SARS-COV-2 Omicron variants conformationally escape a rare quaternary antibody binding mode.

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.

Improved Bst DNA Polymerase Variants Derived via a Machine Learning Approach.

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.

Machine learning-aided engineering of hydrolases for PET depolymerization.

Plastic waste poses an ecological challenge1-3 and enzymatic degradation offers one, potentially green and scalable, route for polyesters waste recycling4. Poly(ethylene terephthalate) (PET) accounts for 12% of global solid waste5, and a circular carbon economy for PET is theoretically attainable through rapid enzymatic depolymerization followed by repolymerization or conversion/valorization into other products6-10. Application of PET hydrolases, however, has been hampered by their lack of robustness to pH and temperature ranges, slow reaction rates and inability to directly use untreated postconsumer plastics11. Here, we use a structure-based, machine learning algorithm to engineer a robust and active PET hydrolase. Our mutant and scaffold combination (FAST-PETase: functional, active, stable and tolerant PETase) contains five mutations compared to wild-type PETase (N233K/R224Q/S121E from prediction and D186H/R280A from scaffold) and shows superior PET-hydrolytic activity relative to both wild-type and engineered alternatives12 between 30 and 50 °C and a range of pH levels. We demonstrate that untreated, postconsumer-PET from 51 different thermoformed products can all be almost completely degraded by FAST-PETase in 1 week. FAST-PETase can also depolymerize untreated, amorphous portions of a commercial water bottle and an entire thermally pretreated water bottle at 50 ºC. Finally, we demonstrate a closed-loop PET recycling process by using FAST-PETase and resynthesizing PET from the recovered monomers. Collectively, our results demonstrate a viable route for enzymatic plastic recycling at the industrial scale.

Recovery of Information Stored in Modified DNA with an Evolved Polymerase.

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'-O-methyl templates into double-stranded DNA. Starting from a thermophilic, error-correcting reverse transcriptase, RTX, we evolved an enzyme (RTX-Ome v6) that relies on a fully functional proofreading domain to correct mismatches on DNA, RNA, and 2'-O-methyl templates. In addition, we implemented a downstream analysis strategy that accommodates deletions that arise during phosphoramidite synthesis, the most common type of synthesis error. By coupling and integrating new chemistries, enzymes, and algorithms, we further enable the large-scale use of nucleic acids for information storage.

DLPacker: Deep learning for prediction of amino acid side chain conformations in proteins.

Prediction of side chain conformations of amino acids in proteins (also termed "packing") is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have been developed but more accurate ones are still needed. Machine learning (ML) methods have recently become a powerful tool for solving various problems in diverse areas of science, including structural biology. In this study, we evaluate the potential of deep neural networks (DNNs) for prediction of amino acid side chain conformations. We formulate the problem as image-to-image transformation and train a U-net style DNN to solve the problem. We show that our method outperforms other physics-based methods by a significant margin: reconstruction RMSDs for most amino acids are about 20% smaller compared to SCWRL4 and Rosetta Packer with RMSDs for bulky hydrophobic amino acids Phe, Tyr, and Trp being up to 50% smaller.

Discovery of Novel Gain-of-Function Mutations Guided by Structure-Based Deep Learning.

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 in vivo across three diverse proteins by at least 5-fold. Furthermore, this model provides a means to interrogate the chemical space within protein microenvironments and identify specific chemical interactions that contribute to the gain-of-function phenotypes resulting from individual mutations.

Cellular reagents for diagnostics and synthetic biology.

We have found that the overproduction of enzymes in bacteria followed by their lyophilization leads to 'cellular reagents' that can be directly used to carry out numerous molecular biology reactions. We demonstrate the use of cellular reagents in a variety of molecular diagnostics, such as TaqMan qPCR with no diminution in sensitivity, and in synthetic biology cornerstones such as the Gibson assembly of DNA fragments, where new plasmids can be constructed solely based on adding cellular reagents. Cellular reagents have significantly reduced complexity and cost of production, storage and implementation, features that should facilitate accessibility and use in resource-poor conditions.

Custom selenoprotein production enabled by laboratory evolution of recoded bacterial strains.

Incorporation of the rare amino acid selenocysteine to form diselenide bonds can improve stability and function of synthetic peptide therapeutics. However, application of this approach to recombinant proteins has been hampered by heterogeneous incorporation, low selenoprotein yields, and poor fitness of bacterial producer strains. We report the evolution of recoded Escherichia coli strains with improved fitness that are superior hosts for recombinant selenoprotein production. We apply an engineered ß-lactamase containing an essential diselenide bond to enforce selenocysteine dependence during continuous evolution of recoded E. coli strains. Evolved strains maintain an expanded genetic code indefinitely. We engineer a fluorescent reporter to quantify selenocysteine incorporation in vivo and show complete decoding of UAG codons as selenocysteine. Replacement of native, labile disulfide bonds in antibody fragments with diselenide bonds vastly improves resistance to reducing conditions. Highly seleno-competent bacterial strains enable industrial-scale selenoprotein expression and unique diselenide architecture, advancing our ability to customize the selenoproteome.

