Reinforcement learning-guided control strategies for CAR T-cell activation and expansion.

Reinforcement learning (RL), a subset of machine learning (ML), could optimize and control biomanufacturing processes, such as improved production of therapeutic cells. Here, the process of CAR T-cell activation by antigen-presenting beads and their subsequent expansion is formulated in silico. The simulation is used as an environment to train RL-agents to dynamically control the number of beads in culture to maximize the population of robust effector cells at the end of the culture. We make periodic decisions of incremental bead addition or complete removal. The simulation is designed to operate in OpenAI Gym, enabling testing of different environments, cell types, RL-agent algorithms, and state inputs to the RL-agent. RL-agent training is demonstrated with three different algorithms (PPO, A2C, and DQN), each sampling three different state input types (tabular, image, mixed); PPO-tabular performs best for this simulation environment. Using this approach, training of the RL-agent on different cell types is demonstrated, resulting in unique control strategies for each type. Sensitivity to input-noise (sensor performance), number of control step interventions, and advantages of pre-trained RL-agents are also evaluated. Therefore, we present an RL framework to maximize the population of robust effector cells in CAR T-cell therapy production.

Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2.

The association of the receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein with human angiotensin-converting enzyme 2 (hACE2) represents the first required step for cellular entry. SARS-CoV-2 has continued to evolve with the emergence of several novel variants, and amino acid changes in the RBD have been implicated with increased fitness and potential for immune evasion. Reliably predicting the effect of amino acid changes on the ability of the RBD to interact more strongly with the hACE2 can help assess the implications for public health and the potential for spillover and adaptation into other animals. Here, we introduce a two-step framework that first relies on 48 independent 4-ns molecular dynamics (MD) trajectories of RBD-hACE2 variants to collect binding energy terms decomposed into Coulombic, covalent, van der Waals, lipophilic, generalized Born solvation, hydrogen bonding, π-π packing, and self-contact correction terms. The second step implements a neural network to classify and quantitatively predict binding affinity changes using the decomposed energy terms as descriptors. The computational base achieves a validation accuracy of 82.8% for classifying single-amino acid substitution variants of the RBD as worsening or improving binding affinity for hACE2 and a correlation coefficient of 0.73 between predicted and experimentally calculated changes in binding affinities. Both metrics are calculated using a fivefold cross-validation test. Our method thus sets up a framework for screening binding affinity changes caused by unknown single- and multiple-amino acid changes offering a valuable tool to predict host adaptation of SARS-CoV-2 variants toward tighter hACE2 binding.

De novo design and Rosetta-based assessment of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike receptor binding domain (RBD).

The continued emergence of new SARS-CoV-2 variants has accentuated the growing need for fast and reliable methods for the design of potentially neutralizing antibodies (Abs) to counter immune evasion by the virus. Here, we report on the de novo computational design of high-affinity Ab variable regions (Fv) through the recombination of VDJ genes targeting the most solvent-exposed hACE2-binding residues of the SARS-CoV-2 spike receptor binding domain (RBD) protein using the software tool OptMAVEn-2.0. Subsequently, we carried out computational affinity maturation of the designed variable regions through amino acid substitutions for improved binding with the target epitope. Immunogenicity of designs was restricted by preferring designs that match sequences from a 9-mer library of "human Abs" based on a human string content score. We generated 106 different antibody designs and reported in detail on the top five that trade-off the greatest computational binding affinity for the RBD with human string content scores. We further describe computational evaluation of the top five designs produced by OptMAVEn-2.0 using a Rosetta-based approach. We used Rosetta SnugDock for local docking of the designs to evaluate their potential to bind the spike RBD and performed "forward folding" with DeepAb to assess their potential to fold into the designed structures. Ultimately, our results identified one designed Ab variable region, P1.D1, as a particularly promising candidate for experimental testing. This effort puts forth a computational workflow for the de novo design and evaluation of Abs that can quickly be adapted to target spike epitopes of emerging SARS-CoV-2 variants or other antigenic targets.

Evaluation of strategies to narrow the product chain-length distribution of microbially synthesized free fatty acids.

