Biology subjects

King, N. P.

Publications and source records attributed to King, N. P..

At least 55 records · Page 3Linked to original sources

bioRxiv2023

Saponin Nanoparticle Adjuvants Incorporating Toll-Like Receptor Agonists Improve Vaccine Immunomodulation

Over the past few decades, the development of potent and safe immune-activating adjuvant technologies has become the heart of intensive research in the constant fight against highly mutative and immune evasive viruses such as influenza, SARS-CoV-2, and HIV. Herein, we developed a highly modular saponin-based nanoparticle platform incorporating toll-like receptor agonists (TLRas) including TLR1/2a, TLR4a, TLR7/8a adjuvants and their mixtures. These various TLRa-SNP adjuvant constructs induce unique acute cytokine and immune-signaling profiles, leading to specific Th-responses that could be of interest depending on the target disease for prevention. In a murine vaccine study, the adjuvants greatly improved the potency, durability, breadth, and neutralization of both COVID-19 and HIV vaccine candidates, suggesting the potential broad application of these adjuvant constructs to a range of different antigens. Overall, this work demonstrates a modular TLRa-SNP adjuvant platform which could improve the design of vaccines for and dramatically impact modern vaccine development. TeaserSaponin-TLRa nanoadjuvants provide distinct immune signatures and drive potent, broad, durable COVID-19 and HIV vaccine responses.

bioengineering↗

bioRxiv2023

Antigen spacing on protein nanoparticles influences antibody responses to vaccination

Immunogen design approaches aim to control the specificity and quality of antibody responses to enable the creation of next-generation vaccines with improved potency and breadth. However, our understanding of the relationship between immunogen structure and immunogenicity is limited. Here we use computational protein design to generate a self-assembling nanoparticle vaccine platform based on the head domain of influenza hemagglutinin (HA) that enables precise control of antigen conformation, flexibility, and spacing on the nanoparticle exterior. Domain-based HA head antigens were presented either as monomers or in a native-like closed trimeric conformation that prevents exposure of trimer interface epitopes. These antigens were connected to the underlying nanoparticle by a rigid linker that was modularly extended to precisely control antigen spacing. We found that nanoparticle immunogens with decreased spacing between closed trimeric head antigens elicited antibodies with improved hemagglutination inhibition (HAI) and neutralization potency as well as binding breadth across diverse HAs within a subtype. Our "trihead" nanoparticle immunogen platform thus enables new 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 to generate next-generation vaccines against influenza and other viruses. HIGHLIGHTSO_LIComputational design of a closed trimeric HA head ("trihead") antigen platform. C_LIO_LIDesign of a rigid, extendable linker between displayed antigen and underlying protein nanoparticle enables precise variation of antigen spacing. C_LIO_LIDecreased antigen spacing of triheads elicits antibodies with the highest HAI, neutralizing activity, and cross-reactivity. C_LIO_LIChanges to antigen spacing alter epitope specificities of vaccine-elicited antibodies. C_LI

immunology↗

bioRxiv2023

Combinatorial immune refocusing within the influenza hemagglutinin head elicits cross-neutralizing antibody responses

The head domain of influenza hemagglutinin (HA) elicits potently neutralizing yet mostly strain-specific antibodies during infection and vaccination. Here we evaluated a series of immunogens that combined several immunofocusing techniques for their ability to enhance the functional breadth of vaccine-elicited immune responses. We designed a series of "trihead" nanoparticle immunogens that display native-like closed trimeric heads from the HAs of several H1N1 influenza viruses, including hyperglycosylated variants and hypervariable variants that incorporate natural and designed sequence diversity at key positions in the periphery of the receptor binding site (RBS). Nanoparticle immunogens displaying triheads or hyperglycosylated triheads elicited higher HAI and neutralizing activity against vaccine-matched and -mismatched H1 viruses than corresponding immunogens lacking either trimer-stabilizing mutations or hyperglycosylation, indicating that both of these engineering strategies contributed to improved immunogenicity. By contrast, mosaic nanoparticle display and antigen hypervariation did not significantly alter the magnitude or breadth of vaccine-elicited antibodies. Serum competition assays and electron microscopy polyclonal epitope mapping revealed that the trihead immunogens, especially when hyperglycosylated, elicited a high proportion of antibodies targeting the RBS, as well as cross-reactive antibodies targeting a conserved epitope on the side of the head. Our results yield important insights into antibody responses against the HA head and the ability of several structure-based immunofocusing techniques to influence vaccine-elicited antibody responses. HIGHLIGHTSO_LIGeneralization of trihead antigen platform to several H1 hemagglutinins, including hyperglycosylated and hypervariable variants C_LIO_LITrimer-stabilizing mutations in trihead nanoparticle immunogens lead to lower levels of non-neutralizing antibody responses in both mice and rabbits C_LIO_LIHyperglycosylated triheads elicit higher antibody responses against broadly neutralizing epitopes C_LI

