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Loop-mediated isothermal amplification (LAMP) has proven to be easier to implement than PCR for point-of-care diagnostic tests. However, the underlying mechanism of LAMP is complicated and the kinetics of the major steps in LAMP have not been fully elucidated, which prevents rational improvements in assay development. Here we present our work to characterize the kinetics of the elementary steps in LAMP and show that: (i) strand invasion / initiation is the rate-limiting step in the LAMP reaction; (ii) the loop primer plays an important role in accelerating the rate of initiation and does not function solely during the exponential amplification phase and (iii) strand displacement synthesis by Bst-LF polymerase is relatively fast (125 nt/s) and processive on both linear and hairpin templates, although with some interruptions on high GC content templates. Building on these data, we were able to develop a kinetic model that relates the individual kinetic experiments to the bulk LAMP reaction. The assays developed here provide important insights into the mechanism of LAMP, and the overall model should be crucial in engineering more sensitive and faster LAMP reactions. The kinetic methods we employ should likely prove useful with other isothermal DNA amplification methods.

Genetic biosensors are integral to synthetic biology. In particular, ligand-inducible prokaryotic transcription factors are frequently used in high-throughput screening, for dynamic feedback regulation, as multilayer logic gates, and in diagnostic applications. In order to provide a curated source that users can rely on for engineering applications, we have developed GroovDB (available at https://groov.bio), a Web-accessible database of ligand-inducible transcription factors that contains all information necessary to build chemically responsive genetic circuits, including biosensor sequence, ligand, and operator data. Ligand and DNA interaction data have been verified against the literature, while an automated data curation pipeline is used to programmatically fetch metadata, structural information, and references for every entry. A custom tool to visualize the natural genetic context of biosensor entries provides potential insights into alternative ligands and systems biology.

Stabilizing proteins is a fundamental challenge in protein engineering and is almost always a prerequisite for the development of industrial and pharmaceutical biotechnologies. Here we present Stability Oracle: a structure-based graph-transformer framework that achieves state-of-the-art performance on predicting the effect of a point mutation on a protein’s thermodynamic stability (ΔΔG). A strength of our model is its ability to identifystabilizingmutations, which often make up a small fraction of a protein’s mutational landscape. Our framework introduces several data and machine learning innovations to overcome well-known challenges in data scarcity and bias, generalization, and computation time. Stability Oracle is first pretrained on over 2M masked microenvironments and then fine-tuned using a novel data augmentation technique, Thermodynamic Permutations (TP), applied to a ∼120K curated subset of the mega-scale cDNA display proteolysis dataset. This technique increases the original 120K mutations to over 2M thermodynamically valid ΔΔG measurements to generate the first structure training set that samples and balances all 380 mutation types. By using the masked microenvironment paradigm, Stability Oracle does not require a second mutant structure and instead uses amino acid structural embeddings to represent a mutation. This architectural design accelerates training and inference times: we can both train on 2M instances with just 119 structures and generate deep mutational scan (DMS) predictions from only the wildtype structure. We benchmark Stability Oracle with both experimental and AlphaFold structures of all proteins on T2837, a test set that aggregates the common test sets (SSym, S669, p53, and Myoglobin) with all additional experimental data from proteins with over a 30% sequence similarity overlap. We used TP augmented T2837 to evaluate performance for engineering protein stability: Stability Oracle correctly identifies 48% of stabilizing mutations (ΔΔG < −0.5 kcal/mol) and 74% of its stabilizing predictions are indeed stabilizing (18% and 8% of predictions were neutral and destabilizing, respectively). For a fair comparison between sequence and structure-based fine-tuned deep learning models, we build on the Prostata framework and fine-tune the sequence embeddings of ESM2 on our training set (Prostata-IFML). A head-to-head comparison demonstrates that Stability Oracle outperforms Prostata-IFML on regression and classification even though the model is 548 times smaller and is pretrained with 4000 times fewer proteins, highlighting the advantages of learning from structures.

The DNA polymerase I from Geobacillus stearothermophilus (also known as Bst DNAP) is widely used in isothermal amplification reactions, where its strand displacement ability is prized. More robust versions of this enzyme should be enabled for diagnostic applications, especially for carrying out higher temperature reactions that might proceed more quickly. To this end, we appended a short fusion domain from the actin-binding protein villin that improved both stability and purification of the enzyme. In parallel, we have developed a machine learning algorithm that assesses the relative fit of individual amino acids to their chemical microenvironments at any position in a protein and applied this algorithm to predict sequence substitutions in Bst DNAP. The top predicted variants had greatly improved thermotolerance (heating prior to assay), and upon combination, the mutations showed additive thermostability, with denaturation temperatures up to 2.5 °C higher than the parental enzyme. The increased thermostability of the enzyme allowed faster loop-mediated isothermal amplification assays to be carried out at 73 °C, where both Bst DNAP and its improved commercial counterpart Bst 2.0 are inactivated. Overall, this is one of the first examples of the application of machine learning approaches to the thermostabilization of an enzyme.

