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Enzyme engineering is limited by the challenge of rapidly generating and using large datasets of sequence-function relationships for predictive design. To address this challenge, we develop a machine learning (ML)-guided platform that integrates cell-free DNA assembly, cell-free gene expression, and functional assays to rapidly map fitness landscapes across protein sequence space and optimize enzymes for multiple, distinct chemical reactions. We apply this platform to engineer amide synthetases by evaluating substrate preference for 1217 enzyme variants in 10,953 unique reactions. We use these data to build augmented ridge regression ML models for predicting amide synthetase variants capable of making 9 small molecule pharmaceuticals. Over these nine compounds, ML-predicted enzyme variants demonstrate 1.6- to 42-fold improved activity relative to the parent. Our ML-guided, cell-free framework promises to accelerate enzyme engineering by enabling iterative exploration of protein sequence space to build specialized biocatalysts in parallel. While machine learning shows promise in expanding protein engineering efforts, its potential is limited by the challenge of gathering large datasets of sequence-function relationships. Here, authors introduce a platform that integrates cell-free DNA assembly and gene expression to accelerate enzyme engineering.

Many industrial chemicals that are produced from fossil resources could be manufactured more sustainably through fermentation. Here we describe the development of a carbon-negative fermentation route to producing the industrially important chemicals acetone and isopropanol from abundant, low-cost waste gas feedstocks, such as industrial emissions and syngas. Using a combinatorial pathway library approach, we first mined a historical industrial strain collection for superior enzymes that we used to engineer the autotrophic acetogen Clostridium autoethanogenum . Next, we used omics analysis, kinetic modeling and cell-free prototyping to optimize flux. Finally, we scaled-up our optimized strains for continuous production at rates of up to ~3 g/L/h and ~90% selectivity. Life cycle analysis confirmed a negative carbon footprint for the products. Unlike traditional production processes, which result in release of greenhouse gases, our process fixes carbon. These results show that engineered acetogens enable sustainable, high-efficiency, high-selectivity chemicals production. We expect that our approach can be readily adapted to a wide range of commodity chemicals. Two industrial chemicals are sustainably produced at large scale by microbial gas fermentation.

Antibody discovery is bottlenecked by the individual expression and evaluation of antigen-specific hits. Here, we address this bottleneck by developing a workflow combining cell-free DNA template generation, cell-free protein synthesis, and binding measurements of antibody fragments in a process that takes hours rather than weeks. We apply this workflow to evaluate 135 previously published antibodies targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), including all 8 antibodies previously granted emergency use authorization for coronavirus disease 2019 (COVID-19), and demonstrate identification of the most potent antibodies. We also evaluate 119 anti-SARS-CoV-2 antibodies from a mouse immunized with the SARS-CoV-2 spike protein and identify neutralizing antibody candidates, including the antibody SC2-3, which binds the SARS-CoV-2 spike protein of all tested variants of concern. We expect that our cell-free workflow will accelerate the discovery and characterization of antibodies for future pandemics and for research, diagnostic, and therapeutic applications more broadly. Antibody discovery is bottlenecked by the individual expression and evaluation of antigen specific hits. Here, the authors build an antibody screening workflow leveraging cell-free protein synthesis that enables expression and evaluation of hundreds of antibody fragments in less than 24 h.

Carbon-negative synthesis of biochemical products has the potential to mitigate global CO 2 emissions. An attractive route to do this is the reverse β-oxidation (r-BOX) pathway coupled to the Wood-Ljungdahl pathway. Here, we optimize and implement r-BOX for the synthesis of C4-C6 acids and alcohols. With a high-throughput in vitro prototyping workflow, we screen 762 unique pathway combinations using cell-free extracts tailored for r-BOX to identify enzyme sets for enhanced product selectivity. Implementation of these pathways into Escherichia coli generates designer strains for the selective production of butanoic acid (4.9 ± 0.1 gL −1 ), as well as hexanoic acid (3.06 ± 0.03 gL −1 ) and 1-hexanol (1.0 ± 0.1 gL −1 ) at the best performance reported to date in this bacterium. We also generate Clostridium autoethanogenum strains able to produce 1-hexanol from syngas, achieving a titer of 0.26 gL −1 in a 1.5 L continuous fermentation. Our strategy enables optimization of r-BOX derived products for biomanufacturing and industrial biotechnology. An attractive route for carbon-negative synthesis of biochemical products is the reverse β-oxidation pathway coupled to the Wood-Ljungdahl pathway. Here the authors use a high-throughput in vitro prototyping workflow to screen 762 unique pathway combinations using cell-free extracts tailored for r-BOX to identify enzyme sets for enhanced product selectivity.

Access to recombinant proteins is vital in basic science and biotechnology research. Cell-free gene expression systems provide one approach to address this need, but widespread utilization remains limited by the cost, complexity, and inconsistency of current platforms. To address these limitations, we carry out a multi-dimensional definitive screening design to reduce the number of reagent components and remove costly secondary energy substrates. From 1,231 different reagent formulations, we discover a simple and reproducible system based on 12 components. The optimized reagent formulation can produce 2.4 ± 0.3 g/L of protein product at the 15-µL scale (~$60/g protein ) and 3.7 ± 0.2 g/L (~$39/g protein ) at the 4-mL scale with oxygen supplementation. This provides an average 95% reduction in cost over previous cell-free reagent formulations. We further show that the optimized reagent formulation can produce nucleoside triphosphates from nitrogenous bases and ribose and that it is robust to failure across batches of cell lysates, users/locations, and in the synthesis of more than 20 different proteins. For example, we demonstrate the production of fifteen therapeutically relevant products, including full-length aglycosylated monoclonal antibodies. We anticipate that our optimized reagent formulation will democratize the use of cell-free systems for protein manufacturing and synthetic biology applications. Recombinant proteins are a crucial product for basic science, biotechnology research, and biomanufacturing. Here, the authors develop a robust, low-cost, and high-yielding cell-free platform for protein production, decreasing cost per gram protein from ~$4,080 to ~$60 for multiple diverse products.