The dominant strategy for tailoring the chain-length distribution of free fatty acids (FFA) synthesized by heterologous hosts is expression of a selective acyl-acyl carrier protein (ACP) thioesterase. However, few of these enzymes can generate a precise (greater than 90% of a desired chain-length) product distribution when expressed in a microbial or plant host. The presence of alternative chain-lengths can complicate purification in situations where blends of fatty acids are not desired. We report the assessment of several strategies for improving the dodecanoyl-ACP thioesterase from the California bay laurel to exhibit more selective production of medium-chain free fatty acids to near exclusivity. We demonstrated that matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF MS) was an effective library screening technique for identification of thioesterase variants with favorable shifts in chain-length specificity. This strategy proved to be a more effective screening technique than several rational approaches discussed herein. With this data, we isolated four thioesterase variants which exhibited a more selective FFA distribution over wildtype when expressed in the fatty acid accumulating E. coli strain, RL08. We then combined mutations from the MALDI isolates to generate BTE-MMD19, a thioesterase variant capable of producing free fatty acids consisting of 90% of C12 products. Of the four mutations which conferred a specificity shift, we noted that three affected the shape of the binding pocket, while one occurred on the positively charged acyl carrier protein landing pad. Finally, we fused the maltose binding protein (MBP) from E. coli to the N - terminus of BTE-MMD19 to improve enzyme solubility and achieve a titer of 1.9 g per L of twelve-carbon fatty acids in a shake flask.

A single amino acid change led to structural and functional differentiation of PvHd1 to control flowering in switchgrass.

Switchgrass, a forage and bioenergy crop, occurs as two main ecotypes with different but overlapping ranges of adaptation. The two ecotypes differ in a range of characteristics, including flowering time. Flowering time determines the duration of vegetative development and therefore biomass accumulation, a key trait in bioenergy crops. No causal variants for flowering time differences between switchgrass ecotypes have, as yet, been identified. In this study, we mapped a robust flowering time quantitative trait locus (QTL) on chromosome 4K in a biparental F2 population and characterized the flowering-associated transcription factor gene PvHd1, an ortholog of CONSTANS in Arabidopsis and Heading date 1 in rice, as the underlying causal gene. Protein modeling predicted that a serine to glycine substitution at position 35 (p.S35G) in B-Box domain 1 greatly altered the global structure of the PvHd1 protein. The predicted variation in protein compactness was supported in vitro by a 4 °C shift in denaturation temperature. Overexpressing the PvHd1-p.35S allele in a late-flowering CONSTANS-null Arabidopsis mutant rescued earlier flowering, whereas PvHd1-p.35G had a reduced ability to promote flowering, demonstrating that the structural variation led to functional divergence. Our findings provide us with a tool to manipulate the timing of floral transition in switchgrass cultivars and, potentially, expand their cultivation range.

Inactivation Mechanism and Efficacy of Grape Seed Extract for Human Norovirus Surrogate.

Proper disinfection of harvested food and water is critical to minimize infectious disease. Grape seed extract (GSE), a commonly used health supplement, is a mixture of plant-derived polyphenols. Polyphenols possess antimicrobial and antifungal properties, but antiviral effects are not well-known. Here we show that GSE outperformed chemical disinfectants (e.g., free chlorine and peracetic acids) in inactivating Tulane virus, a human norovirus surrogate. GSE induced virus aggregation, a process that correlated with a decrease in virus titers. This aggregation and disinfection were not reversible. Molecular docking simulations indicate that polyphenols potentially formed hydrogen bonds and strong hydrophobic interactions with specific residues in viral capsid proteins. Together, these data suggest that polyphenols physically associate with viral capsid proteins to aggregate viruses as a means to inhibit virus entry into the host cell. Plant-based polyphenols like GSE are an attractive alternative to chemical disinfectants to remove infectious viruses from water or food. IMPORTANCE Human noroviruses are major food- and waterborne pathogens, causing approximately 20% of all cases of acute gastroenteritis cases in developing and developed countries. Proper sanitation or disinfection are critical strategies to minimize human norovirus-caused disease until a reliable vaccine is created. Grape seed extract (GSE) is a mixture of plant-derived polyphenols used as a health supplement. Polyphenols are known for antimicrobial, antifungal, and antibiofilm activities, but antiviral effects are not well-known. In studies presented here, plant-derived polyphenols outperformed chemical disinfectants (i.e., free chlorine and peracetic acids) in inactivating Tulane virus, a human norovirus surrogate. Based on data from molecular assays and molecular docking simulations, the current model is that the polyphenols in GSE bind to the Tulane virus capsid, an event that triggers virion aggregation. It is thought that this aggregation prevents Tulane virus from entering host cells.

Rapid fabrication of precise high-throughput filters from membrane protein nanosheets.

