Rapid and Scalable Production of Functional SARS-CoV-2 Virus-like Particles (VLPs) by a Stable HEK293 Cell Pool.

At times of pandemics, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the situation demands rapid development and production timelines of safe and effective vaccines for delivering life-saving medications quickly to patients. Typical biologics production relies on using the lengthy and arduous approach of stable single-cell clones. Here, we used an alternative approach, a stable cell pool that takes only weeks to generate compared to a stable single-cell clone that needs several months to complete. We employed the membrane, envelope, and highly immunogenic spike proteins of SARS-CoV-2 to produce virus-like particles (VLPs) using the HEK293-F cell line as a host system with an economical transfection reagent. The cell pool showed the stability of protein expression for more than one month. We demonstrated that the production of SARS-CoV-2 VLPs using this cell pool was scalable up to a stirred-tank 2 L bioreactor in fed-batch mode. The purified VLPs were properly assembled, and their size was consistent with the authentic virus. Our particles were functional as they specifically entered the cell that naturally expresses ACE-2. Notably, this work reports a practical and cost-effective manufacturing platform for scalable SARS-CoV-2 VLPs production and chromatographic purification.

Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning.

The application of machine learning (ML) models to optimize antibody affinity to an antigen is gaining prominence. Unfortunately, the small and biased nature of the publicly available antibody-antigen interaction datasets makes it challenging to build an ML model that can accurately predict binding affinity changes due to mutations (ΔΔG). Recognizing these inherent limitations, we reformulated the problem to ask whether an ML model capable of classifying deleterious vs non-deleterious mutations can guide antibody affinity maturation in a practical setting. To test this hypothesis, we developed a Random Forest classifier (Antibody Random Forest Classifier or AbRFC) with expert-guided features and integrated it into a computational-experimental workflow. AbRFC effectively predicted non-deleterious mutations on an in-house validation dataset that is free of biases seen in the publicly available training datasets. Furthermore, experimental screening of a limited number of predictions from the model (<10^2 designs) identified affinity-enhancing mutations in two unrelated SARS-CoV-2 antibodies, resulting in constructs with up to 1000-fold increased binding to the SARS-COV-2 RBD. Our findings indicate that accurate prediction and screening of non-deleterious mutations using machine learning offers a powerful approach to improving antibody affinity.

Intact mass analysis reveals the novel O-linked glycosylation on the stalk region of PD-1 protein.

Programmed cell death protein 1 (PD-1) is a key receptor in the immune checkpoint pathway and has emerged to be a promising target for cancer therapy. PD-1 consists of an intracellular domain followed by a transmembrane domain that is connected to the extracellular domain by the stalk region. Although the PD-1 structure has been studied for more than two decades, the posttranslational modification of this protein has been incompletely characterized. In this study, we identified the previously undescribed modification sites of O-linked glycan on the stalk region of PD-1 protein using O-protease digestion coupling with intact mass analysis. The result indicates that T153, S157, S159, and T168 are modified by sialylated mucin-type O-glycan with core 1- and core 2-based structures. This study provides both information on potential novel modification sites on the PD-1 protein and an attractive method for identifying O-linked glycosylation using a specific enzyme and intact mass analysis.

Learned features of antibody-antigen binding affinity.

Defining predictors of antigen-binding affinity of antibodies is valuable for engineering therapeutic antibodies with high binding affinity to their targets. However, this task is challenging owing to the huge diversity in the conformations of the complementarity determining regions of antibodies and the mode of engagement between antibody and antigen. In this study, we used the structural antibody database (SAbDab) to identify features that can discriminate high- and low-binding affinity across a 5-log scale. First, we abstracted features based on previously learned representations of protein-protein interactions to derive 'complex' feature sets, which include energetic, statistical, network-based, and machine-learned features. Second, we contrasted these complex feature sets with additional 'simple' feature sets based on counts of contacts between antibody and antigen. By investigating the predictive potential of 700 features contained in the eight complex and simple feature sets, we observed that simple feature sets perform comparably to complex feature sets in classification of binding affinity. Moreover, combining features from all eight feature-sets provided the best classification performance (median cross-validation AUROC and F1-score of 0.72). Of note, classification performance is substantially improved when several sources of data leakage (e.g., homologous antibodies) are not removed from the dataset, emphasizing a potential pitfall in this task. We additionally observe a classification performance plateau across diverse featurization approaches, highlighting the need for additional affinity-labeled antibody-antigen structural data. The findings from our present study set the stage for future studies aimed at multiple-log enhancement of antibody affinity through feature-guided engineering.

