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Abstract A sustainable operation for harvesting metals in the lanthanide series is needed to meet the rising demand for rare earth elements across diverse global industries. However, existing methods are limited in their capacity for detection and capture at environmentally and industrially relevant lanthanide concentrations. Supercharged fluorescent proteins have solvent-exposed, negatively charged residues that potentially create multiple direct chelation pockets for free lanthanide cations. Here, we demonstrate that negatively supercharged proteins can bind and quantitatively report concentrations of lanthanides via an underutilized lanthanide-to-chromophore pathway of energy transfer. The top-performing sensors detect lanthanides in the micromolar to millimolar range and remain unperturbed by environmentally significant concentrations of competing metals. As a demonstration of the versatility and adaptability of this energy transfer method, we show proximity and signal transmission between the lanthanides and a supramolecular assembly of supercharged proteins, paving the way for the detection of lanthanides via programmable protein oligomers and materials.

Abstract Engineering stabilized proteins is a fundamental challenge in the development of industrial and pharmaceutical biotechnologies. We present Stability Oracle: a structure-based graph-transformer framework that achieves SOTA performance on accurately identifying thermodynamically stabilizing mutations. Our framework introduces several innovations to overcome well-known challenges in data scarcity and bias, generalization, and computation time, such as: Thermodynamic Permutations for data augmentation, structural amino acid embeddings to model a mutation with a single structure, a protein structure-specific attention-bias mechanism that makes transformers a viable alternative to graph neural networks. We provide training/test splits that mitigate data leakage and ensure proper model evaluation. Furthermore, to examine our data engineering contributions, we fine-tune ESM2 representations (Prostata-IFML) and achieve SOTA for sequence-based models. Notably, Stability Oracle outperforms Prostata-IFML even though it was pretrained on 2000X less proteins and has 548X less parameters. Our framework establishes a path for fine-tuning structure-based transformers to virtually any phenotype, a necessary task for accelerating the development of protein-based biotechnologies.

Abstract 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 the low-yielding daffodil Narcissus pseudonarcissus. Here, we propose an efficient biosensor-machine learning technology stack for biocatalyst development, which we apply to engineer an Amaryllidaceae enzyme in Escherichia coli. Directed evolution is used to develop a highly sensitive (EC50 = 20 μM) and specific biosensor for the key Amaryllidaceae alkaloid branchpoint 4’-O-methylnorbelladine. A structure-based residual neural network (MutComputeX) is subsequently developed and used to generate activity-enriched variants of a plant methyltransferase, which are rapidly screened with the biosensor. Functional enzyme variants are identified that yield a 60% improvement in product titer, 2-fold higher catalytic activity, and 3-fold lower off-product regioisomer formation. A solved crystal structure elucidates the mechanism behind key beneficial mutations.

Abstract 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:operator 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 are shown to influence gene expression and 24 produce a >20-fold dynamic range. A panel of the newly repurposed regulators are 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 ratios up to 10.7-fold. Snowprint represents a unique, protein-agnostic tool that greatly facilitates the discovery of ligand-inducible transcriptional regulators for bioengineering applications. A web-accessible version of Snowprint is available at https://snowprint.groov.bio .

Abstract The ongoing evolution of SARS-CoV-2 into more easily transmissible and infectious variants has provided unprecedented insight into mutations enabling immune escape. Understanding how these mutations affect the dynamics of antibody-antigen interactions is crucial to the development of broadly protective antibodies and vaccines. Here we report the characterization of a potent neutralizing antibody (N3-1) identified from a COVID-19 patient during the first disease wave. Cryogenic electron microscopy revealed a quaternary binding mode that enables direct interactions with all three receptor-binding domains of the spike protein trimer, resulting in extraordinary avidity and potent neutralization of all major variants of concern until the emergence of Omicron. Structure-based rational design of N3-1 mutants improved binding to all Omicron variants but only partially restored neutralization of the conformationally distinct Omicron BA.1. This study provides new insights into immune evasion through changes in spike protein dynamics and highlights considerations for future conformationally biased multivalent vaccine designs.

Abstract Deep learning models are seeing increased use as methods to predict mutational effects or allowed mutations 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 commonly 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 two structure-based models (CNNs) and show that the different model types have distinct strengths and weaknesses. The overall prediction accuracies are largely uncorrelated between the sequence- and structure-based models. Overall, the two structure-based models are better at predicting buried aliphatic and hydrophobic residues whereas the two LLMs are better at predicting solvent-exposed polar and charged amino acids. Finally, we find that 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.

