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Accelerating enzyme discovery and engineering with high-throughput screening
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DOI：10.1039/d4np00031e.png)
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Abstract
En 中文
Covering: up to August 2024Enzymes play an essential role in synthesizing value-added chemicals with high specificity and selectivity. Since enzymes utilize substrates derived from renewable resources, biocatalysis offers a pathway to an efficient bioeconomy with reduced environmental footprint. However, enzymes have evolved over millions of years to meet the needs of their host organisms, which often do not align with industrial requirements. As a result, enzymes frequently need to be tailored for specific industrial applications. Combining enzyme engineering with high-throughput screening has emerged as a key approach for developing novel biocatalysts, but several challenges are yet to be addressed. In this review, we explore emergent strategies and methods for isolating, creating, and characterizing enzymes optimized for bioproduction. We discuss fundamental approaches to discovering and generating enzyme variants and identifying those best suited for specific applications. Additionally, we cover techniques for creating libraries using automated systems and highlight innovative high-throughput screening methods that have been successfully employed to develop novel biocatalysts for natural product synthesis. Recent progress in the DBTL cycle, including machine learning, facilitated enzyme mining for biocatalysis. Automation and standardization of library construction, coupled to high-throughput screening, further accelerates the enzyme discovery process.
Keywords:
CELL-SURFACE DISPLAY
IN-VITRO
DIRECTED EVOLUTION
CRISPR-CAS
MANIPULATION
BACTERIAL
DESIGN
IDENTIFICATION
MICROFLUIDICS
BIOCATALYSIS
Journal
IF:
10.6
Papers:
1.8K
Citations:
1.4W