Chronic sequelae complicate convalescence from both dengue and acute viral respiratory illness.

Long Covid has raised awareness of the potentially disabling chronic sequelae that afflicts patients after acute viral infection. Similar syndromes of post-infectious sequelae have also been observed after other viral infections such as dengue, but their true prevalence and functional impact remain poorly defined. We prospectively enrolled 209 patients with acute dengue (n = 48; one with severe dengue) and other acute viral respiratory infections (ARI) (n = 161), and followed them up for chronic sequelae up to one year post-enrolment, prior to the onset of the Covid-19 pandemic. Baseline demographics and co-morbidities were balanced between both groups except for gender, with more males in the dengue cohort (63% vs 29%, p<0.001). Except for the first visit, data on symptoms were collected remotely using a purpose-built mobile phone application. Mental health outcomes were evaluated using the validated SF-12v2 Health Survey. Almost all patients (95.8% of dengue and 94.4% of ARI patients) experienced at least one symptom of fatigue, somnolence, headache, concentration impairment or memory impairment within the first week of enrolment. Amongst patients with at least 3-months of follow-up, 18.0% in the dengue cohort and 14.6% in the ARI cohort experienced persistent symptoms. The median month-3 SF-12v2 Mental Component Summary Score was lower in patients who remained symptomatic at 3 months and beyond, compared to those whose symptoms fully resolved (47.7 vs. 56.0, p<0.001), indicating that patients who self-reported persistence of symptoms also experienced functionally worse mental health. No statistically significant difference in age, gender distribution or hospitalisation status was observed between those with and without chronic sequelae. Our findings reveal an under-appreciated burden of post-infection chronic sequelae in dengue and ARI patients. They call for studies to define the pathophysiology of this condition, and determine the efficacy of both vaccines as well as antiviral drugs in preventing such sequelae.

Prediction of the structural interface between fibroblast growth factor23 and Burosumab using alanine scanning and molecular docking.

Burosumab, an FGF23 targeting monoclonal antibody, was approved by the FDA in 2018 for use in children and adults with X-linked hypophosphatemia (or XLH). While several clinical studies have demonstrated the long-term safety and efficacy of Burosumab, the molecular basis of FGF23-Burosumab interaction which underpins its mechanism of action remains unknown. In this study, we employed molecular docking combined with alanine scanning of epitope and paratope to predict a model of FGF23-Burosumab interaction. Then, we used the model to understand the species-species cross-reactivity of Burosumab and to reverse engineer mouse FGF23 with 'back to human' mutations to bind Burosumab. Finally, we redesigned the CDRs with two mutations to engineer an affinity enhanced variant of the antibody. Our study provides insights into the FGF23-Burosumab interaction and demonstrates that alanine-scanning coupled with molecular docking can be used to optimize antibody candidates (e.g., structure-guided affinity maturation) for therapeutic use.

Complexity of Viral Epitope Surfaces as Evasive Targets for Vaccines and Therapeutic Antibodies.