Abstract Lyme disease, one of the most common tickborne diseases, has been rapidly spreading in parallel with the expansion of the range of its tick vector. Better tick surveillance efforts are needed to accurately estimate disease risk and to guide public health and clinical management. We have developed two multiplex loop-mediated isothermal amplification (LAMP) reactions coupled with oligonucleotide strand displacement (OSD) probes to identify the tick host, Ixodes scapularis, and the Lyme disease pathogen, Borrelia burgdorferi, they carry. In each multiplex LAMP-OSD assay the co-presence of two target sequences is computed at the DNA level by linking the two corresponding amplicons and detecting the co-product on colorimetric lateral flow dipsticks. In tests with synthetic DNA, the co-presence of as few as four copies of input DNA could be detected, without producing spurious signals. Most importantly, though, the LAMP-OSD assay is amenable to being carried out directly with macerated tick samples, without any sample preparation. In such field conditions, assays performed robustly and demonstrated 97–100% sensitivity and 100% specificity with both field-collected and lab-raised artificially infected ticks. Such easy-to-use, arthropod and pathogen-specific assays would be well suited to field and near patient use without relying on complex instrumentation or infrastructure.

Ribosomes have evolved to polymerize L-α-amino acids into proteins comprising a peptide backbone. Here, a pyridazinone backbone is formed using ribosomes in vitro, producing a variety of sequence-defined alternating block-copolymers.

The yeast Saccharomyces cerevisiae is powerful for studying human G protein-coupled receptors as they can be coupled to its mating pathway. Here the authors engineer baker’s yeast to produce human sterols and show that vertebrate G protein coupled receptors are more sensitive in this membrane environment.

Abstract 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.

Flexizymes have been used to expand the scope of chemical substrates for ribosome-directed polymerization in vitro. Here the authors deduce design rules of Flexizyme-mediated tRNA acylation that more effectively predict the incorporation of new monomers into peptides.

One of the key approaches to treatment and control of infectious diseases, such as COVID-19, is accurate and rapid diagnostics that is widely deployable in a timely and scalable manner. To achieve this, it is essential to go beyond the traditional gold standard of quantitative PCR (qPCR) that is often faced with difficulties in scaling due to the complexity of infrastructure and human resource requirements.

Abstract Background Encoding arbitrary digital information in DNA has attracted attention as a potential avenue for large scale and long term data storage. However, in order to enable DNA data storage technologies there needs to be improvements in data storage fidelity (tolerance to mutation), the facility of writing and reading the data (biases and systematic error arising from synthesis and sequencing), and overall scalability. Results To this end, we have developed and implemented an encoding scheme that is suitable for detecting and correcting errors that may arise during storage, writing, and reading, such as those arising from nucleotide substitutions, insertions, and deletions. We propose a scheme for parallelized long term storage of encoded sequences that relies on overlaps rather than the address blocks found in previously published work. Using computer simulations, we illustrate the encoding, sequencing, decoding, and recovery of encoded information, ultimately demonstrating the possibility of a successful round-trip read/write. These demonstrations show that in theory a precise control over error tolerance is possible. Even after simulated degradation of DNA, recovery of original data is possible owing to the error correction capabilities built into the encoding strategy. A secondary advantage of our method is that the statistical characteristics (such as repetitiveness and GC-composition) of encoded sequences can also be tailored without sacrificing the overall ability to store large amounts of data. Finally, the combination of the overlap-based partitioning of data with the LZMA compression that is integral to encoding means that the entire sequence must be present for successful decoding. This feature enables inordinately strong encryptions. As a potential application, an encrypted pathogen genome could be distributed and carried by cells without danger of being expressed, and could not even be read out in the absence of the entire DNA consortium. Conclusions We have developed a method for DNA encoding, using a significantly different fundamental approach from existing work, which often performs better than alternatives and allows for a great deal of freedom and flexibility of application.

Single-stranded DNA (ssDNA) oligonucleotides are useful as aptamers, hybridization probes and for emerging applications in DNA nanotechnology. Current methods to purify ssDNA require both a strand-separation step and a separate size-separation step but may still leave double-stranded DNA (dsDNA) impurities in the sample. Here, we use commercially available acrydite DNA primers to immobilize one strand of a PCR product within a polyacrylamide matrix. Electrophoresis moves the non-crosslinked DNA into the gel where the single-stranded product of desired size can be recovered. Our results show this method produces high yields of pure ssDNA.

