Harnessing Avidity: Quantifying the Entropic and Energetic Effects of Linker Length and Rigidity for Multivalent Binding of Antibodies to HIV-1

Bioluminescent detection of viral surface proteins using branched multivalent protein switches

Fast and reliable virus diagnostics is key to prevent the spread of viruses in populations. A hallmark of viruses is the presence of multivalent surface proteins, a property that can be harnessed to control conformational switching in sensor proteins. Here, we introduce a new sensor platform (dark-LUX) for the detection of viral surface proteins consisting of a general bioluminescent framework that can be post-translationally functionalized with separately expressed binding domains. The platform relies on (1) plug-and-play bioconjugation of different binding proteins via SpyTag/SpyCatcher technology to create branched protein structures, (2) an optimized turn-on bioluminescent switch based on complementation of the split-luciferase NanoBiT upon target binding and (3) straightforward exploration of the protein linker space. The influenza A virus (IAV) surface proteins hemagglutinin (HA) and neuraminidase (NA) were used as relevant multivalent targets to establish proof of principle and optimize relevant parameters such as linker properties, choice of target binding domains and the optimal combination of the competing NanoBiT components SmBiT and DarkBiT. The sensor framework allows rapid conjugation and exchange of various binding domains including scFvs, nanobodies and de novo designed binders for a variety of targets, including the construction of a heterobivalent switch that targets the head and stem region of hemagglutinin. The modularity of the platform thus allows straightforward optimization of binding domains and scaffold properties for existing viral targets, and is well suited to quickly adapt bioluminescent sensor proteins to effectively detect newly evolving viral epitopes.

Cleavage-intermediate Lassa virus trimer elicits neutralizing responses, identifies neutralizing nanobodies, and reveals an apex-situated site-of-vulnerability

Lassa virus (LASV) infection is expanding outside its traditionally endemic areas in West Africa, posing a pandemic biothreat. LASV-neutralizing antibodies, moreover, have proven difficult to elicit. To gain insight into LASV neutralization, here we develop a prefusion-stabilized LASV glycoprotein trimer (GPC), pan it against phage libraries comprising single-domain antibodies (nanobodies) from shark and camel, and identify one, D5, which neutralizes LASV. Cryo-EM analyses reveal D5 to recognize a cleavage-dependent site-of-vulnerability at the trimer apex. The recognized site appears specific to GPC intermediates, with protomers lacking full cleavage between GP1 and GP2 subunits. Guinea pig immunizations with the prefusion-stabilized cleavage-intermediate LASV GPC, first as trimer and then as a nanoparticle, induce neutralizing responses, targeting multiple epitopes including that of D5; we identify a neutralizing antibody (GP23) from the immunized guinea pigs. Collectively, our findings define a prefusion-stabilized GPC trimer, reveal an apex-situated site-of-vulnerability, and demonstrate elicitation of LASV-neutralizing responses by a cleavage-intermediate LASV trimer.

HLA-E-restricted SARS-CoV-2-specific T cells from convalescent COVID-19 patients suppress virus replication despite HLA class Ia down-regulation

Pathogen-specific CD8+ T cell responses restricted by the nonpolymorphic nonclassical class Ib molecule human leukocyte antigen E (HLA-E) are rarely reported in viral infections. The natural HLA-E ligand is a signal peptide derived from classical class Ia HLA molecules that interact with the NKG2/CD94 receptors to regulate natural killer cell functions, but pathogen-derived peptides can also be presented by HLA-E. Here, we describe five peptides from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that elicited HLA-E-restricted CD8+ T cell responses in convalescent patients with coronavirus disease 2019. These T cell responses were identified in the blood at frequencies similar to those reported for classical HLA-Ia-restricted anti-SARS-CoV-2 CD8+ T cells. HLA-E peptide-specific CD8+ T cell clones, which expressed diverse T cell receptors, suppressed SARS-CoV-2 replication in Calu-3 human lung epithelial cells. SARS-CoV-2 infection markedly down-regulated classical HLA class I expression in Calu-3 cells and primary reconstituted human airway epithelial cells, whereas HLA-E expression was not affected, enabling T cell recognition. Thus, HLA-E-restricted T cells could contribute to the control of SARS-CoV-2 infection alongside classical T cells.

