Deep mutational scanning reveals functional constraints and antibody-escape potential of Lassa virus glycoprotein complex.

Lassa virus is estimated to cause thousands of human deaths per year, primarily due to spillovers from its natural host, Mastomys rodents. Efforts to create vaccines and antibody therapeutics must account for the evolutionary variability of the Lassa virus's glycoprotein complex (GPC), which mediates viral entry into cells and is the target of neutralizing antibodies. To map the evolutionary space accessible to GPC, we used pseudovirus deep mutational scanning to measure how nearly all GPC amino-acid mutations affected cell entry and antibody neutralization. Our experiments defined functional constraints throughout GPC. We quantified how GPC mutations affected neutralization with a panel of monoclonal antibodies. All antibodies tested were escaped by mutations that existed among natural Lassa virus lineages. Overall, our work describes a biosafety-level-2 method to elucidate the mutational space accessible to GPC and shows how prospective characterization of antigenic variation could aid the design of therapeutics and vaccines.

A gH/gL-encoding replicon vaccine elicits neutralizing antibodies that protect humanized mice against EBV challenge.

Epstein-Barr virus (EBV) is associated with several malignancies, neurodegenerative disorders and is the causative agent of infectious mononucleosis. A vaccine that prevents EBV-driven morbidity and mortality remains an unmet need. EBV is orally transmitted, infecting both B cells and epithelial cells. Several virally encoded proteins are involved in entry. The gH/gL glycoprotein complex is essential for infectivity irrespective of cell type, while gp42 is essential for infection of B cells. gp350 promotes viral attachment by binding to CD21 or CD35 and is the most abundant glycoprotein on the virion. gH/gL, gp42 and gp350, are known targets of neutralizing antibodies and therefore relevant immunogens for vaccine development. Here, we developed and optimized the delivery of several alphavirus-derived replicon RNA (repRNA) vaccine candidates encoding gH/gL, gH/gL/gp42 or gp350 delivered by a cationic nanocarrier termed LION™. The lead candidate, encoding full-length gH/gL, elicited high titers of neutralizing antibodies that persisted for at least 8 months and a vaccine-specific CD8+ T cell response. Transfer of vaccine-elicited IgG protected humanized mice from EBV-driven tumor formation and death following high-dose viral challenge. These data demonstrate that LION/repRNA-gH/gL is an ideal candidate vaccine for preventing EBV infection and/or related malignancies in humans.

A low-calorie meal replacement improves body composition and metabolic parameters in shift workers with overweight and obesity: a randomized, controlled, parallel group trial.

BACKGROUND: Shift work has been identified as a risk factor for several chronic health conditions including obesity. This study evaluated the impact of a low-calorie meal replacement (MR) as a dinner substitute on body composition and metabolic parameters in shift workers with overweight and obesity. METHODS: An 8-week parallel, randomized controlled trial was conducted on overweight and obese shift workers in a large hospital. An intervention group (IG) (n = 25) was provided with a low-calorie MR shake (∼200 kcal) as a replacement for dinner, every day for 8 weeks, while the control group (CG) (n = 25) continued their habitual diet. Anthropometric measurements, body composition, biochemical, and lifestyle data were assessed at the first and last visits. Analyses were done per protocol (PP) and by intention to treat (ITT). RESULTS: Over the study duration, both groups displayed moderate changes in anthropometric measurements and body composition, although these were not statistically significant according to the PP analysis. In the ITT analysis, apart from the hip circumference (HC), all other anthropometric parameters demonstrated significant group and time interactions, suggesting the advantageous effects of the meal replacement over the study period (P < 0.05). HDL and VLDL cholesterol measures showed significant main effects, influenced by both group (P = 0.031) and time (P = 0.050) respectively. The most pronounced dietary shift in the IG was a reduction in carbohydrate consumption and an increase in protein intake. Throughout the study, the meal replacement was well-tolerated, with no adverse events reported. CONCLUSIONS: The meal replacement dietary intervention appears to offer beneficial health effects over time. Extended research is crucial to understand the broader implications of meal replacements across diverse populations. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12622000231741. Registered on 09 February 2022. https://www.anzctr.org.au/ACTRN12622000231741.aspx .