In the search for life beyond Earth, distinguishing the living from the non-living is paramount. However, this distinction is often elusive, as the origin of life is likely a stepwise evolutionary process, not a singular event. Regardless of the favored origin of life model, an inherent “grayness” blurs the theorized threshold defining life. Here, we explore the ambiguities between the biotic and the abiotic at the origin of life. The role of grayness extends into later transitions as well. By recognizing the limitations posed by grayness, life detection researchers will be better able to develop methods sensitive to prebiotic chemical systems and life with alternative biochemistries.

ABSTRACT Over half of adults experience gingivitis, a mild yet treatable form of periodontal disease caused by the overgrowth of oral microbes. Left untreated, gingivitis can progress to a more severe and irreversible disease, most commonly chronic periodontitis. While periodontal diseases are associated with a shift in the oral microbiota composition, it remains unclear how this shift impacts microbiota function early in disease progression. Here, we analyzed the transition from health to gingivitis through both 16S v4-v5 rRNA amplicon and metatranscriptome sequencing of subgingival plaque samples from individuals undergoing an experimental gingivitis treatment. Beta-diversity analysis of 16S rRNA reveals that samples cluster based on disease severity and patient but not by oral hygiene status. Significant shifts in the abundance of several genera occurred during disease transition, suggesting a dysbiosis due to development of gingivitis. Comparing taxonomic abundance with transcriptomic activity revealed concordance of bacterial diversity composition between the two quantification assays in samples originating from both healthy and diseased teeth. Metatranscriptome sequencing analysis indicates that during the early stages of transition to gingivitis, a number of virulence-related transcripts were significantly differentially expressed in individual and across pooled patient samples. Upregulated genes include those involved in proteolytic and nucleolytic processes, while expression levels of those involved in surface structure assembly and other general virulence functions leading to colonization or adaptation within the host are more dynamic. These findings help characterize the transition from health to periodontal disease and identify genes associated with early disease. IMPORTANCE Although more than 50% of adults have some form of periodontal disease, there remains a significant gap in our understanding of its underlying cause. We initiated this study in order to better characterize the progression from oral health to disease. We first analyzed changes in the abundances of specific microorganisms in dental plaque collected from teeth during health and gingivitis, the mildest form of periodontal disease. We found that the clinical score of disease and patient from whom the sample originated but not tooth brushing are significantly correlated with microbial community composition. While a number of virulence-related gene transcripts are differentially expressed in gingivitis samples relative to health, not all are increased, suggesting that the overall activity of the microbiota is dynamic during disease transition. Better understanding of which microbes are present and their function during early periodontal disease can potentially lead to more targeted prophylactic approaches to prevent disease progression.

Aptamers have a well-earned place in therapeutic, diagnostic, and sensor applications, and we now show that they provide an excellent foundation for education, as well. Within the context of the Freshman Research Initiative (FRI) at The University of Texas at Austin, students have used aptamer selection and development technologies in a teaching laboratory to build technical and 21st century skills appropriate for research scientists. One of the unique aspects of this course-based undergraduate research experience is that students develop and execute their own projects, taking ownership of their experience in what would otherwise be a traditional teaching lab setting. Of the many successes, this work includes the isolation and characterization of novel calf intestinal alkaline phosphatase (anti-CIAP) RNA aptamers by an undergraduate researcher. Further, preliminary survey data suggest that students who participate in the aptamer research experience express significant gains in their self-efficacy to conduct research, and their perceived ability to communicate scientific results, as well as organize and interpret data. This work describes, for the first time, the use of aptamers in an educational setting, highlights the positive student outcomes of the aptamer research experience, and presents the research findings relative to the novel anti-CIAP aptamer.

Synthetically engineered organisms hold promise for a broad range of medical, environmental, and industrial applications. Organisms can potentially be designed, for example, for the inexpensive and environmentally benign synthesis of pharmaceuticals and industrial chemicals, for the cleanup of environmental pollutants, and potentially even for biomedical applications such as the targeting of specific diseases or tissues. However, the use of synthetically engineered organisms comes with several reasonable safety concerns, one of which is that the organisms or their genes could escape their intended habitats and cause environmental disruption. Here we review key recent developments in this emerging field of synthetic biocontainment and discuss further developments that might be necessary for the widespread use of synthetic organisms. Specifically, we discuss the history and modern development of three strategies for the containment of synthetic microbes: addiction to an exogenously supplied ligand; self-killing outside of a designated environment; and self-destroying encoded DNA circuitry outside of a designated environment.