Tuning specificity and topology of lectins through synthetic biology

Lectins are non-immunoglobulin and non-catalytic glycan binding proteins that are able to decipher the structure and function of complex glycans. They are widely used as biomarkers for following alteration of glycosylation state in many diseases and have application in therapeutics. Controlling and extending lectin specificity and topology is the key for obtaining better tools. Furthermore, lectins and other glycan binding proteins can be combined with additional domains, providing novel functionalities. We provide a view on the current strategy with a focus on synthetic biology approaches yielding to novel specificity, but other novel architectures with novel application in biotechnology or therapy.

A covalent opsonization approach to enhance synthetic immunity against viral escape variants

The sensitivity of therapeutic antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral "escape" mutations has inspired efforts to develop treatment strategies that are still effective in the face of rapidly mutating viral surface proteins. Here, we demonstrate a chemical strategy that enforces viral opsonization by natural serum antibodies. This strategy uses chimeric molecules that we call covalent viral opsonizers, which covalently label viral surface proteins, with synthetic antibody-binding ligands. As a proof of concept, we develop covalent viral opsonizers that covalently label the spike protein on SARS-CoV-2 using a "mutation-proof" small-molecule-binding ligand for anti-dinitrophenyl serum antibodies. In model assays, we observe that covalent viral opsonizers can rapidly and selectively covalently label the receptor-binding domain of both native and mutant spike proteins, leading to antibody opsonization. Opsonization mediated by this strategy is able to efficiently block the key binding domain interactions, in contrast to non-covalent analogs. We also show that covalent viral opsonizers enact targeted anti-viral phagocytotic immune function. This strategy has potential general utility for the rapid deployment of anti-viral synthetic immunotherapeutics at the onset of a new pandemic to reinforce vaccination and antibody engineering efforts.

How cells wrap around virus-like particles using extracellular filamentous protein structures

Nanoparticles, such as viruses, can enter cells via endocytosis. During endocytosis, the cell surface wraps around the nanoparticle to effectively eat it. Prior focus has been on how nanoparticle size and shape impacts endocytosis. However, inspired by the noted presence of extracellular vimentin affecting viral and bacteria uptake, as well as the structure of coronaviruses, we construct a computational model in which both the cell-like construct and the virus-like construct contain filamentous protein structures protruding from their surfaces. We then study the impact of these additional degrees of freedom on viral wrapping. We find that cells with an optimal density of filamentous extracellular components (ECCs) are more likely to be infected as they uptake the virus faster and use relatively less cell surface area per individual virus. At the optimal density, the cell surface folds around the virus, and folds are faster and more efficient at wrapping the virus than crumple-like wrapping. We also find that cell surface bending rigidity helps generate folds, as bending rigidity enhances force transmission across the surface. However, changing other mechanical parameters, such as the stretching stiffness of filamentous ECCs or virus spikes, can drive crumple-like formation of the cell surface. We conclude with the implications of our study on the evolutionary pressures of virus-like particles, with a particular focus on the cellular microenvironment that may include filamentous ECCs.

How cells wrap around virus-like particles using extracellular filamentous protein structures

Nanoparticles, such as viruses, can enter cells via endocytosis. During endocytosis, the cell surface wraps around the nanoparticle to effectively eat it. Prior focus has been on how nanoparticle size and shape impacts endocytosis. However, inspired by the noted presence of extracellular vimentin affecting viral and bacteria uptake, as well as the structure of coronaviruses, we construct a computational model in which both the cell-like construct and the virus-like construct contain filamentous protein structures protruding from their surfaces. We then study the impact of these additional degrees of freedom on viral wrapping. We find that cells with an optimal density of filamentous extracellular components (ECCs) are more likely to be infected as they uptake the virus faster and use relatively less cell surface area per individual virus. At the optimal density, the cell surface folds around the virus, and folds are faster and more efficient at wrapping the virus than crumple-like wrapping. We also find that cell surface bending rigidity helps generate folds, as bending rigidity enhances force transmission across the surface. However, changing other mechanical parameters, such as the stretching stiffness of filamentous ECCs or virus spikes, can drive crumple-like formation of the cell surface. We conclude with the implications of our study on the evolutionary pressures of virus-like particles, with a particular focus on the cellular microenvironment that may include filamentous ECCs.

Recently Designed Multivalent Spike Binders Cannot Bind Multivalently─How Do They Achieve Enhanced Avidity to SARS-CoV-2?