Growing Glycans in Rosetta: Accurate de novo glycan modeling, density fitting, and rational sequon design.

Carbohydrates and glycoproteins modulate key biological functions. However, experimental structure determination of sugar polymers is notoriously difficult. Computational approaches can aid in carbohydrate structure prediction, structure determination, and design. In this work, we developed a glycan-modeling algorithm, GlycanTreeModeler, that computationally builds glycans layer-by-layer, using adaptive kernel density estimates (KDE) of common glycan conformations derived from data in the Protein Data Bank (PDB) and from quantum mechanics (QM) calculations. GlycanTreeModeler was benchmarked on a test set of glycan structures of varying lengths, or "trees". Structures predicted by GlycanTreeModeler agreed with native structures at high accuracy for both de novo modeling and experimental density-guided building. We employed these tools to design de novo glycan trees into a protein nanoparticle vaccine to shield regions of the scaffold from antibody recognition, and experimentally verified shielding. This work will inform glycoprotein model prediction, glycan masking, and further aid computational methods in experimental structure determination and refinement.

A broadly generalizable stabilization strategy for sarbecovirus fusion machinery vaccines.

Evolution of SARS-CoV-2 alters the antigenicity of the immunodominant spike (S) receptor-binding domain and N-terminal domain, undermining the efficacy of vaccines and antibody therapies. To overcome this challenge, we set out to develop a vaccine focusing antibody responses on the highly conserved but metastable S2 subunit, which folds as a spring-loaded fusion machinery. We describe a strategy for prefusion-stabilization and high yield recombinant production of SARS-CoV-2 S2 trimers with native structure and antigenicity. We demonstrate that our design strategy is broadly generalizable to sarbecoviruses, as exemplified with the SARS-CoV-1 (clade 1a) and PRD-0038 (clade 3) S2 subunits. Immunization of mice with a prefusion-stabilized SARS-CoV-2 S2 trimer elicits broadly reactive sarbecovirus antibodies and neutralizing antibody titers of comparable magnitude against Wuhan-Hu-1 and the immune evasive XBB.1.5 variant. Vaccinated mice were protected from weight loss and disease upon challenge with XBB.1.5, providing proof-of-principle for fusion machinery sarbecovirus vaccines.

Deep mutational scanning of H5 hemagglutinin to inform influenza virus surveillance.

H5 influenza is considered a potential pandemic threat. Recently, H5 viruses belonging to clade 2.3.4.4b have caused large outbreaks in avian and multiple non-human mammalian species1,2. Previous studies have identified molecular phenotypes of the viral hemagglutinin (HA) protein that contribute to pandemic potential in humans, including cell entry, receptor preference, HA stability, and reduced neutralization by polyclonal sera3-6. However, prior experimental work has only measured how these phenotypes are affected by a handful of the >10,000 different possible amino-acid mutations to HA. Here we use pseudovirus deep mutational scanning7 to measure how all mutations to a 2.3.4.4b H5 HA affect each phenotype. We identify mutations that allow HA to better bind α2-6-linked sialic acids, and show that some viruses already carry mutations that stabilize HA. We also measure how all HA mutations affect neutralization by sera from mice and ferrets vaccinated against or infected with 2.3.4.4b H5 viruses. These antigenic maps enable rapid assessment of when new viral strains have acquired mutations that may create mismatches with candidate vaccine strains. Overall, the systematic nature of deep mutational scanning combined with the safety of pseudoviruses enables comprehensive measurements of the phenotypic effects of mutations that can inform real-time interpretation of viral variation observed during surveillance of H5 influenza.

Computational design of non-porous pH-responsive antibody nanoparticles.

