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Deciphering protein evolution and fitness landscapes with latent space models

Author

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  • Xinqiang Ding

    (University of Michigan)

  • Zhengting Zou

    (University of Michigan)

  • Charles L. Brooks III

    (University of Michigan
    University of Michigan
    University of Michigan)

Abstract

Protein sequences contain rich information about protein evolution, fitness landscapes, and stability. Here we investigate how latent space models trained using variational auto-encoders can infer these properties from sequences. Using both simulated and real sequences, we show that the low dimensional latent space representation of sequences, calculated using the encoder model, captures both evolutionary and ancestral relationships between sequences. Together with experimental fitness data and Gaussian process regression, the latent space representation also enables learning the protein fitness landscape in a continuous low dimensional space. Moreover, the model is also useful in predicting protein mutational stability landscapes and quantifying the importance of stability in shaping protein evolution. Overall, we illustrate that the latent space models learned using variational auto-encoders provide a mechanism for exploration of the rich data contained in protein sequences regarding evolution, fitness and stability and hence are well-suited to help guide protein engineering efforts.

Suggested Citation

  • Xinqiang Ding & Zhengting Zou & Charles L. Brooks III, 2019. "Deciphering protein evolution and fitness landscapes with latent space models," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-13633-0
    DOI: 10.1038/s41467-019-13633-0
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    Cited by:

    1. Ziyi Zhou & Liang Zhang & Yuanxi Yu & Banghao Wu & Mingchen Li & Liang Hong & Pan Tan, 2024. "Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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