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The generative capacity of probabilistic protein sequence models

Author

Listed:
  • Francisco McGee

    (Temple University
    Temple University
    Temple University)

  • Sandro Hauri

    (Temple University
    Temple University)

  • Quentin Novinger

    (Temple University
    Temple University)

  • Slobodan Vucetic

    (Temple University
    Temple University)

  • Ronald M. Levy

    (Temple University
    Temple University
    Temple University
    Temple University)

  • Vincenzo Carnevale

    (Temple University
    Temple University)

  • Allan Haldane

    (Temple University
    Temple University)

Abstract

Potts models and variational autoencoders (VAEs) have recently gained popularity as generative protein sequence models (GPSMs) to explore fitness landscapes and predict mutation effects. Despite encouraging results, current model evaluation metrics leave unclear whether GPSMs faithfully reproduce the complex multi-residue mutational patterns observed in natural sequences due to epistasis. Here, we develop a set of sequence statistics to assess the “generative capacity” of three current GPSMs: the pairwise Potts Hamiltonian, the VAE, and the site-independent model. We show that the Potts model’s generative capacity is largest, as the higher-order mutational statistics generated by the model agree with those observed for natural sequences, while the VAE’s lies between the Potts and site-independent models. Importantly, our work provides a new framework for evaluating and interpreting GPSM accuracy which emphasizes the role of higher-order covariation and epistasis, with broader implications for probabilistic sequence models in general.

Suggested Citation

  • Francisco McGee & Sandro Hauri & Quentin Novinger & Slobodan Vucetic & Ronald M. Levy & Vincenzo Carnevale & Allan Haldane, 2021. "The generative capacity of probabilistic protein sequence models," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26529-9
    DOI: 10.1038/s41467-021-26529-9
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    References listed on IDEAS

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    1. Michael Socolich & Steve W. Lockless & William P. Russ & Heather Lee & Kevin H. Gardner & Rama Ranganathan, 2005. "Evolutionary information for specifying a protein fold," Nature, Nature, vol. 437(7058), pages 512-518, September.
    2. 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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