Bioengineers increasingly rely on ligand-inducible transcription regulators for chemical-responsive control of gene expression, yet the number of regulators available is limited. Novel regulators can be mined from genomes, but an inadequate understanding of their DNA specificity complicates genetic design. Here we present Snowprint, a simple yet powerful bioinformatic tool for predicting regulator:DNA interactions. Benchmarking results demonstrate that Snowprint predictions are significantly similar for >45% of experimentally validated regulator:operator pairs from organisms across nine phyla and for regulators that span five distinct structural families. We then use Snowprint to design promoters for 33 previously uncharacterized regulators sourced from diverse phylogenies, of which 28 were shown to influence gene expression and 24 produced a >20-fold signal-to-noise ratio. A panel of the newly domesticated regulators were then screened for response to biomanufacturing-relevant compounds, yielding new sensors for a polyketide (olivetolic acid), terpene (geraniol), steroid (ursodiol), and alkaloid (tetrahydropapaverine) with induction ranges up to 10.7-fold. Snowprint represents a unique, generalizable tool that greatly facilitates the discovery of ligand-inducible transcriptional regulators for bioengineering applications.Abstract Figure

A major challenge to achieving industry-scale biomanufacturing of therapeutic alkaloids is the slow process of biocatalyst engineering. Amaryllidaceae alkaloids, such as the Alzheimer’s medication galantamine, are complex plant secondary metabolites with recognized therapeutic value. Due to their difficult synthesis they are regularly sourced by extraction and purification from low-yielding plants, including the wild daffodilNarcissus pseudonarcissus. Engineered biocatalytic methods have the potential to stabilize the supply chain of amaryllidaceae alkaloids. Here, we propose a highly efficient biosensor-AI technology stack for biocatalyst development, which we apply to engineer amaryllidaceae alkaloid production inEscherichia coli. Directed evolution is used to develop a highly sensitive (EC50= 20 uM) and specific biosensor for the key amaryllidaceae alkaloid branchpoint 4-O’Methylnorbelladine. A machine learning model (MutComputeX) was subsequently developed and used to generate activity-enriched variants of a plant methyltransferase, which were rapidly screened with the biosensor. Functional enzyme variants were identified that yielded a 60% improvement in product titer, 17-fold reduced remnant substrate, and 3-fold lower off-product regioisomer formation.

Exposing rare but highly malignant tumor cells that migrate from the primary tumor mass into adjacent tissue(s) or circulate in the bloodstream is critical for early detection and effective intervention(s). Here, we report on an aptamer-based strategy directed against epidermal growth factor receptor (EGFR), the most common oncogene in glioblastoma (GBM), to detect these deadly tumor cells. GBMs are characterized by diffuse infiltration into normal brain regions, and the inability to detect GBM cells renders the disease surgically incurable with a median survival of just 14.2 months. To test the sensitivity and specificity of our platform, anti-EGFR RNA aptamers were immobilized on chemically modified glass surfaces. Cells tested included primary human GBM cells expressing high levels of the wild-type EGFR, as well as genetically engineered murine glioma cells overexpressing the most common EGFR mutant (EGFRvIII lacking exons 2–7) in Ink4a/Arf-deficient astrocytes. We found that surfaces functionalized with anti-EGFR aptamers could capture both the human and murine GBM cells with high sensitivity and specificity. Our findings show how novel aptamer substrates could be used to determine whether surgical resection margins are free of tumor cells, or more widely for detecting tumor cells circulating in peripheral blood to improve early detection and/or monitoring residual disease after treatment. Cancer Res; 70(22); 9371–80. ©2010 AACR.

Supplementary Video 1 from Surface-Immobilized Aptamers for Cancer Cell Isolation and Microscopic Cytology

Supplementary Figure 7 from Aptamer:Toxin Conjugates that Specifically Target Prostate Tumor Cells

Supplementary Figures 1-6 from Aptamer:Toxin Conjugates that Specifically Target Prostate Tumor Cells

We have used RNA aptamer:gelonin conjugates to target and specifically destroy cells overexpressing the known cancer biomarker prostate-specific membrane antigen (PSMA). Aptamer:toxin conjugates have an IC50 of 27 nmol/L and display an increased potency of at least 600-fold relative to cells that do not express PSMA. The aptamer not only promotes uptake into target cells but also decreases the toxicity of gelonin in non-target cells. These results validate the notion that “escort aptamers” may be useful for the treatment of specific tumors expressing unique antigen targets.(Cancer Res 2006; 66(12): 5989-92)