immunology↗

bioRxiv2023

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 important for targeted delivery of biologics. We describe the design of octahedral non-porous nanoparticles with the three symmetry axes (four-fold, three-fold, and two-fold) occupied by three distinct protein homooligomers: a de novo designed tetramer, an antibody of interest, and a designed trimer programmed to disassemble below a tunable pH transition point. The nanoparticles assemble cooperatively from independently purified components, and a cryo-EM density map reveals that the structure is very close to the computational design model. The designed nanoparticles can package a variety of molecular payloads, are endocytosed following antibody-mediated targeting of cell surface receptors, and undergo tunable pH-dependent disassembly at pH values ranging between to 5.9-6.7. To our knowledge, these are the first designed nanoparticles with more than two structural components and with finely tunable environmental sensitivity, and they provide new routes to antibody-directed targeted delivery.

bioengineering↗

bioRxiv2023

Extremely potent pan-sarbecovirus neutralizing antibodies generated by immunization of macaques with an AS03-adjuvanted monovalent subunit vaccine against SARS-CoV-2

The rapid emergence of SARS-CoV-2 variants that evade immunity to vaccination has placed a global health imperative on the development of therapeutic countermeasures that provide broad protection against SARS-CoV-2 and related sarbecoviruses. Here, we identified extremely potent pan-sarbecovirus antibodies from non-human primates vaccinated with an AS03 adjuvanted subunit vaccine against SARS-CoV-2 that recognize conserved epitopes in the receptor binding domain (RBD) with femtomolar affinities. Longitudinal analysis revealed progressive accumulation of somatic mutation in the immunoglobulin genes of antigen-specific memory B cells for at least one year following primary vaccination. 514 monoclonal antibodies (mAbs) were generated from antigen-specific memory B cells. Antibodies isolated at 5 to 12 months following vaccination displayed greater potency and breadth, relative to those identified at 1.4 months. Notably, 15 out of 338 ([~]4.4%) antibodies isolated at 1.4[~]6 months after the primary vaccination showed extraordinary neutralization potency against SARS-CoV-2 omicron BA.1, despite the absence of BA.1 neutralization in serum. Two of them, 25F9 and 20A7, neutralized authentic clade Ia sarbecoviruses (SARS-CoV, WIV-1, SHC014) and clade Ib sarbecoviruses (SARS-CoV-2 D614G, SARS-CoV-2 BA.1, Pangolin-GD) with half-maximal inhibition concentrations of (0.85 ng/ml, 3 ng/ml, 6 ng/ml, 6 ng/ml, 42 ng/ml, 6 ng/ml) and (13 ng/ml, 2 ng/ml, 18 ng/ml, 9 ng/ml, 6 ng/ml, 345 ng/ml), respectively. Furthermore, 20A7 and 27A12 showed potent neutralization against all SARS-CoV-2 variants of concern and multiple Omicron sublineages, including BA.1, BA.2, BA.3, BA.4/5, BQ.1, BQ.1.1 and XBB variants. X-ray crystallography studies revealed the molecular basis of broad and potent neutralization through targeting conserved RBD sites. In vivo prophylactic protection of 25F9, 20A7 and 27A12 was confirmed in aged Balb/c mice. Notably, administration of 25F9 provided complete protection against SARS-CoV-2, SARS-CoV-2 BA.1, SARS-CoV, and SHC014 challenge, underscoring that these mAbs are promising pan-sarbecovirus therapeutic antibodies. One Sentence SummaryExtremely potent pan-sarbecovirus neutralizing antibodies

immunology↗

bioRxiv2022

Broad and Durable Humoral Responses Following Single Hydrogel Immunization of SARS-CoV-2 Subunit Vaccine

