Native ion mobility-mass spectrometry reveals the binding mechanisms of anti-amyloid therapeutic antibodies.

One of the most important attributes of anti-amyloid antibodies is their selective binding to oligomeric and amyloid aggregates. However, current methods of examining the binding specificities of anti-amyloid ß (Aß) antibodies have limited ability to differentiate between complexes that form between antibodies and monomeric or oligomeric Aß species during the dynamic Aß aggregation process. Here, we present a high-resolution native ion-mobility mass spectrometry (nIM-MS) method to investigate complexes formed between a variety of Aß oligomers and three Aß-specific IgGs, namely two antibodies with relatively high conformational specificity (aducanumab and A34) and one antibody with low conformational specificity (crenezumab). We found that crenezumab primarily binds Aß monomers, while aducanumab preferentially binds Aß monomers and dimers and A34 preferentially binds Aß dimers, trimers, and tetrameters. Through collision induced unfolding (CIU) analysis, our data indicate that antibody stability is increased upon Aß binding and, surprisingly, this stabilization involves the Fc region. Together, we conclude that nIM-MS and CIU enable the identification of Aß antibody binding stoichiometries and provide important details regarding antibody binding mechanisms.

Development of a pan-tau multivalent nanobody that binds tau aggregation motifs and recognizes pathological tau aggregates.

Alzheimer's disease and other tauopathies are characterized by the misfolding and aggregation of the tau protein into oligomeric and fibrillar structures. Antibodies against tau play an increasingly important role in studying these neurodegenerative diseases and the generation of tools to diagnose and treat them. The development of antibodies that recognize tau protein aggregates, however, is hindered by complex immunization and antibody selection strategies and limitations to antigen presentation. Here, we have taken a facile approach to identify single-domain antibodies, or nanobodies, that bind to many forms of tau by screening a synthetic yeast surface display nanobody library against monomeric tau and creating multivalent versions of our lead nanobody, MT3.1, to increase its avidity for tau aggregates. We demonstrate that MT3.1 binds to tau monomer, oligomers, and fibrils, as well as pathogenic tau from a tauopathy mouse model, despite being identified through screens against monomeric tau. Through epitope mapping, we discovered binding epitopes of MT3.1 contain the key motif VQIXXK which drives tau aggregation. We show that our bivalent and tetravalent versions of MT3.1 have greatly improved binding ability to tau oligomers and fibrils compared to monovalent MT3.1. Our results demonstrate the utility of our nanobody screening and multivalent design approach in developing nanobodies that bind amyloidogenic protein aggregates. This approach can be extended to the generation of multivalent nanobodies that target other amyloid proteins and has the potential to advance the research and treatment of neurodegenerative diseases.

Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning.

Antibody development, delivery, and efficacy are influenced by antibody-antigen affinity interactions, off-target interactions that reduce antibody bioavailability and pharmacokinetics, and repulsive self-interactions that increase the stability of concentrated antibody formulations and reduce their corresponding viscosity. Yet identifying antibody variants with optimal combinations of these three types of interactions is challenging. Here we show that interpretable machine-learning classifiers, leveraging antibody structural features descriptive of their variable regions and trained on experimental data for a panel of 80 clinical-stage monoclonal antibodies, can identify antibodies with optimal combinations of low off-target binding in a common physiological-solution condition and low self-association in a common antibody-formulation condition. For three clinical-stage antibodies with suboptimal combinations of off-target binding and self-association, the classifiers predicted variable-region mutations that optimized non-affinity interactions while maintaining high-affinity antibody-antigen interactions. Interpretable machine-learning models may facilitate the optimization of antibody candidates for therapeutic applications.

Bispecific antibody shuttles targeting CD98hc mediate efficient and long-lived brain delivery of IgGs.

The inability of antibodies to penetrate the blood-brain barrier (BBB) is a key limitation to their use in diverse applications. One promising strategy is to deliver IgGs using a bispecific BBB shuttle, which involves fusing an IgG to a second affinity ligand that engages a cerebrovascular endothelial target and facilitates transport across the BBB. Nearly all prior efforts have focused on shuttles that target transferrin receptor (TfR-1) despite inherent delivery and safety challenges. Here, we report bispecific antibody shuttles that engage CD98hc, the heavy chain of the large neutral amino acid transporter (LAT1), and efficiently transport IgGs into the brain. Notably, CD98hc shuttles lead to much longer-lived brain retention of IgGs than TfR-1 shuttles while enabling more specific targeting due to limited CD98hc engagement in the brain parenchyma, which we demonstrate for IgGs that either agonize a neuronal receptor (TrkB) or target other endogenous cell-surface proteins on neurons and astrocytes.

Quantitative flow cytometric selection of tau conformational nanobodies specific for pathological aggregates.