Deep learning models are seeing increased use as methods to predict mutational effects or allowed mutations at various sites in proteins. The models commonly used for these purposes include large language models (LLMs) and 3D Convolutional Neural Networks (CNNs). These two model types have very different architectures and are trained on different representations of proteins. LLMs make use of the transformer architecture and are trained purely on protein sequences whereas 3D CNNs are trained on voxelized representations of local protein structure. While comparable overall prediction accuracies have been reported for both types of models, it is not known to what extent these models make comparable specific predictions and/or generalize protein biochemistry in similar ways. Here, we perform a systematic comparison of two LLMs and one 3D CNN model and show that the different model types have distinct strengths and weaknesses. The overall prediction accuracies are largely uncorrelated between sequence and structure based models. Overall, the 3D CNN model is better at predicting buried aliphatic and hydrophobic residues whereas the LLMs are better at predicting solvent-exposed polar and charged amino acids. A combined model that takes the individual model predictions as input can leverage these individual model strengths and results in significantly improved overall prediction accuracy.

Biosensors can accelerate the engineering of new biosynthetic pathways. Phloroglucinol is a platform chemical of wide utility that can be produced at limited titers inEscherichia coli. Starting from the TetR family repressor RolR that is responsive to the related compound resorcinol, we were able to employ a combined selection and screen to identify variants that had greatly improved activities with phloroglucinol (EC50 for phloroglucinol of 131 uM, relative to an estimated 42 mM for wild-type RolR). The variants obtained were further screened with a panel of similar single ring aromatics, and several were found to be generalists, consistent with the hypothesis that both natural and directed evolution tend to first create semi-specific pockets prior to further optimization for new function

The DNA polymerase I from Geobacillus stearothermophilus (also known as Bst DNAP) is widely used in isothermal amplification reactions, where its strand displacement ability is prized. More robust versions of this enzyme should be enabled for diagnostic applications, especially for carrying out higher temperature reactions that might proceed more quickly. To this end, we appended a short fusion domain from the actin-binding protein villin that improved both stability and purification of the enzyme. In parallel, we have developed a machine learning algorithm that assesses the relative fit of individual amino acids to their chemical microenvironments at any position in a protein and applied this algorithm to predict sequence substitutions in Bst DNAP. The top predicted variants had greatly improved thermotolerance (heating prior to assay), and upon combination, the mutations showed additive thermostability, with denaturation temperatures up to 2.5 °C higher than the parental enzyme. The increased thermostability of the enzyme allowed faster loop-mediated isothermal amplification assays to be carried out at 73 °C, where both Bst DNAP and its improved commercial counterpart Bst 2.0 are inactivated. Overall, this is one of the first examples of the application of machine learning approaches to the thermostabilization of an enzyme.

One fundamental problem of protein biochemistry is to predict protein structure from amino acid sequence. The inverse problem, predicting either entire sequences or individual mutations that are consistent with a given protein structure, has received much less attention even though it has important applications in both protein engineering and evolutionary biology. Here, we ask whether 3D convolutional neural networks (3D CNNs) can learn the local fitness landscape of protein structure to reliably predict either the wild-type amino acid or the consensus in a multiple sequence alignment from the local structural context surrounding site of interest. We find that the network can predict wild type with good accuracy, and that network confidence is a reliable measure of whether a given prediction is likely going to be correct or not. Predictions of consensus are less accurate and are primarily driven by whether or not the consensus matches the wild type. Our work suggests that high-confidence mis-predictions of the wild type may identify sites that are primed for mutation and likely targets for protein engineering. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10867-021-09593-6.

Yeast expression of human G Protein Coupled Receptors (GPCRs) can be used as a biosensor platform for the detection of pharmaceuticals. The Cannabinoid receptors type 1 and 2 (CB1/2R) are of particular interest, given the cornucopia of natural and synthetic cannabinoids being explored as therapeutics. We show for the first time that engineering the N-terminus of CB1R allows for efficient signal transduction in yeast, and that engineering the sterol composition of the yeast membrane optimizes CB2R performance. Using the dual cannabinoid biosensors, large libraries of synthetic cannabinoids and terpenes could be quickly screened to elucidate known and novel structure-activity relationships, including compounds and trends that more selectively target each of the two receptors. The biosensor strains offer a ready platform for evaluating the activity of new synthetic cannabinoids, monitoring drugs of abuse, and developing molecules that target the therapeutically important CB2R receptor while minimizing psychoactive effects.

