverse Statistical Approaches for Protein Structure and Design with Exactly Solvable Models," PLOS Computational Biology, Public Library of Science, vol. 12(5), pages 1-18, May.
    2. Kathryn Tunyasuvunakool & Jonas Adler & Zachary Wu & Tim Green & Michal Zielinski & Augustin Žídek & Alex Bridgland & Andrew Cowie & Clemens Meyer & Agata Laydon & Sameer Velankar & Gerard J. Kleywegt, 2021. "Highly accurate protein structure prediction for the human proteome," Nature, Nature, vol. 596(7873), pages 590-596, August.
    3. Xian-Li Jiang & Rey P. Dimas & Clement T. Y. Chan & Faruck Morcos, 2021. "Coevolutionary methods enable robust design of modular repressors by reestablishing intra-protein interactions," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    4. Jonathan Frazer & Pascal Notin & Mafalda Dias & Aidan Gomez & Joseph K. Min & Kelly Brock & Yarin Gal & Debora S. Marks, 2021. "Disease variant prediction with deep generative models of evolutionary data," Nature, Nature, vol. 599(7883), pages 91-95, November.
    5. Qin Zhou & Nikesh Kunder & José Alberto De la Paz & Alexandra E. Lasley & Vandita D. Bhat & Faruck Morcos & Zachary T. Campbell, 2018. "Global pairwise RNA interaction landscapes reveal core features of protein recognition," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    6. John Jumper & Richard Evans & Alexander Pritzel & Tim Green & Michael Figurnov & Olaf Ronneberger & Kathryn Tunyasuvunakool & Russ Bates & Augustin Žídek & Anna Potapenko & Alex Bridgland & Clemens Me, 2021. "Highly accurate protein structure prediction with AlphaFold," Nature, Nature, vol. 596(7873), pages 583-589, August.
    7. Alex Hawkins-Hooker & Florence Depardieu & Sebastien Baur & Guillaume Couairon & Arthur Chen & David Bikard, 2021. "Generating functional protein variants with variational autoencoders," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-23, February.
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