The dynamic interplay between virus and host plays out across many interacting surfaces as virus and host evolve continually in response to one another. In particular, epitope-paratope interactions (EPIs) between viral antigen and host antibodies drive much of this evolutionary race. In this review, we describe a series of recent studies examining aspects of epitope complexity that go beyond two interacting protein surfaces as EPIs are typically understood. To structure our discussion, we present a framework for understanding epitope complexity as a spectrum along a series of axes, focusing primarily on 1) epitope biochemical complexity (e.g., epitopes involving N-glycans) and 2) antigen conformational/dynamic complexity (e.g., epitopes with differential properties depending on antigen state or fold-axis). We highlight additional epitope complexity factors including epitope tertiary/quaternary structure, which contribute to epistatic relationships between epitope residues within- or adjacent-to a given epitope, as well as epitope overlap resulting from polyclonal antibody responses, which is relevant when assessing antigenic pressure against a given epitope. Finally, we discuss how these different forms of epitope complexity can limit EPI analyses and therapeutic antibody development, as well as recent efforts to overcome these limitations.

A structural dynamic explanation for observed escape of SARS-CoV-2 BA.2 variant mutation S371L/F.

The SARS-CoV-2 Omicron sub-variants BA.1 and BA.2 have become the dominant variants worldwide due to enhanced transmissibility and immune evasion. In response to the rise of BA.1 and BA.2, two recent studies by Liu et al. and Iketani et al. provide a detailed analysis of loss of therapeutic antibody potency through evaluation of escape by pseudotyped viruses harboring BA.1 and BA.2 receptor binding domain (RBD) point mutations. Surprisingly, Liu et al. and Iketani et al. observed a profoundly broad escape effect for the individual mutations S371L and S371F. This result cannot be explained by known escape mechanisms of the SARS-CoV-2 RBD, and conflicts with existing computational and experimental escape measurements for S371 mutations performed on monomeric RBD. Through an examination of these conflicting datasets and a structural analysis of the antibodies assayed by Liu et al. and Iketani et al., we propose a mechanism to explain S371L/F escape according to a perturbation of spike trimer conformational dynamics that has not yet been described for any SARS-CoV-2 escape mutation. The proposed mechanism is relevant to Omicron and future variant surveillance as well as therapeutic antibody design.

Insights on the mutational landscape of the SARS-CoV-2 Omicron variant receptor-binding domain.

The Omicron variant features enhanced transmissibility and antibody escape. Here, we describe the Omicron receptor-binding domain (RBD) mutational landscape using amino acid interaction (AAI) networks, which are well suited for interrogating constellations of mutations that function in an epistatic manner. Using AAI, we map Omicron mutations directly and indirectly driving increased escape breadth and depth in class 1-4 antibody epitopes. Further, we present epitope networks for authorized therapeutic antibodies and assess perturbations to each antibody's epitope. Since our initial modeling following the identification of Omicron, these predictions have been realized by experimental findings of Omicron neutralization escape from therapeutic antibodies ADG20, AZD8895, and AZD1061. Importantly, the AAI predicted escape resulting from indirect epitope perturbations was not captured by previous sequence or point mutation analyses. Finally, for several Omicron RBD mutations, we find evidence for a plausible role in enhanced transmissibility via disruption of RBD-down conformational stability at the RBDdown-RBDdown interface.

Conserved topology of virus glycoepitopes presents novel targets for repurposing HIV antibody 2G12.

Complex glycans decorate viral surface proteins and play a critical role in virus-host interactions. Viral surface glycans shield vulnerable protein epitopes from host immunity yet can also present distinct "glycoepitopes" that can be targeted by host antibodies such as the potent anti-HIV antibody 2G12 that binds high-mannose glycans on gp120. Two recent publications demonstrate 2G12 binding to high mannose glycans on SARS-CoV-2 and select Influenza A (Flu) H3N2 viruses. Previously, our lab observed 2G12 binding and functional inhibition of a range of Flu viruses that include H3N2 and H1N1 lineages. In this manuscript, we present these data alongside structural analyses to offer an expanded picture of 2G12-Flu interactions. Further, based on the remarkable breadth of 2G12 N-glycan recognition and the structural factors promoting glycoprotein oligomannosylation, we hypothesize that 2G12 glycoepitopes can be defined from protein structure alone according to N-glycan site topology. We develop a model describing 2G12 glycoepitopes based on N-glycan site topology, and apply the model to identify viruses within the Protein Data Bank presenting putative 2G12 glycoepitopes for 2G12 repurposing toward analytical, diagnostic, and therapeutic applications.