Programming protein nanomaterials to respond to changes in environmental conditions is a current challenge for protein design and is important for targeted delivery of biologics. Here we describe the design of octahedral non-porous nanoparticles with a targeting antibody on the two-fold symmetry axis, a designed trimer programmed to disassemble below a tunable pH transition point on the three-fold axis, and a designed tetramer on the four-fold symmetry axis. Designed non-covalent interfaces guide cooperative nanoparticle assembly from independently purified components, and a cryo-EM density map closely matches the computational design model. The designed nanoparticles can package protein and nucleic acid payloads, are endocytosed following antibody-mediated targeting of cell surface receptors, and undergo tunable pH-dependent disassembly at pH values ranging between 5.9 and 6.7. The ability to incorporate almost any antibody into a non-porous pH-dependent nanoparticle opens up new routes to antibody-directed targeted delivery.

De novo design of protein minibinder agonists of TLR3.

Toll-like Receptor 3 (TLR3) is a pattern recognition receptor that initiates antiviral immune responses upon binding double-stranded RNA (dsRNA). Several nucleic acid-based TLR3 agonists have been explored clinically as vaccine adjuvants in cancer and infectious disease, but present substantial manufacturing and formulation challenges. Here, we use computational protein design to create novel miniproteins that bind to human TLR3 with nanomolar affinities. Cryo-EM structures of two minibinders in complex with TLR3 reveal that they bind the target as designed, although one partially unfolds due to steric competition with a nearby N-linked glycan. Multimeric forms of both minibinders induce NF-κB signaling in TLR3-expressing cell lines, demonstrating that they may have therapeutically relevant biological activity. Our work provides a foundation for the development of specific, stable, and easy-to-formulate protein-based agonists of TLRs and other pattern recognition receptors.

Protein nanoparticle vaccines induce potent neutralizing antibody responses against MERS-CoV.

Middle East respiratory syndrome coronavirus (MERS-CoV) is a zoonotic betacoronavirus that causes severe and often lethal respiratory illness in humans. The MERS-CoV spike (S) protein is the viral fusogen and the target of neutralizing antibodies, and has therefore been the focus of vaccine design efforts. Currently there are no licensed vaccines against MERS-CoV and only a few candidates have advanced to Phase I clinical trials. Here we developed MERS-CoV vaccines utilizing a computationally designed protein nanoparticle platform that has generated safe and immunogenic vaccines against various enveloped viruses, including a licensed vaccine for SARS-CoV-2. Two-component protein nanoparticles displaying MERS-CoV S-derived antigens induced robust neutralizing antibody responses and protected mice against challenge with mouse-adapted MERS-CoV. Electron microscopy polyclonal epitope mapping and serum competition assays revealed the specificities of the dominant antibody responses elicited by immunogens displaying the prefusion-stabilized S-2P trimer, receptor binding domain (RBD), or N-terminal domain (NTD). An RBD nanoparticle vaccine elicited antibodies targeting multiple non-overlapping epitopes in the RBD, whereas anti-NTD antibodies elicited by the S-2P- and NTD-based immunogens converged on a single antigenic site. Our findings demonstrate the potential of two-component nanoparticle vaccine candidates for MERS-CoV and suggest that this platform technology could be broadly applicable to betacoronavirus vaccine development.

KovaaK's aim trainer as a reliable metrics platform for assessing shooting proficiency in esports players: a pilot study.

