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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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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