Single-domain antibodies, also known as nanobodies, are broadly important for studying the structure and conformational states of several classes of proteins, including membrane proteins, enzymes, and amyloidogenic proteins. Conformational nanobodies specific for aggregated conformations of amyloidogenic proteins are particularly needed to better target and study aggregates associated with a growing class of associated diseases, especially neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. However, there are few reported nanobodies with both conformational and sequence specificity for amyloid aggregates, especially for large and complex proteins such as the tau protein associated with Alzheimer's disease, due to difficulties in selecting nanobodies that bind to complex aggregated proteins. Here, we report the selection of conformational nanobodies that selectively recognize aggregated (fibrillar) tau relative to soluble (monomeric) tau. Notably, we demonstrate that these nanobodies can be directly isolated from immune libraries using quantitative flow cytometric sorting of yeast-displayed libraries against tau aggregates conjugated to quantum dots, and this process eliminates the need for secondary nanobody screening. The isolated nanobodies demonstrate conformational specificity for tau aggregates in brain samples from both a transgenic mouse model and human tauopathies. We expect that our facile approach will be broadly useful for isolating conformational nanobodies against diverse amyloid aggregates and other complex antigens.

Flow cytometric isolation of drug-like conformational antibodies specific for amyloid fibrils.

Antibodies that recognize specific protein conformational states are broadly important for research, diagnostic and therapeutic applications, yet they are difficult to generate in a predictable and systematic manner using either immunization or in vitro antibody display methods. This problem is particularly severe for conformational antibodies that recognize insoluble antigens such as amyloid fibrils associated with many neurodegenerative disorders. Here we report a quantitative fluorescence-activated cell sorting (FACS) method for directly selecting high-quality conformational antibodies against different types of insoluble (amyloid fibril) antigens using a single, off-the-shelf human library. Our approach uses quantum dots functionalized with antibodies to capture insoluble antigens, and the resulting quantum dot conjugates are used in a similar manner as conventional soluble antigens for multi-parameter FACS selections. Notably, we find that this approach is robust for isolating high-quality conformational antibodies against tau and α-synuclein fibrils from the same human library with combinations of high affinity, high conformational specificity and, in some cases, low off-target binding that rival or exceed those of clinical-stage antibodies specific for tau (zagotenemab) and α-synuclein (cinpanemab). This approach is expected to enable conformational antibody selection and engineering against diverse types of protein aggregates and other insoluble antigens (e.g., membrane proteins) that are compatible with presentation on the surface of antibody-functionalized quantum dots.

Bispecific antibody shuttles targeting CD98hc mediate efficient and long-lived brain delivery of IgGs.

The inability of antibodies and other biologics to penetrate the blood-brain barrier (BBB) is a key limitation to their use in diagnostic, imaging, and therapeutic applications. One promising strategy is to deliver IgGs using a bispecific BBB shuttle, which involves fusing an IgG with a second affinity ligand that engages a cerebrovascular endothelial target and facilitates transport across the BBB. Nearly all prior efforts have focused on the transferrin receptor (TfR-1) as the prototypical endothelial target despite inherent delivery and safety challenges. Here we report bispecific antibody shuttles that engage CD98hc (also known as 4F2 and SLC3A2), the heavy chain of the large neutral amino acid transporter (LAT1), and efficiently transport IgGs into the brain parenchyma. Notably, CD98hc shuttles lead to much longer-lived brain retention of IgGs than TfR-1 shuttles while enabling more specific brain targeting due to limited CD98hc engagement in the brain parenchyma. We demonstrate the broad utility of the CD98hc shuttles by reformatting three existing IgGs as CD98hc bispecific shuttles and delivering them to the mouse brain parenchyma that either agonize a neuronal receptor (TrkB) or target other endogenous antigens on specific types of brain cells (neurons and astrocytes).

Reduction of therapeutic antibody self-association using yeast-display selections and machine learning.