Capillary Electrophoretic Analysis of Isolated Sulfated Polysaccharides to Characterize Pharmaceutical Products.

Capillary electrophoresis is a powerful methodology for quantification and structural characterization of highly anionic polysaccharides. Separation of saccharides under conditions of electrophoretic flow, typically achieved under low pH (Ampofo et al., Anal Biochem 199: 249-255, 1991; Rhomberg et al., Proc Natl Acad Sci U S A 95: 4176-4181, 1998) is charge-based. Resolution of components is often superior to flow-based techniques, such as liquid chromatography. During the heparin contamination crisis, capillary electrophoresis was one of the key methodologies used to identify whether or not heparin lots were contaminated (Guerrini et al., Nat Biotechnol 26: 669-675, 2008; Ye et al., J Pharm Biomed Anal 85: 99-107, 2013; Volpi et al., Electrophoresis 33: 1531-1537, 2012).Here we describe a method for the isolation of sulfated heparin/heparan sulfate saccharides from urine, their digestion by deployment of heparinase enzymes (Ernst et al., Crit Rev Biochem Mol Biol 30: 387-444, 1995) resolution of species through use of orthogonal digestions, and analysis of the resulting disaccharides by capillary electrophoresis.

Generation of bispecific antibodies by structure-guided redesign of IgG constant regions.

Bispecific antibodies (BsAbs) form an exciting class of bio-therapeutics owing to their multispecificity. Although numerous formats have been developed, generation of hetero-tetrameric IgG1-like BsAbs having acceptable safety and pharmacokinetics profiles from a single cell culture system remains challenging due to the heterogeneous pairing between the four chains. Herein, we employed a structure-guided approach to engineer mutations in the constant domain interfaces (CH1-CL and CH3-CH3) of heavy and κ light chains to prevent heavy-light mispairing in the antigen binding fragment (Fab) region and heavy-heavy homodimerization in the Fc region. Transient co-transfection of mammalian cells with heavy and light chains of pre-existing antibodies carrying the engineered constant domains generates BsAbs with percentage purity ranging from 78% to 85%. The engineered BsAbs demonstrate simultaneous binding of both antigens, while retaining the thermal stability, Fc-mediated effector properties and FcRn binding properties of the parental antibodies. Importantly, since the variable domains were not modified, the mutations may enable BsAb formation from antibodies belonging to different germline origins and isotypes. The rationally designed mutations reported in this work could serve as a starting point for generating optimized solutions required for large scale production.

Insights on the mutational landscape of the SARS-CoV-2 Omicron variant.

The SARS-COV2 Omicron variant has sparked global concern due to the possibility of enhanced transmissibility and escape from vaccines and therapeutics. In this study, we describe the mutational landscape of the Omicron variant using amino acid interaction (AAI) networks. AAI network analysis is particularly well suited for interrogating the impact of constellations of mutations as occur on Omicron that may function in an epistatic manner. Our analyses suggest that as compared to previous variants of concern, the Omicron variant has increased antibody escape breadth due to mutations in class 3 and 4 antibody epitopes as well as increased escape depth due to accumulated mutations in class 1 antibody epitopes. We note certain RBD mutations that might further enhance Omicron's escape, and in particular advise careful surveillance of two subclades bearing R346S/K mutations with relevance for certain therapeutic antibodies. Further, AAI network analysis suggests that the function of certain therapeutic monoclonal antibodies may be disrupted by Omicron mutations as a result of the cumulative indirect perturbations to the epitope surface properties, despite point-mutation analyses suggesting these antibodies are tolerant of the set of Omicron mutations in isolation. Finally, for several Omicron mutations that do not appear to contribute meaningfully to antibody escape, we find evidence for a plausible role in enhanced transmissibility via disruption of RBD-down conformational stability at the RBD-RBD interface.

