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100th Anniversary of Macromolecular Science Viewpoint: Data-Driven Protein Design

机译：大分子科学观点100周年：数据驱动蛋白质设计

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摘要

The design of synthetic proteins with the desired function is a long-standing goal in biomolecular science, with broad applications in biochemical engineering, agriculture, medicine, and public health. Rational de novo design and experimental directed evolution have achieved remarkable successes but are challenged by the requirement to find functional "needles" in the vast "haystack" of protein sequence space. Data-driven models for fitness landscapes provide a predictive map between protein sequence and function and can prospectively identify functional candidates for experimental testing to greatly improve the efficiency of this search. This Viewpoint reviews the applications of machine learning and, in particular, deep learning as part of data-driven protein engineering platforms. We highlight recent successes, review promising computational methodologies, and provide an outlook on future challenges and opportunities. The article is written for a broad audience comprising both polymer and protein scientists and computer and data scientists interested in an up-to-date review of recent innovations and opportunities in this rapidly evolving field.
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  • 来源
    《ACS Macro Letters》 |2021年第3期|共14页
  • 作者

    Ferguson Andrew L.; Ranganathan Rama;

  • 作者单位

    Univ Chicago Pritzker Sch Mol Engn Chicago IL 60637 USA;

    Univ Chicago Ctr Phys Evolving Syst &

    Biochem &

    Mol Biol Pritzker Sch Mol Engn Chicago IL 60637 USA;

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  • 正文语种 eng
  • 中图分类 有机化学;
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