Biological membranes are ideal for separations as they provide high permeability while maintaining high solute selectivity due to the presence of specialized membrane protein (MP) channels. However, successful integration of MPs into manufactured membranes has remained a significant challenge. Here, we demonstrate a two-hour organic solvent method to develop 2D crystals and nanosheets of highly packed pore-forming MPs in block copolymers (BCPs). We then integrate these hybrid materials into scalable MP-BCP biomimetic membranes. These MP-BCP nanosheet membranes maintain the molecular selectivity of the three types of ß-barrel MP channels used, with pore sizes of 0.8 nm, 1.3 nm, and 1.5 nm. These biomimetic membranes demonstrate water permeability that is 20-1,000 times greater than that of commercial membranes and 1.5-45 times greater than that of the latest research membranes with comparable molecular exclusion ratings. This approach could provide high performance alternatives in the challenging sub-nanometre to few-nanometre size range.

7 Log Virus Removal in a Simple Functionalized Sand Filter.

Viral contamination of drinking water due to fecal contamination is difficult to detect and treat effectively, leading to frequent outbreaks worldwide. The purpose of this paper is to report on the molecular mechanism for unprecedented high virus removal from a practical sand filter. Sand filters functionalized using a water extract of Moringa oleifera (MO) seeds, functionalized sand (f-sand) filters, achieved a ∼7 log10 virus removal. These tests were conducted with MS2 bacteriophage, a recognized surrogate for pathogenic norovirus and rotavirus. We studied the molecular mechanism of this high removal since it can have important implications for sand filtration, the most common water treatment technology worldwide. Our data reveal that the virus removal activity of f-sand is due to the presence of a chitin-binding protein, M. oleifera chitin-binding protein (MoCBP) on f-sand. Standard column experiments were supported by proteomic analysis and molecular docking simulations. Our simulations show that MoCBP binds preferentially to MS2 capsid proteins demonstrating that specific molecular interactions are responsible for enhanced virus removal. In addition, we simplified the process of making f-sand and evinced how it could be regenerated using saline water. At present, no definitive solution exists for the challenge of treating fecally contaminated drinking and irrigation water for viruses without using technologies that demand high energy or chemical consumption. We propose functionalized sand (f-sand) filters as a highly effective, energy-efficient, and practical technology for virus removal applicable to both developing and developed countries.

Identification and biophysical characterization of epitope atlas of Porcine Reproductive and Respiratory Syndrome Virus.

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) have been a critical threat to swine health since 1987 due to its high mutation rate and substantial economic loss over half a billion dollar in USA. The rapid mutation rate of PRRSV presents a significant challenge in developing an effective vaccine. Even though surveillance and intervention studies have recently (2019) unveiled utilization of PRRSV glycoprotein 5 (GP5; encoded by ORF5 gene) to induce immunogenic reaction and production of neutralizing antibodies in porcine populations, the future viral generations can accrue escape mutations. In this study we identify 63 porcine-PRRSV protein-protein interactions which play primary or ancillary roles in viral entry and infection. Using genome-proteome annotation, protein structure prediction, multiple docking experiments, and binding energy calculations, we identified a list of 75 epitope locations on PRRSV proteins crucial for infection. Additionally, using machine learning-based diffusion model, we designed 56 stable immunogen peptides that contain one or more of these epitopes with their native tertiary structures stabilized through optimized N- and C-terminus flank sequences and interspersed with appropriate linker regions. Our workflow successfully identified numerous known interactions and predicted several novel PRRSV-porcine interactions. By leveraging the structural and sequence insights, this study paves the way for more effective, high-avidity, multi-valent PRRSV vaccines, and leveraging neural networks for immunogen design.

Exploring putative enteric methanogenesis inhibitors using molecular simulations and a graph neural network.

Atmospheric methane (CH4) acts as a key contributor to global warming. As CH4 is a short-lived climate forcer (12 years atmospheric lifespan), its mitigation represents the most promising means to address climate change in the short term. Enteric CH4 (the biosynthesized CH4 from the rumen of ruminants) represents 5.1% of total global greenhouse gas (GHG) emissions, 23% of emissions from agriculture, and 27.2% of global CH4 emissions. Therefore, it is imperative to investigate methanogenesis inhibitors and their underlying modes of action. We hereby elucidate the detailed biophysical and thermodynamic interplay between anti-methanogenic molecules and cofactor F430 of methyl coenzyme M reductase and interpret the stoichiometric ratios and binding affinities of sixteen inhibitor molecules. We leverage this as prior in a graph neural network to first functionally cluster these sixteen known inhibitors among ~54,000 bovine metabolites. We subsequently demonstrate a protocol to identify precursors to and putative inhibitors for methanogenesis, based on Tanimoto chemical similarity and membrane permeability predictions. This work lays the foundation for computational and de novo design of inhibitor molecules that retain/ reject one or more biochemical properties of known inhibitors discussed in this study.