DNA circuits have proven to be useful amplifiers for diagnostic applications, in part because of their modularity and programmability. In order to determine whether different circuits could be modularly stacked, we used a catalytic hairpin assembly (CHA) circuit to initiate a hybridization chain reaction (HCR) circuit. In response to an input nucleic acid sequence, the CHA reaction accumulates immobilized duplexes and HCR elongates these duplexes. With fluorescein as a reporter each of these processes yielded 10-fold signal amplification in a convenient 96-well format. The modular circuit connections also allowed the output reporter to be readily modified to a G-quadruplex-DNAzyme that yielded a fluorescent signal.

We have developed a set of DNA circuits that execute during gel electrophoresis to yield immobile, fluorescent features in the gel. The parallel execution of orthogonal circuits led to the simultaneous production of different fluorescent lines at different positions in the gel. The positions of the lines could be rationally manipulated by changing the mobilities of the reactants. The ability to program at the nanoscale so as to produce patterns at the macroscale is a step towards programmable, synthetic chemical systems for generating defined spatiotemporal patterns.

We have developed a generalizable “smart molecular diagnostic„ capable of accurate point-of-care (POC) detection of variable nucleic acid targets. Our isothermal assay relies on multiplex execution of four loop-mediated isothermal amplification reactions, with primers that are degenerate and redundant, thereby increasing the breadth of targets while reducing the probability of amplification failure. An easy-to-read visual answer is computed directly by a multi-input Boolean OR logic gate (gate output is true if either one or more gate inputs is true) signal transducer that uses degenerate strand exchange probes to assess any combination of amplicons. We demonstrate our methodology by using the same assay to detect divergent Asian and African lineages of the evolving Zika virus (ZIKV), while maintaining selectivity against non-target viruses. Direct analysis of biological specimens proved possible, with crudely macerated ZIKV-infected Aedes aegypti mosquitoes being identified with 100% specificity and sensitivity. The ease-of-use with minimal instrumentation, broad programmability, and built-in fail-safe reliability make our smart molecular diagnostic attractive for POC use.

In many biological applications, we would like to be able to computationally predict mutational effects on affinity in protein–protein interactions. However, many commonly used methods to predict these effects perform poorly in important test cases. In particular, the effects of multiple mutations, non alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here an existing method applied in a novel way to a new test case; we interrogate affinity differences resulting from mutations in a host–virus protein–protein interface. We use steered molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then approximate affinity using the maximum applied force of separation and the area under the force-versus-distance curve. We find, even without the rigor and planning required for free energy calculations, that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation. Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-resistant hTfR1 mutants. Finally, our approach provides a framework to compare the effects of multiple mutations, individually and jointly, on protein–protein interactions.

ABSTRACT Bacteria are social organisms that display distinct behaviors/phenotypes when present in groups. These behaviors include the abilities to construct antibiotic-resistant sessile biofilm communities and to communicate with small signaling molecules (quorum sensing [QS]). Our understanding of biofilms and QS arises primarily from in vitro studies of bacterial communities containing large numbers of cells, often greater than 108 bacteria; however, in nature, bacteria often reside in dense clusters (aggregates) consisting of significantly fewer cells. Indeed, bacterial clusters containing 101 to 105 cells are important for transmission of many bacterial pathogens. Here, we describe a versatile strategy for conducting mechanistic studies to interrogate the molecular processes controlling antibiotic resistance and QS-mediated virulence factor production in high-density bacterial clusters. This strategy involves enclosing a single bacterium within three-dimensional picoliter-scale microcavities (referred to as bacterial “lobster traps”) defined by walls that are permeable to nutrients, waste products, and other bioactive small molecules. Within these traps, bacteria divide normally into extremely dense (1012 cells/ml) clonal populations with final population sizes similar to that observed in naturally occurring bacterial clusters. Using these traps, we provide strong evidence that within low-cell-number/high-density bacterial clusters, QS is modulated not only by bacterial density but also by population size and flow rate of the surrounding medium. We also demonstrate that antibiotic resistance develops as cell density increases, with as few as ~150 confined bacteria exhibiting an antibiotic-resistant phenotype similar to biofilm bacteria. Together, these findings provide key insights into clinically relevant phenotypes in low-cell-number/high-density bacterial populations. IMPORTANCE Prokaryotes are social organisms capable of coordinated group behaviors, including the abilities to construct antibiotic-resistant biofilms and to communicate with small signaling molecules (quorum sensing [QS]). While there has been significant effort devoted to understanding biofilm formation and QS, few studies have examined these processes in high-density/low-cell-number populations. Such studies have clinical significance, as many infections are initiated by small bacterial populations (