The trimeric spike protein of SARS-CoV-2 has been targeted by antibody mimics that bind near or at the receptor-binding domain to neutralize the virus. Several independent studies have reported enhanced binding avidity for dimers and trimers, where binding domains are connected by short peptides. The enhanced avidity of the multivalent constructs was attributed to their simultaneously binding two or three sites within a single spike trimer. We argue here that the 15-20 amino acid peptide linkers, when considered as worm-like-chains, are too short to span the binding sites within a single spike. The enhanced avidity of the multivalent constructs may be explained by a rebinding mechanism, which does not involve multivalent binding.

Antibody class-switching as a strategy to improve HIV-1 neutralization

Broadly neutralizing antibodies (bNAbs), when administered through passive immunization, are protective against HIV-1 infection. Current HIV-1 vaccine strategies are aimed at guiding the immune system to make bNAbs by mimicking their development during infection. Somatic hypermutation of the variable region is known to be crucial for the development of bNAbs. More recently, however, studies have shown how class-switch recombination (CSR) resulting in the generation of different antibody isotypes may serve as an additional mechanism through which antibodies can gain neutralization breadth and potency. In this review, we discuss the importance of different antibody isotypes for HIV-1 neutralization breadth and potency and how this information can be leveraged to improve passive and active immunization against HIV-1.

Ultrapotent and broad neutralization of SARS-CoV-2 variants by modular, tetravalent, bi-paratopic antibodies

Neutralizing antibodies (nAbs) that target the SARS-CoV-2 spike protein have received emergency use approval for treatment of COVID-19. However, with the emergence of variants of concern, there is a need for new treatment options. We report a format that enables modular assembly of bi-paratopic tetravalent nAbs with antigen-binding sites from two distinct nAbs. The tetravalent nAb purifies in high yield and exhibits biophysical characteristics that are comparable to those of clinically used therapeutic antibodies. The tetravalent nAb binds to the spike protein trimer at least 100-fold more tightly than bivalent IgGs (apparent K_D < 1 pM) and neutralizes a broad array of SARS-CoV-2 pseudoviruses, chimeric viruses, and authentic viral variants with high potency. Together, these results establish the tetravalent diabody-Fc-Fab as a robust, modular platform for rapid production of drug-grade nAbs with potencies and breadth of coverage that greatly exceed those of conventional bivalent IgGs.

Structural and Computational Studies of the SARS-CoV-2 Spike Protein Binding Mechanisms with Nanobodies: From Structure and Dynamics to Avidity-Driven Nanobody Engineering

Nanobodies provide important advantages over traditional antibodies, including their smaller size and robust biochemical properties such as high thermal stability, high solubility, and the ability to be bioengineered into novel multivalent, multi-specific, and high-affinity molecules, making them a class of emerging powerful therapies against SARS-CoV-2. Recent research efforts on the design, protein engineering, and structure-functional characterization of nanobodies and their binding with SARS-CoV-2 S proteins reflected a growing realization that nanobody combinations can exploit distinct binding epitopes and leverage the intrinsic plasticity of the conformational landscape for the SARS-CoV-2 S protein to produce efficient neutralizing and mutation resistant characteristics. Structural and computational studies have also been instrumental in quantifying the structure, dynamics, and energetics of the SARS-CoV-2 spike protein binding with nanobodies. In this review, a comprehensive analysis of the current structural, biophysical, and computational biology investigations of SARS-CoV-2 S proteins and their complexes with distinct classes of nanobodies targeting different binding sites is presented. The analysis of computational studies is supplemented by an in-depth examination of mutational scanning simulations and identification of binding energy hotspots for distinct nanobody classes. The review is focused on the analysis of mechanisms underlying synergistic binding of multivalent nanobodies that can be superior to single nanobodies and conventional nanobody cocktails in combating escape mutations by effectively leveraging binding avidity and allosteric cooperativity. We discuss how structural insights and protein engineering approaches together with computational biology tools can aid in the rational design of synergistic combinations that exhibit superior binding and neutralization characteristics owing to avidity-mediated mechanisms.