Inhibiting the cGAS-STING Pathway in Ulcerative Colitis with Programmable Micelles.

Ulcerative colitis is a chronic condition in which a dysregulated immune response contributes to the acute intestinal inflammation of the colon. Current clinical therapies often exhibit limited efficacy and undesirable side effects. Here, programmable nanomicelles were designed for colitis treatment and loaded with RU.521, an inhibitor of the cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway. STING-inhibiting micelles (SIMs) comprise hyaluronic acid-stearic acid conjugates and include a reactive oxygen species (ROS)-responsive thioketal linker. SIMs were designed to selectively accumulate at the site of inflammation and trigger drug release in the presence of ROS. Our in vitro studies in macrophages and in vivo studies in a murine model of colitis demonstrated that SIMs leverage HA-CD44 binding to target sites of inflammation. Oral delivery of SIMs to mice in both preventive and delayed therapeutic models ameliorated colitis's severity by reducing STING expression, suppressing the secretion of proinflammatory cytokines, enabling bodyweight recovery, protecting mice from colon shortening, and restoring colonic epithelium. In vivo end points combined with metabolomics identified key metabolites with a therapeutic role in reducing intestinal and mucosal inflammation. Our findings highlight the significance of programmable delivery platforms that downregulate inflammatory pathways at the intestinal mucosa for managing inflammatory bowel diseases.

Protein engineering of pores for separation, sensing, and sequencing.

Proteins are critical to cellular function and survival. They are complex molecules with precise structures and chemistries, which allow them to serve diverse functions for maintaining overall cell homeostasis. Since the discovery of the first enzyme in 1833, a gamut of advanced experimental and computational tools has been developed and deployed for understanding protein structure and function. Recent studies have demonstrated the ability to redesign/alter natural proteins for applications in industrial processes of interest and to make customized, novel synthetic proteins in the laboratory through protein engineering. We comprehensively review the successes in engineering pore-forming proteins and correlate the amino acid-level biochemistry of different pore modification strategies to the intended applications limited to nucleotide/peptide sequencing, single-molecule sensing, and precise molecular separations.

A multi-organ maize metabolic model connects temperature stress with energy production and reducing power generation.

Climate change has adversely affected maize productivity. Thereby, a holistic understanding of metabolic crosstalk among its organs is important to address this issue. Thus, we reconstructed the first multi-organ maize metabolic model, iZMA6517, and contextualized it with heat and cold stress transcriptomics data using expression distributed reaction flux measurement (EXTREAM) algorithm. Furthermore, implementing metabolic bottleneck analysis on contextualized models revealed differences between these stresses. While both stresses had reducing power bottlenecks, heat stress had additional energy generation bottlenecks. We also performed thermodynamic driving force analysis, revealing thermodynamics-reducing power-energy generation axis dictating the nature of temperature stress responses. Thus, a temperature-tolerant maize ideotype can be engineered by leveraging the proposed thermodynamics-reducing power-energy generation axis. We experimentally inoculated maize root with a beneficial mycorrhizal fungus, Rhizophagus irregularis, and as a proof-of-concept demonstrated its efficacy in alleviating temperature stress. Overall, this study will guide the engineering effort of temperature stress-tolerant maize ideotypes.

Single-sequence protein structure prediction using a language model and deep learning.

AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain in (1) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated; (2) rapid exploration of designed structures; and (3) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) that uses a protein language model (AminoBERT) to learn latent structural information from unaligned proteins. A linked geometric module compactly represents Cα backbone geometry in a translationally and rotationally invariant way. On average, RGN2 outperforms AlphaFold2 and RoseTTAFold on orphan proteins and classes of designed proteins while achieving up to a 106-fold reduction in compute time. These findings demonstrate the practical and theoretical strengths of protein language models relative to MSAs in structure prediction.

A CNN model for predicting binding affinity changes between SARS-CoV-2 spike RBD variants and ACE2 homologues.

