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Protein sequence design with a learned potential

Author

Listed:
  • Namrata Anand

    (Stanford University)

  • Raphael Eguchi

    (Stanford University)

  • Irimpan I. Mathews

    (Stanford Synchrotron Radiation Lightsource)

  • Carla P. Perez

    (Stanford University)

  • Alexander Derry

    (Stanford University)

  • Russ B. Altman

    (Stanford University
    Stanford University)

  • Po-Ssu Huang

    (Stanford University)

Abstract

The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.

Suggested Citation

  • Namrata Anand & Raphael Eguchi & Irimpan I. Mathews & Carla P. Perez & Alexander Derry & Russ B. Altman & Po-Ssu Huang, 2022. "Protein sequence design with a learned potential," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28313-9
    DOI: 10.1038/s41467-022-28313-9
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    References listed on IDEAS

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    Cited by:

    1. Noelia Ferruz & Steffen Schmidt & Birte Höcker, 2022. "ProtGPT2 is a deep unsupervised language model for protein design," Nature Communications, Nature, vol. 13(1), pages 1-10, December.

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