Most vaccines require several immunizations to induce robust immunity, and indeed, most SARS-CoV-2 vaccines require an initial two-shot regimen followed by several boosters to maintain efficacy. Such a complex series of immunizations unfortunately increases the cost and complexity of populations-scale vaccination and reduces overall compliance and vaccination rate. In a rapidly evolving pandemic affected by the spread of immune-escaping variants, there is an urgent need to develop vaccines capable of providing robust and durable immunity. In this work, we developed a single immunization SARS-CoV-2 subunit vaccine that could rapidly generate potent, broad, and durable humoral immunity. We leveraged injectable polymer-nanoparticle (PNP) hydrogels as a depot technology for the sustained delivery of a nanoparticle COVID antigen displaying multiple copies of the SARS-CoV-2 receptor-binding-domain (RBD-NP), and potent adjuvants including CpG and 3M-052. Compared to a clinically relevant prime-boost regimen with soluble vaccines formulated with CpG/Alum or 3M-052/Alum adjuvants, PNP hydrogel vaccines more rapidly generated higher, broader, and more durable antibody responses. Additionally, these single-immunization hydrogel-based vaccines elicited potent and consistent neutralizing responses. Overall, we show that PNP hydrogels elicit improved anti-COVID immune responses with only a single administration, demonstrating their potential as critical technologies to enhance our overall pandemic readiness.

bioengineering↗

bioRxiv2022

De novo designed ice-binding proteins from twist-constrained helices

Attaining molecular-level control over solidification processes is a crucial aspect of materials science. To control ice formation, organisms have evolved bewildering arrays of ice-binding proteins (IBPs) but these have poorly understood structure-activity relationships. We propose that reverse engineering using de novo computational protein design can shed light on structureactivity relationships of IBPs. We hypothesized that the model alpha-helical winter flounder antifreeze protein (wfAFP) uses an unusual under-twisting of its alpha-helix to align its putative ice-binding threonine residues in exactly the same direction. We test this hypothesis by designing a series of straight three-helix bundles with an ice-binding helix projecting threonines and two supporting helices constraining the twist of the ice-binding helix. We find that ice recrystallization inhibition by the designed proteins increases with the degree of designed under-twisting, thus validating our hypothesis and opening up new avenues for the computational design of icebinding proteins. Significance StatementIce-binding proteins (IBPs) modulate ice nucleation and growth in cold-adapted organisms so that they can survive in ice-laden environments at (sub)freezing temperatures. The functional repertoire of IBPs is diverse, ranging from inhibition of recrystallization and freezing point depression to shaping of ice crystals and ice nucleation. Precisely how these activities arise from the structure and ice-binding properties of IBPs is poorly understood. We demonstrate through de novo computational protein design that constraining the twist of an ice-binding helix is a key feature determining its ice-binding activity, opening new avenues for the design of synthetic IBPs with activities tailored to the requirements of specific applications, such as cell and tissue cryopreservation.

bioengineering↗

bioRxiv2022

Fast and versatile sequence-independent protein docking for nanomaterials design using RPXDock