The cellular entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) involves the association of its receptor binding domain (RBD) with human angiotensin converting enzyme 2 (hACE2) as the first crucial step. Efficient and reliable prediction of RBD-hACE2 binding affinity changes upon amino acid substitutions can be valuable for public health surveillance and monitoring potential spillover and adaptation into non-human species. Here, we introduce a convolutional neural network (CNN) model trained on protein sequence and structural features to predict experimental RBD-hACE2 binding affinities of 8,440 variants upon single and multiple amino acid substitutions in the RBD or ACE2. The model achieves a classification accuracy of 83.28% and a Pearson correlation coefficient of 0.85 between predicted and experimentally calculated binding affinities in five-fold cross-validation tests and predicts improved binding affinity for most circulating variants. We pro-actively used the CNN model to exhaustively screen for novel RBD variants with combinations of up to four single amino acid substitutions and suggested candidates with the highest improvements in RBD-ACE2 binding affinity for human and animal ACE2 receptors. We found that the binding affinity of RBD variants against animal ACE2s follows similar trends as those against human ACE2. White-tailed deer ACE2 binds to RBD almost as tightly as human ACE2 while cattle, pig, and chicken ACE2s bind weakly. The model allows testing whether adaptation of the virus for increased binding with other animals would cause concomitant increases in binding with hACE2 or decreased fitness due to adaptation to other hosts.

Recombination and lineage-specific mutations linked to the emergence of SARS-CoV-2.

BACKGROUND: The emergence of SARS-CoV-2 underscores the need to better understand the evolutionary processes that drive the emergence and adaptation of zoonotic viruses in humans. In the betacoronavirus genus, which also includes SARS-CoV and MERS-CoV, recombination frequently encompasses the receptor binding domain (RBD) of the Spike protein, which is responsible for viral binding to host cell receptors. In this work, we reconstruct the evolutionary events that have accompanied the emergence of SARS-CoV-2, with a special emphasis on the RBD and its adaptation for binding to its receptor, human ACE2. METHODS: By means of phylogenetic and recombination analyses, we found evidence of a recombination event in the RBD involving ancestral linages to both SARS-CoV and SARS-CoV-2. We then assessed the effect of this recombination at protein level by reconstructing the RBD of the closest ancestors to SARS-CoV-2, SARS-CoV, and other Sarbecoviruses, including the most recent common ancestor of the recombining clade. The resulting information was used to measure and compare, in silico, their ACE2-binding affinities using the physics-based trRosetta algorithm. RESULTS: We show that, through an ancestral recombination event, SARS-CoV and SARS-CoV-2 share an RBD sequence that includes two insertions (positions 432-436 and 460-472), as well as the variants 427N and 436Y. Both 427N and 436Y belong to a helix that interacts directly with the human ACE2 (hACE2) receptor. Reconstruction of ancestral states, combined with protein-binding affinity analyses, suggests that the recombination event involving ancestral strains of SARS-CoV and SARS-CoV-2 led to an increased affinity for hACE2 binding and that alleles 427N and 436Y significantly enhanced affinity as well. CONCLUSIONS: We report an ancestral recombination event affecting the RBD of both SARS-CoV and SARS-CoV-2 that was associated with an increased binding affinity to hACE2. Structural modeling indicates that ancestors of SARS-CoV-2 may have acquired the ability to infect humans decades ago. The binding affinity with the human receptor would have been subsequently boosted in SARS-CoV and SARS-CoV-2 through further mutations in RBD.

Recombination and lineage-specific mutations linked to the emergence of SARS-CoV-2.

The emergence of SARS-CoV-2 underscores the need to better understand the evolutionary processes that drive the emergence and adaptation of zoonotic viruses in humans. In the betacoronavirus genus, which also includes SARS-CoV and MERS-CoV, recombination frequently encompasses the Receptor Binding Domain (RBD) of the Spike protein, which, in turn, is responsible for viral binding to host cell receptors. Here, we find evidence of a recombination event in the RBD involving ancestral linages to both SARS-CoV and SARS-CoV-2. Although we cannot specify the recombinant nor the parental strains, likely due to the ancestry of the event and potential undersampling, our statistical analyses in the space of phylogenetic trees support such an ancestral recombination. Consequently, SARS-CoV and SARS-CoV-2 share an RBD sequence that includes two insertions (positions 432-436 and 460-472), as well as the variants 427N and 436Y. Both 427N and 436Y belong to a helix that interacts directly with the human ACE2 (hACE2) receptor. Reconstruction of ancestral states, combined with protein-binding affinity analyses using the physics-based trRosetta algorithm, reveal that the recombination event involving ancestral strains of SARS-CoV and SARS-CoV-2 led to an increased affinity for hACE2 binding, and that alleles 427N and 436Y significantly enhanced affinity as well. Structural modeling indicates that ancestors of SARS-CoV-2 may have acquired the ability to infect humans decades ago. The binding affinity with the human receptor was subsequently boosted in SARS-CoV and SARS-CoV-2 through further mutations in RBD. In sum, we report an ancestral recombination event affecting the RBD of both SARS-CoV and SARS-CoV-2 that was associated with an increased binding affinity to hACE2.