The catechol group of 3,4-dihydroxyphenylalanine (L-DOPA) derived from L-tyrosine oxidation is a key post-translational modification (PTM) in many protein biomaterials and has potential as a bioorthogonal handle for precision protein conjugation applications such as antibody-drug conjugates. Despite this potential, indiscriminate enzymatic modification of exposed tyrosine residues or complete replacement of tyrosine using auxotrophic hosts remains the preferred method of introducing the catechol moiety into proteins, which precludes many protein engineering applications. We have developed new orthogonal translation machinery to site-specifically incorporate L-DOPA into recombinant proteins and a new fluorescent biosensor to selectively monitor L-DOPA incorporation in vivo. We show simultaneous biosynthesis and incorporation of L-DOPA and apply this translation machinery to engineer a novel metalloprotein containing a DOPA-Fe chromophore.

Signal amplification is ubiquitous in biology and engineering. Protein enzymes, such as DNA polymerases, can routinely achieve >106-fold signal increase, making them powerful tools for signal enhancement. Considerable signal amplification can also be achieved using nonenzymatic, cascaded nucleic acid strand exchange reactions. However, the practical application of such kinetically trapped circuits has so far proven difficult due to uncatalyzed leakage of the cascade. We now demonstrate that strategically positioned mismatches between circuit components can reduce unprogrammed hybridization reactions and therefore greatly diminish leakage. In consequence, we were able to synthesize a three-layer catalytic hairpin assembly cascade that could operate in a single tube and that yielded 3.7 × 104-fold signal amplification in only 4 h, a greatly improved performance relative to previous cascades. This advance should facilitate the implementation of nonenzymatic signal amplification in molecular diagnostics, as well as inform the design of a wide variety of increasingly intricate nucleic acid computation circuits.

We report two isothermal nucleic acid amplification assays for detection of monkeypox virus (MPXV) clades 2 and 3 that include the strains responsible for the current global outbreak of monkeypox. The assays use loop-mediated isothermal amplification (LAMP) to amplify two distinct sequences in the MPXV genome. Readout specificity is ensured by oligonucleotide strand displacement (OSD) probes integrated in one-pot LAMP-OSD reactions. OSD probes undergo toehold-mediated strand displacement hybridization to LAMP amplicon loop sequences derived from MPXV clades 2 and 3 resulting in fluorescence readable both in realtime and visually at endpoint. We also perform both assays on two different portable devices, the GeneTiger and the miniPCR, to exemplify compatibility with minimum infrastructure point-of-care (POC) testing in clinical and at-home settings. Both assays could readily detect single digit copies of MPXV synthetic double stranded DNA templates within 30 min.

With the rapid acceleration in the design and development of new biotherapeutics, ensuring consistent quality and understanding degradation pathways remain paramount, requiring an array of analytical methods including mass spectrometry. The incorporation of non-canonical amino acids, such as for synthetic selenoproteins, creates additional challenges. A comprehensive strategy to characterize selenoproteins should serve dual purposes of providing sequence confirmation and mapping of selenocysteine bridge locations and the identification of unanticipated side products. In the present study, a combined approach exploiting the benefits of both top-down and bottom-up mass spectrometry was developed. Both electron-transfer/higher-energy collision dissociation and 213 nm ultraviolet photodissociation were utilized to provide complementary information, allowing high quality characterization, localization of diselenide bridges for complex proteins, and the identification of previously unreported selenoprotein dimers.

Ribonucleoproteins (RNPs) are RNA-protein complexes utilized natively in both prokaryotes and eukaryotes to regulate essential processes within the cell. Over the past few years, many of these native systems have been adapted to provide control over custom genetic targets. Engineered RNP-based control systems allow for fine-tune regulation of desired targets, by providing customizable nucleotide-nucleotide interactions. However, as there have been several engineered RNP systems developed recently, identifying an optimal system for various bioprocesses is challenging. Here, we review the most successful engineered RNP systems and their applications to survey the current state of the field. Additionally, we provide selection criteria to provide users a streamlined method for identifying an RNP control system most useful to their own work. Lastly, we discuss future applications of RNP control systems and how they can be utilized to address the current grand challenges of the synthetic biology community.

Escherichia coli that utilize 4- and 5-fluoroindole and replace tryptophan with fluorinated derivatives throughout their proteomes have been evolved.

Despite the promise of deep learning accelerated protein engineering, examples of such improved proteins are scarce. Here we report that a 3D convolutional neural network trained to associate amino acids with neighboring chemical microenvironments can guide identification of novel gain-of-function mutations that are not predicted by energetics-based approaches. Amalgamation of these mutations improved protein function

Methacryloyl-type monomers containing adenine and thymine have been successfully synthesized with good yields. The homopolymerization and copolymerization of these two new functional monomers were carried out using RAFT polymerization. The reactivity ratios of monomer pairs were measured and calculated using a nonlinear least-squares (NLLS) method, and the results confirmed that the monomer reactivities were dependent on the solvent used for polymerization. The presence and absence of hydrogen bonding affected the resultant copolymer composition where moderate alternating copolymers had a tendency to be formed in CHCl3, while in DMF, statistical copolymers were formed. Furthermore, the glass transition temperatures of the copolymers were investigated, and the self-assembly of block copolymers made in solvents with different polarity were studied.