Esports research lacks game-based metrics platforms appropriate for adequately capturing esports performance. The aim of this pilot study was to assess the reliability of the KovaaK's first-person shooter (FPS) aim trainer as a metrics platform for assessing shooting proficiency in esports players. Ten FPS esports players completed two identical experimental trials (T) separated by three to five days. Each trial included four rounds (R) of testing, evaluating four shooting tasks: Micro Flicking, Macro Flicking, Strafe Tracking, and Wall Peeking. Reliability of performance outcomes (e.g., accuracy, headshot accuracy, hits per second, and total shots hit) were assessed using the intraclass correlation coefficient (ICC) and their 95% confidence intervals (CI), and significant differences were identified using repeated-measures analysis of variance (RM-ANOVA). Results indicated excellent, or good to excellent reliability for all outcome variables with the ICC estimates ranging between 0.947-0.995, with lower and upper bound 95% CIs ranging between 0.876-0.988, and 0.984-0.999, respectively. Significant improvements were seen between experimental trials in the Macro Flicking task for accuracy (p = .005) and hits per second (p = .009) only. Significant interactions between trial and round were identified in the Micro Flicking task for accuracy (p = .006), with post hoc analysis showing accuracy was significantly higher in T1R1 compared to T2R1 (87.74 ± 3.13 vs. 85.99 ± 3.05, respectively, p = .02), and in T2R4 compared to T2R2 (87.99 ± 2.89 vs. 84.70 ± 4.25, respectively, p = .049). Significant interactions were also identified in the Strafe Tracking task for headshot accuracy (p = .002), with post hoc analysis showing headshot accuracy was significantly higher in T1R2 compared to T2R2 (78.48 ± 8.15 vs. 76.79 ± 12.16, respectively, p = .003), and in T1R2 compared to T1R1 (78.48 ± 8.15 vs. 73.68 ± 17.94, respectively, p = .023). In summary, this study demonstrates that KovaaK's provides a reliable metrics platform for assessing shooting proficiency in esports, however, some variability in performance was observed.

Protective human monoclonal antibodies target conserved sites of vulnerability on the underside of influenza virus neuraminidase.

Continuously evolving influenza viruses cause seasonal epidemics and pose global pandemic threats. Although viral neuraminidase (NA) is an effective drug and vaccine target, our understanding of the NA antigenic landscape still remains incomplete. Here, we describe NA-specific human antibodies that target the underside of the NA globular head domain, inhibit viral propagation of a wide range of human H3N2, swine-origin variant H3N2, and H2N2 viruses, and confer both pre- and post-exposure protection against lethal H3N2 infection in mice. Cryo-EM structures of two such antibodies in complex with NA reveal non-overlapping epitopes covering the underside of the NA head. These sites are highly conserved among N2 NAs yet inaccessible unless the NA head tilts or dissociates. Our findings help guide the development of effective countermeasures against ever-changing influenza viruses by identifying hidden conserved sites of vulnerability on the NA underside.

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN.

The design of protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.

Deep mutational scanning reveals functional constraints and antigenic variability of Lassa virus glycoprotein complex.

Lassa virus is estimated to cause thousands of human deaths per year, primarily due to spillovers from its natural host, Mastomys rodents. Efforts to create vaccines and antibody therapeutics must account for the evolutionary variability of Lassa virus's glycoprotein complex (GPC), which mediates viral entry into cells and is the target of neutralizing antibodies. To map the evolutionary space accessible to GPC, we use pseudovirus deep mutational scanning to measure how nearly all GPC amino-acid mutations affect cell entry and antibody neutralization. Our experiments define functional constraints throughout GPC. We quantify how GPC mutations affect neutralization by a panel of monoclonal antibodies and show that all antibodies are escaped by mutations that exist among natural Lassa virus lineages. Overall, our work describes a biosafety-level-2 method to elucidate the mutational space accessible to GPC and shows how prospective characterization of antigenic variation could aid design of therapeutics and vaccines.

Macromolecular Cargo Encapsulation via In Vitro Assembly of Two-Component Protein Nanoparticles.

Self-assembling protein nanoparticles are a promising class of materials for targeted drug delivery. Here, the use of a computationally designed, two-component, icosahedral protein nanoparticle is reported to encapsulate multiple macromolecular cargoes via simple and controlled self-assembly in vitro. Single-stranded RNA molecules between 200 and 2500 nucleotides in length are encapsulated and protected from enzymatic degradation for up to a month with length-dependent decay rates. Immunogenicity studies of nanoparticles packaging synthetic polymers carrying a small-molecule TLR7/8 agonist show that co-delivery of antigen and adjuvant results in a more than 20-fold increase in humoral immune responses while minimizing systemic cytokine secretion associated with free adjuvant. Coupled with the precise control over nanoparticle structure offered by computational design, robust and versatile encapsulation via in vitro assembly opens the door to a new generation of cargo-loaded protein nanoparticles that can combine the therapeutic effects of multiple drug classes.

