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Protein Sequence Design with a Learned Potential

View ORCID ProfileNamrata Anand-Achim, View ORCID ProfileRaphael R. Eguchi, Irimpan I. Mathews, Carla P. Perez, Alexander Derry, View ORCID ProfileRuss B. Altman, View ORCID ProfilePo-Ssu Huang
doi: https://doi.org/10.1101/2020.01.06.895466
Namrata Anand-Achim
1Department of Bioengineering, Stanford University
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Raphael R. Eguchi
2Department of Biochemistry, Stanford University
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Irimpan I. Mathews
3Stanford Synchrotron Radiation Lightsource, Stanford University
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Carla P. Perez
4Biophysics Program, Stanford University
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Alexander Derry
5Department of Biomedical Data Science, Stanford University
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Russ B. Altman
6Departments of Bioengineering, Genetics, and Medicine, Stanford University
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Po-Ssu Huang
7Department of Bioengineering, Stanford University
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  • For correspondence: possu@stanford.edu
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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. 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 the in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • namrataa{at}stanford.edu, reguchi{at}stanford.edu, iimathew{at}slac.stanford.edu, cperez8{at}stanford.edu, aderry{at}stanford.edu, russ.altman{at}stanford.edu, possu{at}stanford.edu

  • New experimental validation results

  • https://drive.google.com/drive/folders/1gBfu5LG8-kp9o7qBMkCdBSRCd0E8R6Te?usp=sharing

  • https://github.com/ProteinDesignLab/protein_seq_des

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted March 02, 2021.
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Protein Sequence Design with a Learned Potential
Namrata Anand-Achim, Raphael R. Eguchi, Irimpan I. Mathews, Carla P. Perez, Alexander Derry, Russ B. Altman, Po-Ssu Huang
bioRxiv 2020.01.06.895466; doi: https://doi.org/10.1101/2020.01.06.895466
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Protein Sequence Design with a Learned Potential
Namrata Anand-Achim, Raphael R. Eguchi, Irimpan I. Mathews, Carla P. Perez, Alexander Derry, Russ B. Altman, Po-Ssu Huang
bioRxiv 2020.01.06.895466; doi: https://doi.org/10.1101/2020.01.06.895466

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