Microbiota and Metatranscriptome Changes Accompanying the Onset of Gingivitis.

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.

Evolution of a Thermophilic Strand-Displacing Polymerase Using High-Temperature Isothermal Compartmentalized Self-Replication.

Strand-displacing polymerases are a crucial component of isothermal amplification (IA) reactions, where the lack of thermal cycling reduces equipment needs and improves the time to answer, especially for point-of-care applications. In order to improve the function of strand-displacing polymerases, we have developed an emulsion-based directed evolution scheme, high-temperature isothermal compartmentalized self-replication (HTI-CSR) that does not rely on thermal cycling. Starting from an algorithm-optimized shuffled library of exonuclease-deficient Family A polymerases from Geobacillus stearothermophilus (Bst LF) and Thermus aquaticus (Klentaq), we have applied HTI-CSR to generate a more thermostable strand-displacing polymerase variant that performs well in loop-mediated isothermal amplification and rolling circle amplification, even after thermal challenges of up to 95 °C that lead to better primer annealing. The new enzyme (v5.9) is also capable of a variety of new reactions, including isothermal hyperbranched rolling circle amplification. The HTI-CSR method should now prove useful for evolving additional beneficial phenotypes in strand-displacing polymerases.

Evolving Bacterial Fitness with an Expanded Genetic Code.

Since the fixation of the genetic code, evolution has largely been confined to 20 proteinogenic amino acids. The development of orthogonal translation systems that allow for the codon-specific incorporation of noncanonical amino acids may provide a means to expand the code, but these translation systems cannot be simply superimposed on cells that have spent billions of years optimizing their genomes with the canonical code. We have therefore carried out directed evolution experiments with an orthogonal translation system that inserts 3-nitro-L-tyrosine across from amber codons, creating a 21 amino acid genetic code in which the amber stop codon ambiguously encodes either 3-nitro-L-tyrosine or stop. The 21 amino acid code is enforced through the inclusion of an addicted, essential gene, a beta-lactamase dependent upon 3-nitro-L-tyrosine incorporation. After 2000 generations of directed evolution, the fitness deficit of the original strain was largely repaired through mutations that limited the toxicity of the noncanonical. While the evolved lineages had not resolved the ambiguous coding of the amber codon, the improvements in fitness allowed new amber codons to populate protein coding sequences.

Synthetic evolutionary origin of a proofreading reverse transcriptase.

Most reverse transcriptase (RT) enzymes belong to a single protein family of ancient evolutionary origin. These polymerases are inherently error prone, owing to their lack of a proofreading (3'- 5' exonuclease) domain. To determine if the lack of proofreading is a historical coincidence or a functional limitation of reverse transcription, we attempted to evolve a high-fidelity, thermostable DNA polymerase to use RNA templates efficiently. The evolutionarily distinct reverse transcription xenopolymerase (RTX) actively proofreads on DNA and RNA templates, which greatly improves RT fidelity. In addition, RTX enables applications such as single-enzyme reverse transcription-polymerase chain reaction and direct RNA sequencing without complementary DNA isolation. The creation of RTX confirms that proofreading is compatible with reverse transcription.

Characterizing bacterial gene circuit dynamics with optically programmed gene expression signals.

Gene circuits are dynamical systems that regulate cellular behaviors, often using protein signals as inputs and outputs. Here we have developed an optogenetic 'function generator' method for programming tailor-made gene expression signals in live bacterial cells. We designed precomputed light sequences based on experimentally calibrated mathematical models of light-switchable two-component systems and used them to drive intracellular protein levels to match user-defined reference time courses. We used this approach to generate accelerated and linearized dynamics, sinusoidal oscillations with desired amplitudes and periods, and a complex waveform, all with unprecedented accuracy and precision. We also combined the function generator with a dual fluorescent protein reporter system, analogous to a dual-channel oscilloscope, to reveal that a synthetic repressible promoter linearly transforms repressor signals with an approximate 7-min delay. Our approach will enable a new generation of dynamical analyses of synthetic and natural gene circuits, providing an essential step toward the predictive design and rigorous understanding of biological systems.

Fragile X AGG analysis provides new risk predictions for 45-69 repeat alleles.

We investigated the effect of AGG interruptions on fragile X repeat instability upon transmission of fragile X intermediate and small premutation alleles with 45-69 CGG repeats. The FMR1 repeat structure was determined for 375 mothers, 48 fathers, and 538 offspring (457 maternal and 81 paternal transmissions) using a novel PCR assay to determine repeat length and AGG interruptions. The number of AGG interruptions and the length of uninterrupted CGG repeats at the 3' end were correlated with repeat instability on transmission. Maternal alleles with no AGGs conferred the greatest risk for unstable transmissions. All nine full mutation expansions were inherited from maternal alleles with no AGGs. Furthermore, the magnitude of repeat expansion was larger for alleles lacking AGG interruptions. Transmissions from paternal alleles with no AGGs also exhibited greater instability than those with one or more AGGs. Our results demonstrate that characterization of the AGG structure within the FMR1 repeat allows more accurate risk estimates of repeat instability and expansion to full mutations for intermediate and small premutation alleles.