An Antigenic Space Framework for Understanding Antibody Escape of SARS-CoV-2 Variants.

The evolution of mutations in SARS-CoV-2 at antigenic sites that impact neutralizing antibody responses in humans poses a risk to immunity developed through vaccination and natural infection. The highly successful RNA-based vaccines have enabled rapid vaccine updates that incorporate mutations from current variants of concern (VOCs). It is therefore important to anticipate future antigenic mutations as the virus navigates the heterogeneous global landscape of host immunity. Toward this goal, we survey epitope-paratope interfaces of anti-SARS-CoV-2 antibodies to map an antigenic space that captures the role of each spike protein residue within the polyclonal antibody response directed against the ACE2-receptor binding domain (RBD) or the N-terminal domain (NTD). In particular, the antigenic space map builds on recently published epitope definitions by annotating epitope overlap and orthogonality at the residue level. We employ the antigenic space map as a framework to understand how mutations on nine major variants contribute to each variant's evasion of neutralizing antibodies. Further, we identify constellations of mutations that span the orthogonal epitope regions of the RBD and NTD on the variants with the greatest antibody escape. Finally, we apply the antigenic space map to predict which regions of antigenic space-should they mutate-may be most likely to complementarily augment antibody evasion for the most evasive and transmissible VOCs.

Glycans in Virus-Host Interactions: A Structural Perspective.

Many interactions between microbes and their hosts are driven or influenced by glycans, whose heterogeneous and difficult to characterize structures have led to an underappreciation of their role in these interactions compared to protein-based interactions. Glycans decorate microbe glycoproteins to enhance attachment and fusion to host cells, provide stability, and evade the host immune system. Yet, the host immune system may also target these glycans as glycoepitopes. In this review, we provide a structural perspective on the role of glycans in host-microbe interactions, focusing primarily on viral glycoproteins and their interactions with host adaptive immunity. In particular, we discuss a class of topological glycoepitopes and their interactions with topological mAbs, using the anti-HIV mAb 2G12 as the archetypical example. We further offer our view that structure-based glycan targeting strategies are ready for application to viruses beyond HIV, and present our perspective on future development in this area.

Mapping Potential Antigenic Drift Sites (PADS) on SARS-CoV-2 Spike in Continuous Epitope-Paratope Space.

SARS-CoV-2 mutations with antigenic effects pose a risk to immunity developed through vaccination and natural infection. While vaccine updates for current variants of concern (VOCs) are underway, it is likewise important to prepare for further antigenic mutations as the virus navigates the heterogeneous global landscape of host immunity. Toward this end, a wealth of data and tools exist that can augment existing genetic surveillance of VOC evolution. In this study, we integrate published datasets describing genetic, structural, and functional constraints on mutation along with computational analyses of antibody-spike co-crystal structures to identify a set of potential antigenic drift sites (PADS) within the receptor binding domain (RBD) and N-terminal domain (NTD) of SARS-CoV-2 spike protein. Further, we project the PADS set into a continuous epitope-paratope space to facilitate interpretation of the degree to which newly observed mutations might be antigenically synergistic with existing VOC mutations, and this representation suggests that functionally convergent and synergistic antigenic mutations are accruing across VOC NTDs. The PADS set and synergy visualization serve as a reference as new mutations are detected on VOCs, enable proactive investigation of potentially synergistic mutations, and offer guidance to antibody and vaccine design efforts.

Modified recombinant human erythropoietin with potentially reduced immunogenicity.