SummaryThe worldwide spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the repeated emergence of variants of concern. The Omicron variant has two dominant sub-lineages, BA.1 and BA.2, each with unprecedented numbers of nonsynonymous and indel spike protein mutations: 33 and 29, respectively. Some of these mutations individually increase transmissibility and enhance immune evasion, but their interactions within the Omicron mutational background is unknown. We characterize the molecular effects of all Omicron spike mutations on expression, human ACE2 receptor affinity, and neutralizing antibody recognition. We show that key mutations enable escape from neutralizing antibodies at a variety of epitopes. Stabilizing mutations in the N-terminal and S2 domains of the spike protein compensate for destabilizing mutations in the receptor binding domain, thereby enabling the record number of mutations in Omicron sub-lineages. Taken together, our results provide a comprehensive account of the mutational effects in the Omicron spike protein and illuminate previously unknown mechanisms of how the N-terminal domain can compensate for destabilizing mutations within the more evolutionarily constrained RBD.

The charge states of proteins can greatly influence their stabilities and interactions with substrates, and the addition of multiple charges (supercharging) has been shown to be a successful approach for engineering protein stability and function. The addition of a fast-folding fusion domain to the Bacillus stearothermophilus DNA polymerase improved its functionality in isothermal amplification assays, and further charge engineering of this domain has increased both protein stability and diagnostics performance. When combined with mutations that stabilize the core of the protein, the charge-engineered fusion domain leads to the ability to carry out loop-mediated isothermal amplification (LAMP) at temperatures up to 74° C or in the presence of high concentrations of urea, with detection times under 10 min. Adding both positive and negative charges to the fusion domain led to changes in the relative reverse transcriptase and DNA polymerase activities of the polymerase. Overall, the development of a modular fusion domain whose charged surface can be modified at will should prove to be of use in the engineering of other polymerases and, in general, may prove useful for protein stabilization.

The ribosome is a macromolecular machine that catalyzes the sequence-defined polymerization of L-α-amino acids into polypeptides. The catalysis of peptide bond formation between amino acid substrates is based on entropy trapping, wherein the adjacency of transfer RNA (tRNA)-coupled acyl bonds in the P-site and the α-amino groups in the A-site aligns the substrates for coupling. The plasticity of this catalytic mechanism has been observed in both remnants of the evolution of the genetic code and modern efforts to reprogram the genetic code (e.g., ribosomal incorporation of non-canonical amino acids, ribosomal ester formation). However, the limits of ribosome-mediated polymerization are underexplored. Here, rather than peptide bonds, we demonstrate ribosome-mediated polymerization of pyridazinone bonds via a cyclocondensation reaction between activated γ-keto and α-hydrazino ester monomers. In addition, we demonstrate the ribosome-catalyzed synthesis of peptide-hybrid oligomers composed of multiple sequence-defined alternating pyridazinone linkages. Our results highlight the plasticity of the ribosome’s ancient bond-formation mechanism, expand the range of non-canonical polymeric backbones that can be synthesized by the ribosome, and open the door to new applications in synthetic biology.

Protein reagents are indispensable for most molecular and synthetic biology procedures. Most conventional protocols rely on highly purified protein reagents that require considerable expertise, time, and infrastructure to produce. In consequence, most proteins are acquired from commercial sources, reagent expense is often high, and accessibility may be hampered by shipping delays, customs barriers, geopolitical constraints, and the need for a constant cold chain. Such limitations to the widespread availability of protein reagents, in turn, limit the expansion and adoption of molecular biology methods in research, education, and technology development and application. Here, we describe protocols for producing a low-resource and locally sustainable reagent delivery system, termed "cellular reagents," in which bacteria engineered to overexpress proteins of interest are dried and can then be used directly as reagent packets in numerous molecular biology reactions, without the need for protein purification or a constant cold chain. As an example of their application, we describe the execution of polymerase chain reaction (PCR) and loop-mediated isothermal amplification (LAMP) using cellular reagents, detailing how to replace pure protein reagents with optimal amounts of rehydrated cellular reagents. We additionally describe a do-it-yourself fluorescence visualization device for using these cellular reagents in common molecular biology applications. The methods presented in this article can be used for low-cost, on-site production of commonly used molecular biology reagents (including DNA and RNA polymerases, reverse transcriptases, and ligases) with minimal instrumentation and expertise, and without the need for protein purification. Consequently, these methods should generally make molecular biology reagents more affordable and accessible. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Preparation of cellular reagents Alternate Protocol 1: Preparation of lyophilized cellular reagents Alternate Protocol 2: Evaluation of bacterial culture growth via comparison to McFarland turbidity standards Support Protocol 1: SDS-PAGE for protein expression analysis of cellular reagents Basic Protocol 2: Using Taq DNA polymerase cellular reagents for PCR Basic Protocol 3: Using Br512 DNA polymerase cellular reagents for loop-mediated isothermal amplification (LAMP) Support Protocol 2: Building a fluorescence visualization device.