Model-based design of biological parts is a critical goal of synthetic biology, especially for eukaryotes. Here we demonstrate that nucleosome architecture can have a role in defining yeast promoter activity and utilize a computationally-guided approach that can enable both the redesign of endogenous promoter sequences and the de novo design of synthetic promoters. Initially, we use our approach to reprogram native promoters for increased expression and evaluate their performance in various genetic contexts. Increases in expression ranging from 1.5- to nearly 6-fold in a plasmid-based system and up to 16-fold in a genomic context were obtained. Next, we demonstrate that, in a single design cycle, it is possible to create functional, purely synthetic yeast promoters that achieve substantial expression levels (within the top sixth percentile among native yeast promoters). In doing so, this work establishes a unique DNA-level specification of promoter activity and demonstrates predictive design of synthetic parts. Model-based part design is a key step in synthetic biology. Here, the authors report a method for tuning nucleosome architecture in order to strengthen native promoters and facilitate synthetic promoter design in yeast.

Point-of-use diagnostics based on allosteric transcription factors (aTFs) are promising tools for environmental monitoring and human health. However, biosensors relying on natural aTFs rarely exhibit the sensitivity and selectivity needed for real-world applications, and traditional directed evolution struggles to optimize multiple biosensor properties at once. To overcome these challenges, we develop a multi-objective, machine learning (ML)-guided cell-free gene expression workflow for engineering aTF-based biosensors. Our approach rapidly generates high-quality sequence-to-function data, which we transform into an augmented paired dataset to train an ML model using directional labels that capture how aTF mutations alter performance. We apply our workflow to engineer the aTF PbrR as a point-of-use diagnostic for lead contamination in water. We tune the sensitivity of PbrR to sense at the U.S. Environmental Protection Agency (EPA) action level for lead and modify the selectivity away from zinc, a common metal found in water supplies. Finally, we show that the engineered PbrR functions in freeze-dried cell-free reactions, enabling a diagnostic capable of detecting lead in drinking water down to ~5.7 ppb. Our ML-driven, multi-objective framework powered by directional tokens can generalize to other biosensors and proteins, accelerating the development of synthetic biology tools for biotechnology applications. Allosteric transcription factors (aTFs) are promising tools for environmental and human health monitoring. Here the authors develop a multi-objective, machine learning-guided method to engineer an aTF-based portable diagnostic for environment sensing of lead in drinking water at the legal limit.

Post-translational modifications (PTMs) are important for the stability and function of many therapeutic proteins and peptides. Current methods for studying and engineering PTMs are often limited by low-throughput experimental techniques. Here we describe a generalizable, in vitro workflow coupling cell-free gene expression (CFE) with AlphaLISA for the rapid expression and testing of PTM installing proteins. We apply our workflow to two representative classes of peptide and protein therapeutics: ribosomally synthesized and post-translationally modified peptides (RiPPs) and glycoproteins. First, we demonstrate how our workflow can be used to characterize the binding activity of RiPP recognition elements, an important first step in RiPP biosynthesis, and be integrated into a biodiscovery pipeline for computationally predicted RiPP products. Then, we adapt our workflow to study and engineer oligosaccharyltransferases (OSTs) involved in protein glycan coupling technology, leading to the identification of mutant OSTs and sites within a model vaccine carrier protein that enable high efficiency production of glycosylated proteins. We expect that our workflow will accelerate design-build-test-learn cycles for engineering PTMs. Post-translational modifications (PTMs) are important for the stability and function of many therapeutic proteins. Here, the authors develop a high-throughput workflow combining cell-free gene expression with AlphaLISA to rapidly characterize and engineer PTMs on both proteins and peptides.

Synthetic biology allows us to reuse, repurpose, and reconfigure biological systems to address society’s most pressing challenges. Developing biotechnologies in this way requires integrating concepts across disciplines, posing challenges to educating students with diverse expertise. We created a framework for synthetic biology training that deconstructs biotechnologies across scales—molecular, circuit/network, cell/cell-free systems, biological communities, and societal—giving students a holistic toolkit to integrate cross-disciplinary concepts towards responsible innovation of successful biotechnologies. We present this framework, lessons learned, and inclusive teaching materials to allow its adaption to train the next generation of synthetic biologists. Developing biotechnologies to address society’s challenges requires integrating concepts across disciplines, posing challenges to educating students with diverse expertise. In this Perspective the authors create a framework for synthetic biology training that deconstructs biotechnologies across spatial scales.

Cell-free biology is the activation of biological processes without the use of intact living cells. It has been used for more than 50 years across the life sciences as a foundational research tool, but a recent technical renaissance has facilitated high-yielding (grams of protein per litre), cell-free gene expression systems from model bacteria, the development of cell-free platforms from non-model organisms and multiplexed strategies for rapidly assessing biological design. These advances provide exciting opportunities to profoundly transform synthetic biology by enabling new approaches to the model-driven design of synthetic gene networks, the fast and portable sensing of compounds, on-demand biomanufacturing, building cells from the bottom up, and next-generation educational kits.Cell-free gene expression systems have long been used to address fundamental research questions. Now, owing to technological advances, these systems are finding wider applications in the field of synthetic biology, including in biosensing, biomanufacturing, education and the design of gene networks.