Self-association governs the viscosity and solubility of therapeutic antibodies in high-concentration formulations used for subcutaneous delivery, yet it is difficult to reliably identify candidates with low self-association during antibody discovery and early-stage optimization. Here, we report a high-throughput protein engineering method for rapidly identifying antibody candidates with both low self-association and high affinity. We find that conjugating quantum dots to IgGs that strongly self-associate (pH 7.4, PBS), such as lenzilumab and bococizumab, results in immunoconjugates that are highly sensitive for detecting other high self-association antibodies. Moreover, these conjugates can be used to rapidly enrich yeast-displayed bococizumab sub-libraries for variants with low levels of immunoconjugate binding. Deep sequencing and machine learning analysis of the enriched bococizumab libraries, along with similar library analysis for antibody affinity, enabled identification of extremely rare variants with co-optimized levels of low self-association and high affinity. This analysis revealed that co-optimizing bococizumab is difficult because most high-affinity variants possess positively charged variable domains and most low self-association variants possess negatively charged variable domains. Moreover, negatively charged mutations in the heavy chain CDR2 of bococizumab, adjacent to its paratope, were effective at reducing self-association without reducing affinity. Interestingly, most of the bococizumab variants with reduced self-association also displayed improved folding stability and reduced nonspecific binding, revealing that this approach may be particularly useful for identifying antibody candidates with attractive combinations of drug-like properties.Abbreviations: AC-SINS: affinity-capture self-interaction nanoparticle spectroscopy; CDR: complementarity-determining region; CS-SINS: charge-stabilized self-interaction nanoparticle spectroscopy; FACS: fluorescence-activated cell sorting; Fab: fragment antigen binding; Fv: fragment variable; IgG: immunoglobulin; QD: quantum dot; PBS: phosphate-buffered saline; VH: variable heavy; VL: variable light.

Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space.

Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies.

Isolating Anti-amyloid Antibodies from Yeast-Displayed Libraries.

Conformational antibodies specific for amyloid-forming peptides and proteins are important for a range of biomedical applications, including detecting, inhibiting, and potentially treating protein aggregation disorders ranging from Alzheimer's to Parkinson's diseases. Generation of anti-amyloid antibodies is greatly complicated by the complex, heterogeneous and insoluble nature of amyloid antigens. Here we describe systematic methods for isolating and affinity maturing anti-amyloid antibodies using yeast surface display. Magnetic-activated cell sorting is used to sort single-chain antibody libraries positively for binding to amyloid antigens and negatively against the corresponding disaggregated antigens to remove antibodies that bind in a conformation-independent manner. Isolated lead antibody clones with conformational specificity are affinity matured via targeted CDR mutagenesis and magnetic-activated cell sorting.

Facile isolation of high-affinity nanobodies from synthetic libraries using CDR-swapping mutagenesis.

The generation of high-affinity nanobodies for diverse biomedical applications typically requires immunization or affinity maturation. Here, we report a simple protocol using complementarity-determining region (CDR)-swapping mutagenesis to isolate high-affinity nanobodies from common framework libraries. This approach involves shuffling the CDRs of low-affinity variants during the sorting of yeast-displayed libraries to directly isolate high-affinity nanobodies without the need for lead isolation and optimization. We expect this approach, which we demonstrate for SARS-CoV-2 neutralizing nanobodies, will simplify the generation of high-affinity nanobodies. For complete details on the use and execution of this profile, please refer to Zupancic et al. (2021).

Discovery and characterization of high-affinity, potent SARS-CoV-2 neutralizing antibodies via single B cell screening.

Monoclonal antibodies that target SARS-CoV-2 with high affinity are valuable for a wide range of biomedical applications involving novel coronavirus disease (COVID-19) diagnosis, treatment, and prophylactic intervention. Strategies for the rapid and reliable isolation of these antibodies, especially potent neutralizing antibodies, are critical toward improved COVID-19 response and informed future response to emergent infectious diseases. In this study, single B cell screening was used to interrogate antibody repertoires of immunized mice and isolate antigen-specific IgG1+ memory B cells. Using these methods, high-affinity, potent neutralizing antibodies were identified that target the receptor-binding domain of SARS-CoV-2. Further engineering of the identified molecules to increase valency resulted in enhanced neutralizing activity. Mechanistic investigation revealed that these antibodies compete with ACE2 for binding to the receptor-binding domain of SARS-CoV-2. These antibodies may warrant further development for urgent COVID-19 applications. Overall, these results highlight the potential of single B cell screening for the rapid and reliable identification of high-affinity, potent neutralizing antibodies for infectious disease applications.

Engineered Multivalent Nanobodies Potently and Broadly Neutralize SARS-CoV-2 Variants.

The COVID-19 pandemic continues to be a severe threat to human health, especially due to current and emerging SARS-CoV-2 variants with potential to escape humoral immunity developed after vaccination or infection. The development of broadly neutralizing antibodies that engage evolutionarily conserved epitopes on coronavirus spike proteins represents a promising strategy to improve therapy and prophylaxis against SARS-CoV-2 and variants thereof. Herein, a facile multivalent engineering approach is employed to achieve large synergistic improvements in the neutralizing activity of a SARS-CoV-2 cross-reactive nanobody (VHH-72) initially generated against SARS-CoV. This synergy is epitope specific and is not observed for a second high-affinity nanobody against a non-conserved epitope in the receptor-binding domain. Importantly, a hexavalent VHH-72 nanobody retains binding to spike proteins from multiple highly transmissible SARS-CoV-2 variants (B.1.1.7 and B.1.351) and potently neutralizes them. Multivalent VHH-72 nanobodies also display drug-like biophysical properties, including high stability, high solubility, and low levels of non-specific binding. The unique neutralizing and biophysical properties of VHH-72 multivalent nanobodies make them attractive as therapeutics against SARS-CoV-2 variants.

