Bioscience Evidence 2024, Vol.14, No.3, 131-142 http://bioscipublisher.com/index.php/be 134 Figure 1 General strategy for directed evolution and selected experimental methods (Adopted from Zeymer and Hilvert, 2018) Image caption: Protein catalysts are optimized using iterative cycles of gene diversification by mutagenesis (1), gene expression (2), screening or selection for improved variants (3), and subsequent gene amplification (4). Abbreviations: FACS, fluorescence-activated cell sorting; HPLC, high-performance liquid chromatography; IVC, in vitro compartmentalization; NMR, nuclear magnetic resonance; μSCALE, microcapillary single-cell analysis and laser extraction; PACE, phage-assisted continuous evolution; PCR, polymerase chain reaction (Adopted from Zeymer and Hilvert, 2018) 4.3 Gene synthesis and assembly techniques Advancements in gene synthesis and assembly techniques have significantly contributed to the field of directed evolution. These techniques enable the de novo synthesis of entire genes or genomes, allowing researchers to explore a vast sequence space. DNA synthesis and assembly methods, such as in vitro recombination and high-throughput screening, have matured and are widely adopted in synthetic biology (Kang et al., 2015). These methods facilitate the rapid generation of genetic diversity, which is crucial for the semi-rational engineering of enzymes and genomes. The ability to synthesize and assemble genes with high precision and efficiency is a key enabler of directed evolution experiments. 4.4 Computational tools for enzyme design Computational tools, including in silico modeling and machine learning algorithms, play a pivotal role in the design and optimization of enzymes. These tools can predict the effects of mutations on enzyme structure and function, guiding the selection of beneficial variants. Machine learning-assisted approaches have been integrated into directed evolution workflows to enhance the efficiency of mutagenesis and screening processes (Iqbal and Sadaf, 2022). By leveraging computational models, researchers can better navigate the sequence diversity and identify promising candidates for further experimental validation. The integration of computational tools with experimental techniques accelerates the discovery and optimization of enzymes with enhanced catalytic efficiency. In summary, the application of synthetic biology tools, such as CRISPR/Cas systems, recombinant DNA technology, gene synthesis and assembly techniques, and computational tools, has greatly advanced the field of directed evolution. These tools enable the rapid and efficient engineering of enzymes with improved catalytic properties, paving the way for novel biocatalytic applications.
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