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Evolutionary Context-Integrated Deep Sequence Modeling for Protein Engineering

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Research in Computational Molecular Biology (RECOMB 2020)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12074))

Abstract

Protein engineering seeks to design proteins with improved or novel functions. Compared to rational design and directed evolution approaches, machine learning-guided approaches traverse the fitness landscape more effectively and hold the promise for accelerating engineering and reducing the experimental cost and effort.

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Acknowledgments

J.P. acknowledges the support from the Sloan Research Fellowship and the NSF CAREER Award. Y. Luo acknowledges the support from the CompGen Fellowship.

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Authors and Affiliations

  1. Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, USA

    Yunan Luo, Hantian Ding, Yufeng Su, Yang Liu, Wesley Wei Qian & Jian Peng

  2. Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, USA

    Lam Vo & Huimin Zhao

Authors
  1. Yunan Luo
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  2. Lam Vo
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  3. Hantian Ding
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  4. Yufeng Su
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  5. Yang Liu
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  6. Wesley Wei Qian
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  7. Huimin Zhao
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  8. Jian Peng
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Corresponding author

Correspondence to Jian Peng .

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Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Russell Schwartz

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Cite this paper

Luo, Y. et al. (2020). Evolutionary Context-Integrated Deep Sequence Modeling for Protein Engineering. In: Schwartz, R. (eds) Research in Computational Molecular Biology. RECOMB 2020. Lecture Notes in Computer Science(), vol 12074. Springer, Cham. https://doi.org/10.1007/978-3-030-45257-5_30

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  • DOI: https://doi.org/10.1007/978-3-030-45257-5_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-45256-8

  • Online ISBN: 978-3-030-45257-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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