DNA is increasingly being explored as an alternative medium for digital information storage, but the potential information loss from degradation and associated issues with error during reading challenge its wide-scale implementation. To address this, we propose an atomic-scale encoding standard for DNA, where information is encoded in degradation-resistant analogues of natural nucleic acids (xNAs). To better enable this approach, we used directed evolution to create a polymerase capable of transforming 2'

Prokaryotic transcription factors can be repurposed as analytical and synthetic tools for precise chemical measurement and regulation. Monoterpenes encompass a broad chemical family that are commercially valuable as flavors, cosmetics, and fragrances, but have proven difficult to measure, especially in cells. Herein, we develop genetically-encoded, generalist monoterpene biosensors by using directed evolution to expand the effector specificity of the camphor-responsive TetR-family regulator CamR from Pseudomonas putida. Using a novel negative selection coupled with a high-throughput positive screen (Seamless Enrichment of Ligand-Inducible Sensors, SELIS), we evolve CamR biosensors that can recognize four distinct monoterpenes: borneol, fenchol, eucalyptol, and camphene. Different evolutionary trajectories surprisingly yielded common mutations, emphasizing the utility of CamR as a platform for creating generalist biosensors. Systematic promoter optimization driving the reporter increased the system’s signal-to-noise ratio to 150-fold. These sensors can serve as a starting point for the high-throughput screening and dynamic regulation of bicyclic monoterpene production strains.

The yeast Saccharomyces cerevisiae is powerful for studying human G protein-coupled receptors as they can be coupled to its mating pathway. However, some receptors, including the mu opioid receptor, are non-functional, which may be due to the presence of the fungal sterol ergosterol instead of cholesterol. Here we engineer yeast to produce cholesterol and introduce diverse mu, delta, and kappa opioid receptors to create sensitive opioid biosensors that recapitulate agonist binding profiles and antagonist inhibition. Additionally, human mu opioid receptor variants, including those with clinical relevance, largely display expected phenotypes. By testing mu opioid receptor-based biosensors with systematically adjusted cholesterol biosynthetic intermediates, we relate sterol profiles to biosensor sensitivity. Finally, we apply sterol-modified backgrounds to other human receptors revealing sterol influence in SSTR5, 5-HTR4, FPR1, and NPY1R signaling. This work provides a platform for generating human G protein-coupled receptor-based biosensors, facilitating receptor deorphanization and high-throughput screening of receptors and effectors.

Machine and deep learning approaches can leverage the increasingly available massive datasets of protein sequences, structures, and mutational effects to predict variants with improved fitness. Many different approaches are being developed, but systematic benchmarking studies indicate that even though the specifics of the machine learning algorithms matter, the more important constraint comes from the data availability and quality utilized during training. In cases where little experimental data are available, unsupervised and self-supervised pre-training with generic protein datasets can still perform well after subsequent refinement via hybrid or transfer learning approaches. Overall, recent progress in this field has been staggering, and machine learning approaches will likely play a major role in future breakthroughs in protein biochemistry and engineering.

Background The construction and application of synthetic genetic circuits is frequently improved if gene expression can be orthogonally controlled, relative to the host. In plants, orthogonality can be achieved via the use of CRISPR-based transcription factors that are programmed to act on natural or synthetic promoters. The construction of complex gene circuits can require multiple, orthogonal regulatory interactions, and this in turn requires that the full programmability of CRISPR elements be adapted to non-natural and non-standard promoters that have few constraints on their design. Therefore, we have developed synthetic promoter elements in which regions upstream of the minimal 35S CaMV promoter are designed from scratch to interact via programmed gRNAs with dCas9 fusions that allow activation of gene expression. Results A panel of three, mutually orthogonal promoters that can be acted on by artificial gRNAs bound by CRISPR regulators were designed. Guide RNA expression targeting these promoters was in turn controlled by either Pol III (U6) or ethylene-inducible Pol II promoters, implementing for the first time a fully artificial Orthogonal Control System (OCS). Following demonstration of the complete orthogonality of the designs, the OCS was tied to cellular metabolism by putting gRNA expression under the control of an endogenous plant signaling molecule, ethylene. The ability to form complex circuitry was demonstrated via the ethylene-driven, ratiometric expression of fluorescent proteins in single plants. Conclusions The design of synthetic promoters is highly generalizable to large tracts of sequence space, allowing Orthogonal Control Systems of increasing complexity to potentially be generated at will. The ability to tie in several different basal features of plant molecular biology (Pol II and Pol III promoters, ethylene regulation) to the OCS demonstrates multiple opportunities for engineering at the system level. Moreover, given the fungibility of the core 35S CaMV promoter elements, the derived synthetic promoters can potentially be utilized across a variety of plant species.