Computationally designed multi-subunit assemblies have shown considerable promise for a variety of applications, including a new generation of potent vaccines. One of the major routes to such materials is rigid body sequence-independent docking of cyclic oligomers into architectures with point group or lattice symmetries. Current methods for docking and designing such assemblies are tailored to specific classes of symmetry and are difficult to modify for novel applications. Here we describe RPXDock, a fast, flexible, and modular software package for sequence-independent rigid-body protein docking across a wide range of symmetric architectures that is easily customizable for further development. RPXDock uses an efficient hierarchical search and a residue-pair transform (RPX) scoring method to rapidly search through multidimensional docking space. We describe the structure of the software, provide practical guidelines for its use, and describe the available functionalities including a variety of score functions and filtering tools that can be used to guide and refine docking results towards desired configurations. Author SummaryProtein design methodologies are now able to generate, through a stepwise approach, a wide variety of self-assembling protein structures that begin to rival the structural complexity of naturally occurring protein nanomachines. Efficient methods for docking oligomeric protein building blocks in user-defined target symmetries are central to these techniques. We developed RPXDock as a fast and versatile method to systematically dock pre-existing proteins together into a multitude of asymmetrical and symmetrical architectures. RPXdock is also readily extendable to future applications through the addition of new symmetries, score functions, and filtering criteria.

bioengineering↗

bioRxiv2022

Top-down design of protein nanomaterials with reinforcement learning

The multisubunit protein assemblies that play critical roles in biology are the result of evolutionary selection for function of the entire assembly, and hence the subunits in structures such as icosahedral viral capsids often fit together with remarkable shape complementarity1,2. In contrast, the large multisubunit assemblies that have been created by de novo protein design, notably the icosahedral nanocages used in a new generation of potent vaccines3-7, have been built by first designing symmetric oligomers with cyclic symmetry and then assembling these into nanocages while keeping the internal structure fixed8-14, which results in more porous structures with less extensive shape matching between the components. Such hierarchical "bottom-up" design approaches have the advantage that one interface can be designed and validated in the context of the cyclic oligomer building block15,16, but the disadvantage that the structural and functional features of the assemblies are limited by the properties of the predesigned building blocks. To overcome this limitation, we set out to develop a "top-down" reinforcement learning based approach to protein nanomaterial design in which both the structures of the subunits and the interactions between them are built up coordinately in the context of the entire assembly. We developed a Monte Carlo tree search (MCTS) method17,18 which assembles protein monomer structures in the context of an overall architecture guided by a loss function which enables specification of any desired overall structural properties such as shape and porosity. We demonstrate the power of the approach by designing hyperstable icosahedral assemblies more compact than any previously observed protein icosahedral structure (designed or naturally occurring), that have very low porosity and are robust to fusion and display of proteins as complex as influenza hemagglutinin. CryoEM structures of two designs are very close to the computational design models. Our top-down reinforcement learning approach should enable the design of a wide variety of complex protein nanomaterials by direct optimization of overall system properties.

synthetic biology↗

bioRxiv2022

Improving the secretion of designed protein assemblies through negative design of cryptic transmembrane domains

Computationally designed protein nanoparticles have recently emerged as a promising platform for the development of new vaccines and biologics. For many applications, secretion of designed nanoparticles from eukaryotic cells would be advantageous, but in practice they often secrete poorly. Here we show that designed hydrophobic interfaces that drive nanoparticle assembly are often predicted to form cryptic transmembrane domains, suggesting that interaction with the membrane insertion machinery could limit efficient secretion. We develop a general computational protocol, the Degreaser, to design away cryptic transmembrane domains without sacrificing protein stability. Retroactive application of the Degreaser to previously designed nanoparticle components and nanoparticles considerably improves secretion, and modular integration of the Degreaser into design pipelines results in new nanoparticles that secrete as robustly as naturally occurring protein assemblies. Both the Degreaser protocol and the novel nanoparticles we describe may be broadly useful in biotechnological applications.

biochemistry↗

bioRxiv2022

Robust deep learning based protein sequence design using ProteinMPNN

While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here we describe a deep learning based protein sequence design method, ProteinMPNN, with outstanding performance in both in silico and experimental tests. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. Incorporation of noise during training improves sequence recovery on protein structure models, and produces sequences which more robustly encode their structures as assessed using structure prediction algorithms. We demonstrate the broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target binding proteins. One-sentence summaryA deep learning based protein sequence design method is described that is widely applicable to current design challenges and shows outstanding performance in both in silico and experimental tests.

biophysics↗

bioRxiv2022

Increasing computational protein design literacy through cohort-based learning for undergraduate students

