Bioscience Evidence 2024, Vol.14, No.3, 131-142 http://bioscipublisher.com/index.php/be 138 by region, potentially complicating the commercialization of evolved enzymes. Ethical considerations also include the potential misuse of synthetic biology tools for harmful purposes, highlighting the need for responsible research practices and robust regulatory mechanisms (Li et al., 2017). Figure 3 Schematic illustration of the in vivo directed evolution workflow (Adopted from Jensen et al., 2020) Image caption: A. Schematic illustration of the 3-stepcis, cis-muconic acid pathway, comprising heterologous expression of PaAroZ, KpAroY subunits (B, D, and Ciso), as well as CaCatA and overexpression of Tkl1. B. Schematic illustration of the parental strain (Sc-105, see Table S5) used for in vivo directed evolution of thecis,cis-muconic acid pathway enzymes KpAroY. BandKpAroY. Ciso in yeast cells. The strain replicates and expresses the biosensor, all cis, cis-muconic acid pathway enzymes except KpAroY. B and KpAroY. Ciso, and the variant error-prone TP-DNAP (expressed from AR-Ec633, see Table S4) from the nucleus. All components required for OrthoRep replication and transcription are encoded on p2, whereas, genes encod-ing KpAroY. B and KpAroY. Ciso are expressed from p1.C. Schematic illustration of the in vivo directed evolution workflow showing the passag-ing regimes of the parental strain undergoing (i) the five consecutive rounds of OrthoRep coupled with biosensor-based selection or (ii) fifteenbulk passages to effect drift without biosensor-based selection (Adopted from Jensen et al., 2020) 8 Future Perspectives 8.1 Emerging technologies in synthetic biology The rapid advancements in genome editing technologies, such as CRISPR-Cas9, have significantly enhanced the ability to modify and optimize enzyme functions. These tools allow for precise alterations in the genetic code, facilitating the creation of enzymes with improved catalytic properties. The integration of genome editing with directed evolution has shown promising results in developing artificial enzymes with novel functions and enhanced efficiency (Turner, 2009; Cao et al., 2021; Mariz et al., 2021). This synergy between genome editing and directed evolution is expected to continue driving innovations in enzyme engineering, enabling the production of biocatalysts tailored for specific industrial applications. Artificial intelligence (AI) and machine learning (ML) are revolutionizing the field of enzyme design by providing powerful tools for predicting enzyme-substrate interactions and identifying beneficial mutations. AI algorithms can analyze vast datasets to uncover patterns and relationships that are not immediately apparent through traditional methods. This computational approach complements experimental techniques, reducing the time and cost associated with enzyme optimization. Recent studies have demonstrated the successful application of AI in designing enzymes with enhanced catalytic activities and specificities, paving the way for more efficient and targeted biocatalysts (Markel et al., 2019; Planas-Iglesias et al., 2021; Boukid et al., 2023).
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