Atomistic Simulations and In Silico Mutational Profiling of Protein Stability and Binding in the SARS-CoV-2 Spike Protein Complexes with Nanobodies: Molecular Determinants of Mutational Escape Mechanisms

Structure-functional studies have recently revealed a spectrum of diverse high-affinity nanobodies with efficient neutralizing capacity against SARS-CoV-2 virus and resilience against mutational escape. In this study, we combine atomistic simulations with the ensemble-based mutational profiling of binding for the SARS-CoV-2 S-RBD complexes with a wide range of nanobodies to identify dynamic and binding affinity fingerprints and characterize the energetic determinants of nanobody-escaping mutations. Using an in silico mutational profiling approach for probing the protein stability and binding, we examine dynamics and energetics of the SARS-CoV-2 complexes with single nanobodies Nb6 and Nb20, VHH E, a pair combination VHH E + U, a biparatopic nanobody VHH VE, and a combination of the CC12.3 antibody and VHH V/W nanobodies. This study characterizes the binding energy hotspots in the SARS-CoV-2 protein and complexes with nanobodies providing a quantitative analysis of the effects of circulating variants and escaping mutations on binding that is consistent with a broad range of biochemical experiments. The results suggest that mutational escape may be controlled through structurally adaptable binding hotspots in the receptor-accessible binding epitope that are dynamically coupled to the stability centers in the distant binding epitope targeted by VHH U/V/W nanobodies. This study offers a plausible mechanism in which through cooperative dynamic changes, nanobody combinations and biparatopic nanobodies can elicit the increased binding affinity response and yield resilience to common escape mutants.

Binding Revisited-Avidity in Cellular Function and Signaling

When characterizing biomolecular interactions, avidity, is an umbrella term used to describe the accumulated strength of multiple specific and unspecific interactions between two or more interaction partners. In contrast to the affinity, which is often sufficient to describe monovalent interactions in solution and where the binding strength can be accurately determined by considering only the relationship between the microscopic association and dissociation rates, the avidity is a phenomenological macroscopic parameter linked to several microscopic events. Avidity also covers potential effects of reduced dimensionality and/or hindered diffusion observed at or near surfaces e.g., at the cell membrane. Avidity is often used to describe the discrepancy or the "extra on top" when cellular interactions display binding that are several orders of magnitude stronger than those estimated in vitro. Here we review the principles and theoretical frameworks governing avidity in biological systems and the methods for predicting and simulating avidity. While the avidity and effects thereof are well-understood for extracellular biomolecular interactions, we present here examples of, and discuss how, avidity and the underlying kinetics influences intracellular signaling processes.

Implications of excluded volume for chemical inhibition of protein fibrillation

Two quantitative models for the effect of high total macromolecular volume occupancy ('macromolecular crowding') upon the chemical inhibition of protein fibrillation are presented. The first assumes that fibrillation is reversible, and the second assumes that fibrillation is irreversible. Both models predict that small molecule inhibitors will be less effective in crowded media than in dilute media, whereas macromolecular inhibitors are likely to retain their efficiency in crowded media. It is suggested that the coupling of one or more small-molecule inhibitors to an "inert" macromolecular support will increase inhibition efficiency in crowded media.

When two are better than one: Modeling the mechanisms of antibody mixtures

It is difficult to predict how antibodies will behave when mixed together, even after each has been independently characterized. Here, we present a statistical mechanical model for the activity of antibody mixtures that accounts for whether pairs of antibodies bind to distinct or overlapping epitopes. This model requires measuring n individual antibodies and their n(n-1)2 pairwise interactions to predict the 2ⁿ potential combinations. We apply this model to epidermal growth factor receptor (EGFR) antibodies and find that the activity of antibody mixtures can be predicted without positing synergy at the molecular level. In addition, we demonstrate how the model can be used in reverse, where straightforward experiments measuring the activity of antibody mixtures can be used to infer the molecular interactions between antibodies. Lastly, we generalize this model to analyze engineered multidomain antibodies, where components of different antibodies are tethered together to form novel amalgams, and characterize how well it predicts recently designed influenza antibodies.

With the rise of new antibody combinations in therapeutic regimens, it is important to understand how antibodies work together as well as individually. Here, we investigate the specific case of monoclonal antibodies targeting a cancer-causing receptor or the influenza virus and develop a statistical mechanical framework that predicts the effectiveness of a mixture of antibodies. The power of this model lies in its ability to make a large number of predictions based on a limited amount of data. For example, once 10 antibodies have been individually characterized and their epitopes have been mapped, our model can predict how any of the 2¹⁰ = 1024 combinations will behave. This predictive power can aid therapeutic efforts by assessing which combinations of antibodies will elicit the most effective response.