Directed evolution of potent neutralizing nanobodies against SARS-CoV-2 using CDR-swapping mutagenesis.

There is widespread interest in facile methods for generating potent neutralizing antibodies, nanobodies, and other affinity proteins against SARS-CoV-2 and related viruses to address current and future pandemics. While isolating antibodies from animals and humans are proven approaches, these methods are limited to the affinities, specificities, and functional activities of antibodies generated by the immune system. Here we report a surprisingly simple directed evolution method for generating nanobodies with high affinities and neutralization activities against SARS-CoV-2. We demonstrate that complementarity-determining region swapping between low-affinity lead nanobodies, which we discovered unintentionally but find is simple to implement systematically, results in matured nanobodies with unusually large increases in affinity. Importantly, the matured nanobodies potently neutralize both SARS-CoV-2 pseudovirus and live virus, and possess drug-like biophysical properties. We expect that our methods will improve in vitro nanobody discovery and accelerate the generation of potent neutralizing nanobodies against diverse coronaviruses.

Rational affinity maturation of anti-amyloid antibodies with high conformational and sequence specificity.

The aggregation of amyloidogenic polypeptides is strongly linked to several neurodegenerative disorders, including Alzheimer's and Parkinson's diseases. Conformational antibodies that selectively recognize protein aggregates are leading therapeutic agents for selectively neutralizing toxic aggregates, diagnostic and imaging agents for detecting disease, and biomedical reagents for elucidating disease mechanisms. Despite their importance, it is challenging to generate high-quality conformational antibodies in a systematic and site-specific manner due to the properties of protein aggregates (hydrophobic, multivalent, and heterogeneous) and limitations of immunization (uncontrolled antigen presentation and immunodominant epitopes). Toward addressing these challenges, we have developed a systematic directed evolution procedure for affinity maturing antibodies against Alzheimer's Aß fibrils and selecting variants with strict conformational and sequence specificity. We first designed a library based on a lead conformational antibody by sampling combinations of amino acids in the antigen-binding site predicted to mediate high antibody specificity. Next, we displayed this library on the surface of yeast, sorted it against Aß42 aggregates, and identified promising clones using deep sequencing. The resulting antibodies displayed similar or higher affinities than clinical-stage Aß antibodies (aducanumab and crenezumab). Moreover, the affinity-matured antibodies retained high conformational specificity for Aß aggregates, as observed for aducanumab and unlike crenezumab. Notably, the affinity-maturated antibodies displayed extremely low levels of nonspecific interactions, as observed for crenezumab and unlike aducanumab. We expect that our systematic methods for generating antibodies with unique combinations of desirable properties will improve the generation of high-quality conformational antibodies specific for diverse types of aggregated conformers.

Protein Adsorption to Charged Gold Nanospheres as a Function of Protein Deformability.

The corona that forms as protein adsorbs to gold nanospheres (AuNSs) is directly influenced by the surface chemistry of the AuNS. Tools to predict adsorption outcomes are needed for intelligent design of nanomaterials for biological applications. We hypothesized that the denaturation behavior of a protein might be a useful predictor of adsorption behavior to AuNSs, and used this idea to study protein adsorption to anionic citrate-capped AuNSs and to cationic poly(allylamine hydrochloride) (PAH) wrapped AuNSs. Three proteins (α-amylase (A-Amy), ß-lactoglobulin (BLG), and bovine serum albumin (BSA)), representing three different classes of acid denaturation behavior, were selected with BLG being the least deformable and BSA being the most deformable. Protein adsorption to AuNSs was monitored via UV-vis spectrophotometry and dynamic light scattering. Changes to the protein structure upon AuNS interaction were monitored via circular dichroism spectroscopy. Binding constants were determined using the Langmuir adsorption isotherm, resulting in BSA > BLG ≫ A-Amy affinities for citrate-capped gold nanospheres. PAH-coated AuNSs displayed little affinity for these proteins at similar concentrations as citrate-coated AuNSs and became agglomerated at high protein concentrations. The enzymatic activity of A-Amy/citrate AuNS conjugates was measured via colorimetric assay, and found to be 11% of free A-Amy, suggesting that binding restricts access to the active site. Across both citrate AuNSs and PAH AuNSs, the changes in secondary structure were greatest for BSA > A-Amy > BLG, which does follow the trends predicted by acid denaturation characteristics.