Recombinant human erythropoietin (rHuEPO) is a biopharmaceutical drug given to patients who have a low hemoglobin related to chronic kidney disease, cancer or anemia. However, some patients repeatedly receiving rHuEPO develop anti-rHuEPO neutralizing antibodies leading to the development of pure red cell aplasia (PRCA). The immunogenic antibody response activated by rHuEPO is believed to be triggered by T-cells recognizing EPO epitopes bound to MHC molecules displayed on the cell surface of APCs. Previous studies have reported an association between the development of anti-rHuEpo-associated PRCA and the HLA-DRB1*09 gene, which is reported to be entrenched in the Thai population. In this study, we used computational design to screen for immunogenic hotspots recognized by HLA-DRB1*09, and predicted seventeen mutants having anywhere between one through four mutations that reduce affinity for the allele, without disrupting the structural integrity and bioactivity. Five out of seventeen mutants were less immunogenic in vitro while retaining similar or slightly reduced bioactivity than rHuEPO. These engineered proteins could be the potential candidates to treat patients who are rHuEpo-dependent and express the HLA-DRB1*09 allele.

Prediction of the binding interface between monoclonal antibody m102.4 and Nipah attachment glycoprotein using structure-guided alanine scanning and computational docking.

Nipah Virus (NiV) has been designated as a priority disease with an urgent need for therapeutic development by World Health Organization. The monoclonal antibody m102.4 binds to the immunodominant NiV receptor-binding glycoprotein (GP), and potently neutralizes NiV, indicating its potential as a therapeutic agent. Although the co-crystal structure of m102.3, an m102.4 derivative, in complex with the GP of the related Hendra Virus (HeV) has been solved, the structural interaction between m102.4 and NiV is uncharacterized. Herein, we used structure-guided alanine-scanning mutagenesis to map the functional epitope and paratope residues that govern the antigen-antibody interaction. Our results revealed that the binding of m102.4 is mediated predominantly by two residues in the HCDR3 region, which is unusually small for an antibody-antigen interaction. We performed computational docking to generate a structural model of m102.4-NiV interaction. Our model indicates that m102.4 targets the common hydrophobic central cavity and a hydrophilic rim on the GP, as observed for the m102.3-HeV co-crystal, albeit with Fv orientation differences. In summary, our study provides insight into the m102.4-NiV interaction, demonstrating that structure-guided alanine-scanning and computational modeling can serve as the starting point for additional antibody reengineering (e.g. affinity maturation) to generate potential therapeutic candidates.

Phase 1 Trial of a Therapeutic Anti-Yellow Fever Virus Human Antibody.

BACKGROUND: Insufficient vaccine doses and the lack of therapeutic agents for yellow fever put global health at risk, should this virus emerge from sub-Saharan Africa and South America. METHODS: In phase 1a of this clinical trial, we assessed the safety, side-effect profile, and pharmacokinetics of TY014, a fully human IgG1 anti-yellow fever virus monoclonal antibody. In a double-blind, phase 1b clinical trial, we assessed the efficacy of TY014, as compared with placebo, in abrogating viremia related to the administration of live yellow fever vaccine (YF17D-204; Stamaril). The primary safety outcomes were adverse events reported 1 hour after the infusion and throughout the trial. The primary efficacy outcome was the dose of TY014 at which 100% of the participants tested negative for viremia within 48 hours after infusion. RESULTS: A total of 27 healthy participants were enrolled in phase 1a, and 10 participants in phase 1b. During phase 1a, TY014 dose escalation to a maximum of 20 mg per kilogram of body weight occurred in 22 participants. During phases 1a and 1b, adverse events within 1 hour after infusion occurred in 1 of 27 participants who received TY014 and in none of the 10 participants who received placebo. At least one adverse event occurred during the trial in 22 participants who received TY014 and in 8 who received placebo. The mean half-life of TY014 was approximately 12.8 days. At 48 hours after the infusion, none of the 5 participants who received the starting dose of TY014 of 2 mg per kilogram had detectable YF17D-204 viremia; these participants remained aviremic throughout the trial. Viremia was observed at 48 hours after the infusion in 2 of 5 participants who received placebo and at 72 hours in 2 more placebo recipients. Symptoms associated with yellow fever vaccine were less frequent in the TY014 group than in the placebo group. CONCLUSIONS: This phase 1 trial of TY014 did not identify worrisome safety signals and suggested potential clinical benefit, which requires further assessment in a phase 2 trial. (Funded by Tysana; ClinicalTrials.gov number, NCT03776786.).