A key bottleneck in the microbial production of therapeutic plant metabolites is identifying enzymes that can improve yield. The facile identification of genetically encoded biosensors can overcome this limitation and become part of a general method for engineering scaled production. We have developed a combined screening and selection approach that quickly refines the affinities and specificities of generalist transcription factors; using RamR as a starting point, we evolve highly specific (100-fold preference) and sensitive (half-maximum effective concentration (EC

Aptamers have been the subject of more than 144,000 papers to date. However, there has been a growing concern that errors in reporting aptamer research limit the reliability of these reagents for research and other applications. These observations noting inconsistencies in the use of our RNA anti-lysozyme aptamer served as an impetus for our systematic review of the reporting of aptamer sequences in the literature. Our detailed examination of literature citing the RNA anti-lysozyme aptamer revealed that 93% of the 61 publications reviewed reported unexplained altered sequences with 86% of those using DNA variants. The ten most cited aptamers were examined using a standardized methodology in order to categorize the extent to which the sequences themselves were apparently improperly reproduced, both in the literature and presumably in experiments beyond their discovery. Our review of 800 aptamer publications spanned decades, multiple journals, and research groups, and revealed that 44% of the papers reported unexplained sequence alterations. We identified ten common categories of sequence alterations including deletions, substitutions, additions, among others. The robust data set we have produced elucidates a source of irreproducibility and unreliability in our field and can be used as a starting point for building evidence-based best practices in publication standards to elevate the rigor and reproducibility of aptamer research.

Plastic waste poses an ecological challenge1. While current plastic waste management largely relies on unsustainable, energy-intensive, or even hazardous physicochemical and mechanical processes, enzymatic degradation offers a green and sustainable route for plastic waste recycling2. Poly(ethylene terephthalate) (PET) has been extensively used in packaging and for the manufacture of fabrics and single-used containers, accounting for 12% of global solid waste3. The practical application of PET hydrolases has been hampered by their lack of robustness and the requirement for high processing temperatures. Here, we use a structure-based, deep learning algorithm to engineer an extremely robust and highly active PET hydrolase. Our best resulting mutant (FAST-PETase: Functional, Active, Stable, and Tolerant PETase) exhibits superior PET-hydrolytic activity relative to both wild-type and engineered alternatives, (including a leaf-branch compost cutinase and its mutant4) and possesses enhanced thermostability and pH tolerance. We demonstrate that whole, untreated, post-consumer PET from 51 different plastic products can all be completely degraded by FAST-PETase within one week, and in as little as 24 hours at 50 °C. Finally, we demonstrate two paths for closed-loop PET recycling and valorization. First, we re-synthesize virgin PET from the monomers recovered after enzymatic depolymerization. Second, we enable in situ microbially-enabled valorization using a Pseudomonas strain together with FAST-PETase to degrade PET and utilize the evolved monomers as a carbon source for growth and polyhydroxyalkanoate production. Collectively, our results demonstrate the substantial improvements enabled by deep learning and a viable route for enzymatic plastic recycling at the industrial scale.

Plastic waste poses an ecological challenge

A critical step in repurposing the cellular translation machinery for the synthesis of polymeric products is the acylation of transfer RNA (tRNA) with unnatural monomers. Toward this goal, flexizymes, ribozymes capable of aminoacylation, have emerged as a uniquely adept tool for charging tRNA with ever increasingly diverse substrates. In this review, we present a library of monomer substrates that have been tested for tRNA acylation with the flexizyme system. From this mile-high view, we provide insights for understanding the chemical factors that influence flexizyme-mediated tRNA acylation. We conclude that flexizymes are primitive esterification catalysts that display a modest binding affinity to the monomer's aromatic recognition element. Together, these robust, yet flexible, flexizyme systems provide researchers with unprecedented access for preparing unnatural acyl-tRNA and the opportunity to repurpose the translation machinery for the synthesis of novel biologically derived structures beyond native proteins and peptides.

