Journal
ACS MACRO LETTERS
Volume 10, Issue 3, Pages 327-340Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acsmacrolett.0c00885
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Funding
- Machine Learning in the Chemical Sciences and Engineering program of The Camille and Henry Dreyfus Foundation
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The design of synthetic proteins with desired functions is a long-standing goal in biomolecular science, and data-driven models provide predictive maps between protein sequence and function for efficient search. The applications of machine learning and deep learning in protein engineering platforms have led to recent successes and offer promising computational methodologies for the future.
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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