"Multiagent" Screening Improves Directed Enzyme Evolution by Identifying Epistatic Mutations

A High-Throughput Cell-Free Enzyme Screening System Using Redox-Responsive Hydrogel Beads as Artificial Compartments

We have developed a rapid, simple, and high-throughput screening system for recombinant enzymes using disulfide-bonded hydrogel beads (HBs) produced via a microfluidic method. These redox-responsive HBs were compatible with the biosynthesis of enzyme mutants via cell-free protein synthesis, fluorescent staining through an enzymatic reaction, and genetic information recovery after fluorescence-activated droplet sorting (FADS). The expression of microbial transglutaminase zymogen (MTGz) using cell-free protein synthesis and the cross-linking-reactivity-based staining of HBs with a fluorescent product were validated. Next-generation sequencing (NGS) analysis of the genes recovered from highly fluorescent HBs identified novel mutation sites (N25 and N27) in the propeptide domain. The introduction of these mutations allowed for the design of an engineered active MTGz, demonstrating the potential of HBs as artificial compartments for the FADS-based selection of enzymes that catalyze peptide and protein cross-linking reactions.

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its "fitness" for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.

Current State-of-the-Art Toward Chemoenzymatic Synthesis of Polyketide Natural Products

Polyketide natural products have significant promise as pharmaceutical targets for human health and as molecular tools to probe disease and complex biological systems. While the biosynthetic logic of polyketide synthases (PKS) is well-understood, biosynthesis of designer polyketides remains challenging due to several bottlenecks, including substrate specificity constraints, disrupted protein-protein interactions, and protein solubility and folding issues. Focusing on substrate specificity, PKSs are typically interrogated using synthetic thioesters. PKS assembly lines and their products offer a wealth of information when studied in a chemoenzymatic fashion. This review provides an overview of the past two decades of polyketide chemoenzymatic synthesis and their contributions to the field of chemical biology. These synthetic strategies have successfully yielded natural product derivatives while providing critical insights into enzymatic promiscuity and mechanistic activity.

Optimizing the strain engineering process for industrial-scale production of bio-based molecules

Biomanufacturing could contribute as much as ${\$}$30 trillion to the global economy by 2030. However, the success of the growing bioeconomy depends on our ability to manufacture high-performing strains in a time- and cost-effective manner. The Design-Build-Test-Learn (DBTL) framework has proven to be an effective strain engineering approach. Significant improvements have been made in genome engineering, genotyping, and phenotyping throughput over the last couple of decades that have greatly accelerated the DBTL cycles. However, to achieve a radical reduction in strain development time and cost, we need to look at the strain engineering process through a lens of optimizing the whole cycle, as opposed to simply increasing throughput at each stage. We propose an approach that integrates all 4 stages of the DBTL cycle and takes advantage of the advances in computational design, high-throughput genome engineering, and phenotyping methods, as well as machine learning tools for making predictions about strain scale-up performance. In this perspective, we discuss the challenges of industrial strain engineering, outline the best approaches to overcoming these challenges, and showcase examples of successful strain engineering projects for production of heterologous proteins, amino acids, and small molecules, as well as improving tolerance, fitness, and de-risking the scale-up of industrial strains.