Inducing immune bee genes Honey bees are prone to parasitism by the Varroa mite, which is a vector for several bee pathogens. However, honey bees are also host to the symbiotic gut bacterium Snodgrassella alvi. Leonard et al. engineered S. alvi to produce double-stranded RNA (dsRNA)—a stimulus for insect RNA interference defense responses—from a plasmid containing two inverted promoters tagged with a fluorescent label (see the Perspective by Paxton). This dsRNA module can be targeted to interfere with specific bee genes as well as crucial viral and mite genes. The authors found that gene expression could be blocked for at least 15 days as the symbionts established in the bees' guts and continuously expressed the dsRNA constructs. S. alvi with specifically targeted plasmids not only suppressed infection with deformed wing virus but also effectively reduced Varroa mite survival. Science , this issue p. 573 ; see also p. 504

Natural and synthetic hydrogels have been widely investigated as biomaterial scaffolds to promote tissue repair and regeneration. Nevertheless, the scaffold alone is often insufficient to drive new tissue growth, instead requiring continuous delivery of therapeutics, such as proteins or other biomolecules that work in concert with structural support provided by the scaffold. However, because of the high-water content, hydrogels tend to be permeable and cause rapid release of the encapsulated drug, which could lead to serious complications from local overdose and may result in the significant waste of encapsulated therapeutic(s). To this end, we designed an oligonucleotide-functionalized hydrogel that can provide sustained and controlled delivery of therapeutics for up to 4 weeks. To prove this concept, we successfully achieved sustained release (for over 28 days) of model anti-Nogo receptor (anti-NgR) RNA aptamer from oligonucleotide-functionalized hyaluronic acid-based hydrogel by changing the complementarity between the short antisense sequences and the aptamer. Furthermore, the released aptamer successfully blocked neuro-inhibitory effects of myelin-derived inhibitors and promoted neurite outgrowth from rat dorsal root ganglia in vitro. Because antisense sequences can be designed to bind to proteins, peptides, and aptamer, our oligonucleotide-functionalized hydrogel offers a promising therapeutic delivery system to obtain controlled release (both bolus and sustained) of various therapeutics for the treatment of complex diseases and injury models, such as spinal cord injury. STATEMENT OF SIGNIFICANCE: Producing a therapeutic effect often requires the administration of multiple injections with high dosages. This regimen causes discomfort to the patient and raises cost of treatment. Additionally, systemic delivery of therapeutics often results in adverse effects; therefore, local delivery at the site of injury is desirable. Therefore, in this study, we designed an oligonucleotide-functionalized biomaterial platform using ssDNA oligonucleotides (immobile species) as antisense sequences to increase residence time and fine-tune the release of anti-nogo receptor aptamer (mobile species) for spinal cord injury application. Because antisense sequences can be designed to bind proteins, peptides, and aptamer, our hydrogel offers a promising delivery system to obtain controlled release of various therapeutics for the treatment of complex diseases and injury models.

We demonstrate in vitro incorporation of cyclic β-amino acids into peptides by the ribosome through genetic code reprogramming. Further, we show that incorporation efficiency can be increased through the addition of elongation factor P.

Hysteresis is an important feature of enzyme-catalyzed reactions, as it reflects the influence of enzyme regulation in the presence of ligands such as substrates or allosteric molecules. In typical kinetic studies of enzyme activity, hysteretic behavior is observed as a “lag” or “burst” in the time course of the catalyzed reaction. These lags and bursts are due to the relatively slow transition from one state to another state of the enzyme molecule, with different states having different kinetic properties. However, it is difficult to understand the underlying mechanism of hysteresis by observing bulk reactions because the different enzyme molecules in the population behave stochastically. In this work, we studied the hysteretic behavior of mutant β-glucuronidase (GUS) using a high-throughput single-molecule array platform and investigated the effect of thermal treatment on the hysteresis., We explored the hysteresis of mutant β-glucuronidase at the single-molecule level and found that hysteresis was a substrate-induced activation process, which could be accelerated by a heat-pulse.

InEscherichia coli, DNA replication termination is orchestrated by two clusters ofTersites forming a DNA replication fork trap when bound by Tus proteins. The formation of a ‘locked’ Tus-Tercomplex is essential for halting incoming DNA replication forks. However, the absence of replication fork arrest at someTersites raised questions about their significance. In this study, we examined the genome-wide distribution of Tus and found that only the six innermostTersites (TerA-EandG) were significantly bound by Tus. We also found that a single ectopic insertion ofTerBin its non-permissive orientation could not be achieved, advocating against a need for ‘back-up’Tersites. Finally, examination of the genomes of a variety of Enterobacterales revealed a new replication fork trap architecture exclusively found outside the Enterobacteriaceae family. Taken together, our data enabled the delineation of a narrow prototypical Tus-dependent DNA replication fork trap consisting of only twoTersites.Graphical Abstract

A key bottleneck in the microbial production of therapeutic plant metabolites is identifying enzymes that can greatly improve yield. The facile identification of genetically encoded biosensors can overcome this limitation and become part of a general method for engineering scaled production. We have developed a unique combined screening and selection approach that quickly refines the affinities and specificities of generalist transcription factors, and using RamR as a starting point we evolve highly specific (>100-fold preference) and sensitive (EC50