Can smartphone-based response inhibition training elicit sustained changes in appetite, preference, and cravings for energy-dense foods? A free-living randomized controlled trial.

BACKGROUND: Food-specific response inhibition training has been implemented as a strategy to modify food choices and reward-related eating behaviours, but short-term studies have produced equivocal findings. OBJECTIVE: To longitudinally assess the effect of a smartphone-based response inhibition intervention on food reward, hedonic eating drive, and cravings in a free-living setting. METHODS: 84 adults (Mage = 30.49, SDage = 13.01, 52 female) with high responsivity to food cues or overweight/obesity were randomly assigned to a response inhibition training intervention (n = 45) or a control game (n = 39) at home during a training week, followed by a week with no training. Primary analyses compared groups on measures of explicit liking and implicit wanting for food of different energy densities, food cravings, and reward-related eating throughout this two-week period. RESULTS: A reduction was observed in explicit liking and implicit wanting for energy-dense foods from baseline to post-training independent of condition (ps < .001). These changes from baseline were sustained after a 1-week latency period, also independent of condition (ps < .001). These effects coincided with similar observations of hedonic eating drive, tonic cravings, and control over cravings during the observation period (ps < .01). CONCLUSIONS: Although significant reductions in reward-related appetite were observed, free-living response inhibition training did not offer additional benefit over a control activity. Future intervention studies with observable food intake are needed to investigate which appetitive mechanisms most reliably predict eating behaviour over time. TRIAL REGISTRATION: Retrospectively registered with ANZCTR [ACTRN12622001502729].

Response inhibition training as an intervention to modify liking and wanting for foods based on energy density: a proof of concept study.

The ubiquity of energy-dense, processed foods has been implicated as a salient feature of the modern 'obesogenic' environment. Cognitive strategies, such as response inhibition training, have been demonstrated to reduce the hedonic value of such foods in previous studies. However, this effect has generally been inconsistent or heterogenous, depending on the outcome measure, characteristics of the sample, and the specificity of food stimuli. Characterising the extent of generalised effects may help define the application of this type of intervention in natural settings. A repeated-measures, proof-of-concept study, using mobile app-based response inhibition training (RIT) versus a control app-based activity (N = 25), was undertaken to establish the valid application of a food reward measure to assess intervention efficacy. Liking (i.e., affect) and wanting (i.e., motivation) for food stimuli categorised by energy density were taken concurrently pre- and post-training. A statistically significant reduction in explicit liking, but not implicit wanting, for foods irrespective of their energy density was observed during the RIT app-based training session relative to the control (p = .041, ηp2 = .16). However, effect sizes associated with devaluation of energy-dense relative to low calorie food stimuli, although non-significant, were higher when measured as implicitly wanting (p = .098, ηp2 = .11) than explicit liking (p = .756, ηp2 = .00). Trends in explicit stimulus evaluations were empirically discordant from implicit evaluations for low calorie foods in particular. Additional research is needed to investigate whether these trends are reproducible with larger samples, trained and novel food stimuli in outcome measures, and more comprehensive training protocols.

Combinatorial immune refocusing within the influenza hemagglutinin RBD improves cross-neutralizing antibody responses.