Undergraduate research experiences can improve student success in graduate education and STEM careers. During the COVID-19 pandemic, undergraduate researchers at our institution and many others lost their work-study research positions due to interruption of in-person research activities. This imposed a financial burden on the students and eliminated an important learning opportunity. To address these challenges, we created a paid, fully-remote, cohort-based research curriculum in computational protein design. Our curriculum used existing protein design methods as a platform to first educate and train undergraduate students and then to test research hypotheses. In the first phase, students learned computational methods to assess the stability of designed protein assemblies. In the second phase, students used a larger dataset to identify factors that could improve the accuracy of current protein design algorithms. This cohort-based program created valuable new research opportunities for undergraduates at our institute and enhanced the undergraduates feeling of connection with the lab. Students learned transferable and useful skills such as literature review, programming basics, data analysis, hypothesis testing, and scientific communication. Our program provides a model of structured computational research training opportunities for undergraduate researchers in any field for organizations looking to expand educational access. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

scientific communication and education↗

bioRxiv2022

Durable protection against SARS-CoV-2 Omicron induced by an adjuvanted subunit vaccine

Despite the remarkable efficacy of COVID-19 vaccines, waning immunity, and the emergence of SARS-CoV-2 variants such as Omicron represents a major global health challenge. Here we present data from a study in non-human primates demonstrating durable protection against the Omicron BA.1 variant induced by a subunit SARS-CoV-2 vaccine, consisting of RBD (receptor binding domain) on the I53-50 nanoparticle, adjuvanted with AS03, currently in Phase 3 clinical trial (NCT05007951). Vaccination induced robust neutralizing antibody (nAb) titers that were maintained at high levels for at least one year after two doses (Pseudovirus nAb GMT: 2207, Live-virus nAb GMT: 1964) against the ancestral strain, but not against Omicron. However, a booster dose at 6-12 months with RBD-Wu or RBD-{beta} (RBD from the Beta variant) displayed on I53-50 elicited equivalent and remarkably high neutralizing titers against the ancestral as well as the Omicron variant. Furthermore, there were substantial and persistent memory T and B cell responses reactive to Beta and Omicron variants. Importantly, vaccination resulted in protection against Omicron infection in the lung (no detectable virus in any animal) and profound suppression of viral burden in the nares (median peak viral load of 7567 as opposed to 1.3x107 copies in unvaccinated animals) at 6 weeks post final booster. Even at 6 months post vaccination, there was significant protection in the lung (with 7 out of 11 animals showing no viral load, 3 out of 11 animals showing ~20-fold lower viral load than unvaccinated controls) and rapid control of virus in the nares. These results highlight the durable cross-protective immunity elicited by the AS03-adjuvanted RBD-I53-50 nanoparticle vaccine platform.

immunology↗

bioRxiv2022

Immunization with a self-assembling nanoparticle vaccine displaying EBV gH/gL protects humanized mice against lethal viral challenge

Summary/AbstractEpstein-Barr virus (EBV) is a cancer-associated pathogen responsible for 140,000 deaths per year. EBV is also the etiological agent of infectious mononucleosis and is associated with multiple sclerosis and rheumatoid arthritis. Thus, an EBV vaccine could alleviate significant morbidity and mortality. EBV is orally transmitted and has tropism for both epithelial cells and B cells which are present in the oral cavity. Therefore, a prophylactic vaccine would need to prevent infection of both cell types. Passive transfer neutralizing monoclonal antibodies targeting the viral gH/gL glycoprotein complex prevent experimental EBV infection in humanized mice and rhesus macaques, suggesting that gH/gL is an attractive vaccine candidate. Here, we produced and evaluated the immunogenicity of several nanoparticle immunogens displaying gH/gL with distinct valencies and geometries. After one or two immunizations, all nanoparticles elicited superior binding and neutralizing titers relative to monomeric gH/gL. Antibodies elicited by a computationally designed self-assembling nanoparticle that displays 60 copies of the gH/gL protein conferred protection against a lethal dose of EBV in a humanized mouse challenge model, whereas antibodies elicited by monomeric gH/gL did not. Taken together, these data motivate further development of gH/gL nanoparticle vaccine candidates for EBV.