Foldamer-based ultrapermeable and highly selective artificial water channels that exclude protons.

The outstanding capacity of aquaporins (AQPs) for mediating highly selective superfast water transport1-7 has inspired recent development of supramolecular monovalent ion-excluding artificial water channels (AWCs). AWC-based bioinspired membranes are proposed for desalination, water purification and other separation applications8-18. While some recent progress has been made in synthesizing AWCs that approach the water permeability and ion selectivity of AQPs, a hallmark feature of AQPs-high water transport while excluding protons-has not been reproduced. We report a class of biomimetic, helically folded pore-forming polymeric foldamers that can serve as long-sought-after highly selective ultrafast water-conducting channels with performance exceeding those of AQPs (1.1 × 1010 water molecules per second for AQP1), with high water-over-monovalent-ion transport selectivity (~108 water molecules over Cl- ion) conferred by the modularly tunable hydrophobicity of the interior pore surface. The best-performing AWC reported here delivers water transport at an exceptionally high rate, namely, 2.5 times that of AQP1, while concurrently rejecting salts (NaCl and KCl) and even protons.

Computational biophysical characterization of the SARS-CoV-2 spike protein binding with the ACE2 receptor and implications for infectivity.

SARS-CoV-2 is a novel highly virulent pathogen which gains entry to human cells by binding with the cell surface receptor - angiotensin converting enzyme (ACE2). We computationally contrasted the binding interactions between human ACE2 and coronavirus spike protein receptor binding domain (RBD) of the 2002 epidemic-causing SARS-CoV-1, SARS-CoV-2, and bat coronavirus RaTG13 using the Rosetta energy function. We find that the RBD of the spike protein of SARS-CoV-2 is highly optimized to achieve very strong binding with human ACE2 (hACE2) which is consistent with its enhanced infectivity. SARS-CoV-2 forms the most stable complex with hACE2 compared to SARS-CoV-1 (23% less stable) or RaTG13 (11% less stable). Notably, we calculate that the SARS-CoV-2 RBD lowers the binding strength of angiotensin 2 receptor type I (ATR1) which is the native binding partner of ACE2 by 44.2%. Strong binding is mediated through strong electrostatic attachments with every fourth residue on the N-terminus alpha-helix (starting from Ser19 to Asn53) as the turn of the helix makes these residues solvent accessible. By contrasting the spike protein SARS-CoV-2 Rosetta binding energy with ACE2 of different livestock and pet species we find strongest binding with bat ACE2 followed by human, feline, equine, canine and finally chicken. This is consistent with the hypothesis that bats are the viral origin and reservoir species. These results offer a computational explanation for the increased infection susceptibility by SARS-CoV-2 and allude to therapeutic modalities by identifying and rank-ordering the ACE2 residues involved in binding with the virus.

Engineering sensitivity and specificity of AraC-based biosensors responsive to triacetic acid lactone and orsellinic acid.

We previously described the design of triacetic acid lactone (TAL) biosensor 'AraC-TAL1', based on the AraC regulatory protein. Although useful as a tool to screen for enhanced TAL biosynthesis, this variant shows elevated background (leaky) expression, poor sensitivity and relaxed inducer specificity, including responsiveness to orsellinic acid (OA). More sensitive biosensors specific to either TAL or OA can aid in the study and engineering of polyketide synthases that produce these and similar compounds. In this work, we employed a TetA-based dual-selection to isolate new TAL-responsive AraC variants showing reduced background expression and improved TAL sensitivity. To improve TAL specificity, OA was included as a 'decoy' ligand during negative selection, resulting in the isolation of a TAL biosensor that is inhibited by OA. Finally, to engineer OA-specific AraC variants, the iterative protein redesign and optimization computational framework was employed, followed by 2 rounds of directed evolution, resulting in a biosensor with 24-fold improved OA/TAL specificity, relative to AraC-TAL1.