The generative capacity of probabilistic protein sequence models

Francisco McGee, Sandro Hauri, Quentin Novinger, Slobodan Vucetic, Ronald M. Levy, Vincenzo Carnevale () and Allan Haldane ()
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Francisco McGee: Temple University
Sandro Hauri: Temple University
Quentin Novinger: Temple University
Slobodan Vucetic: Temple University
Ronald M. Levy: Temple University
Vincenzo Carnevale: Temple University
Allan Haldane: Temple University

Nature Communications, 2021, vol. 12, issue 1, 1-14

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26529-9

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DOI: 10.1038/s41467-021-26529-9

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