immunology↗

bioRxiv2022

Distinct sensitivities to SARS-CoV-2 variants in vaccinated humans and mice

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019 has led to the development of a large number of vaccines, several of which are now approved for use in humans. Understanding vaccine-elicited antibody responses against emerging SARS-CoV-2 variants of concern (VOC) in real time is key to inform public health policies. Serum neutralizing antibody titers are the current best correlate of protection from SARS-CoV-2 challenge in non-human primates and a key metric to understand immune evasion of VOC. We report that vaccinated BALB/c mice do not recapitulate faithfully the breadth and potency of neutralizing antibody responses against VOC, as compared to non-human primates or humans, suggesting caution should be exercised when interpreting data for this animal model.

immunology↗

bioRxiv2021

Computational design of nanoscale rotational mechanics in de novo protein assemblies

Natural nanomachines like the F1/F0-ATPase contain protein components that undergo rotation relative to each other. Designing such mechanically constrained nanoscale protein architectures with internal degrees of freedom is an outstanding challenge for computational protein design. Here we explore the de novo construction of protein rotary machinery from designed axle and ring components. Using cryoelectron microscopy, we find that axle-ring systems assemble as designed and populate diverse rotational states depending on symmetry match or mismatch and the designed interface energy landscape. These mechanical systems with internal rotational degrees of freedom are a step towards the systematic design of genetically encodable nanomachines. One-Sentence SummaryComputationally designed self-assembling protein rotary machines sample internal degrees of freedom sculpted within the energy landscape.

synthetic biology↗

bioRxiv2021

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

Carbohydrates and glycoproteins modulate key biological functions. Computational approaches inform function to aid in carbohydrate structure prediction, structure determination, and design. However, experimental structure determination of sugar polymers is notoriously difficult as glycans can sample a wide range of low energy conformations, thus limiting the study of glycan-mediated molecular interactions. In this work, we expanded the RosettaCarbohydrate framework, developed and benchmarked effective tools for glycan modeling and design, and extended the Rosetta software suite to better aid in structural analysis and benchmarking tasks through the SimpleMetrics framework. 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. After a rigorous optimization of kinematic and energetic considerations to improve near-native sampling enrichment and decoy discrimination, GlycanTreeModeler was benchmarked on a test set of diverse glycan structures, or "trees". Structures predicted by GlycanTreeModeler agreed with native structures at high accuracy for both de novo modeling and experimental density-guided building. GlycanTreeModeler algorithms and associated tools were employed to design de novo glycan trees into a protein nanoparticle vaccine that are able to direct the immune response by shielding regions of the scaffold from antibody recognition. This work will inform glycoprotein model prediction, aid in both X-ray and electron microscopy density solutions and refinement, and help lead the way towards a new era of computational glycobiology.

molecular biology↗

bioRxiv2021

Qualification of ELISA and neutralization methodologies to measure SARS-CoV-2 humoral immunity using human clinical samples

In response to the SARS-CoV-2 pandemic many vaccines have been developed and evaluated in human clinical trials. The humoral immune response magnitude, composition and efficacy of neutralizing SARS-CoV-2 are essential endpoints for these trials. Robust assays that are reproducibly precise, linear, and specific for SARS-CoV-2 antigens would be beneficial for the vaccine pipeline. In this work we describe the methodologies and clinical qualification of three SARS-CoV-2 endpoint assays. We developed and qualified Endpoint titer ELISAs for total IgG, IgG1, IgG3, IgG4, IgM and IgA to evaluate the magnitude of specific responses to the trimeric spike (S) antigen and total IgG specific to the spike receptor binding domain (RBD) of SARS-CoV-2. We also qualified a pseudovirus neutralization assay which evaluates functional antibody titers capable of inhibiting the entry and replication of a lentivirus containing the Spike antigen of SARS-CoV-2. To complete the suite of assays we qualified a plaque reduction neutralization test (PRNT) methodology using the 2019-nCoV/USA-WA1/2020 isolate of SARS-CoV-2 to assess neutralizing titers of antibodies in plasma from normal healthy donors and convalescent COVID-19 individuals.

immunology↗