The receptor-binding domain (RBD) of influenza virus hemagglutinin (HA) elicits potently neutralizing yet mostly strain-specific antibodies. Here, we evaluate the ability of several immunofocusing techniques to enhance the functional breadth of vaccine-elicited immune responses against the HA RBD. We present a series of "trihead" nanoparticle immunogens that display native-like closed trimeric RBDs from the HAs of several H1N1 influenza viruses. The series includes hyperglycosylated and hypervariable variants that incorporate natural and designed sequence diversity at key positions in the receptor-binding site periphery. Nanoparticle immunogens displaying triheads or hyperglycosylated triheads elicit higher hemagglutination inhibition (HAI) and neutralizing activity than the corresponding immunogens lacking either trimer-stabilizing mutations or hyperglycosylation. By contrast, mosaic nanoparticle display and antigen hypervariation do not significantly alter the magnitude or breadth of vaccine-elicited antibodies. Our results yield important insights into antibody responses against the RBD and the ability of several structure-based immunofocusing techniques to influence vaccine-elicited antibody responses.

Antigen spacing on protein nanoparticles influences antibody responses to vaccination.

Immunogen design approaches aim to control the specificity and quality of antibody responses elicited by next-generation vaccines. Here, we use computational protein design to generate a nanoparticle vaccine platform based on the receptor-binding domain (RBD) of influenza hemagglutinin (HA) that enables precise control of antigen conformation and spacing. HA RBDs are presented as either monomers or native-like closed trimers that are connected to the underlying nanoparticle by a rigid linker that is modularly extended to precisely control antigen spacing. Nanoparticle immunogens with decreased spacing between trimeric RBDs elicit antibodies with improved hemagglutination inhibition and neutralization potency as well as binding breadth across diverse H1 HAs. Our "trihead" nanoparticle immunogen platform provides insights into anti-HA immunity, establishes antigen spacing as an important parameter in structure-based vaccine design, and embodies several design features that could be used in next-generation vaccines against influenza and other viruses.

In vivo selection of synthetic nucleocapsids for tissue targeting.

Controlling the biodistribution of protein- and nanoparticle-based therapeutic formulations remains challenging. In vivo library selection is an effective method for identifying constructs that exhibit desired distribution behavior; library variants can be selected based on their ability to localize to the tissue or compartment of interest despite complex physiological challenges. Here, we describe further development of an in vivo library selection platform based on self-assembling protein nanoparticles encapsulating their own mRNA genomes (synthetic nucleocapsids or synNCs). We tested two distinct libraries: a low-diversity library composed of synNC surface mutations (45 variants) and a high-diversity library composed of synNCs displaying miniproteins with binder-like properties (6.2 million variants). While we did not identify any variants from the low-diversity surface library that yielded therapeutically relevant changes in biodistribution, the high-diversity miniprotein display library yielded variants that shifted accumulation toward lungs or muscles in just two rounds of in vivo selection. Our approach should contribute to achieving specific tissue homing patterns and identifying targeting ligands for diseases of interest.

Broad receptor tropism and immunogenicity of a clade 3 sarbecovirus.

Although Rhinolophus bats harbor diverse clade 3 sarbecoviruses, the structural determinants of receptor tropism along with the antigenicity of their spike (S) glycoproteins remain uncharacterized. Here, we show that the African Rhinolophus bat clade 3 sarbecovirus PRD-0038 S has a broad angiotensin-converting enzyme 2 (ACE2) usage and that receptor-binding domain (RBD) mutations further expand receptor promiscuity and enable human ACE2 utilization. We determine a cryo-EM structure of the PRD-0038 RBD bound to Rhinolophus alcyone ACE2, explaining receptor tropism and highlighting differences with SARS-CoV-1 and SARS-CoV-2. Characterization of PRD-0038 S using cryo-EM and monoclonal antibody reactivity reveals its distinct antigenicity relative to SARS-CoV-2 and identifies PRD-0038 cross-neutralizing antibodies for pandemic preparedness. PRD-0038 S vaccination elicits greater titers of antibodies cross-reacting with vaccine-mismatched clade 2 and clade 1a sarbecoviruses compared with SARS-CoV-2 S due to broader antigenic targeting, motivating the inclusion of clade 3 antigens in next-generation vaccines for enhanced resilience to viral evolution.