Development of a new affinity maturation protocol for the construction of an internalizing anti-nucleolin antibody library.

Over the last decades, monoclonal antibodies have substantially improved the treatment of several conditions. The continuous search for novel therapeutic targets and improvements in antibody's structure, demands for a constant optimization of their development. In this regard, modulation of an antibody's affinity to its target has been largely explored and culminated in the discovery and optimization of a variety of molecules. It involves the creation of antibody libraries and selection against the target of interest. In this work, we aimed at developing a novel protocol to be used for the affinity maturation of an antibody previously developed by our group. An antibody library was constructed using an in vivo random mutagenesis approach that, to our knowledge, has not been used before for antibody development. Then, a cell-based phage display selection protocol was designed to allow the fast and simple screening of antibody clones capable of being internalized by target cells. Next generation sequencing coupled with computer analysis provided an extensive characterization of the created library and post-selection pool, that can be used as a guide for future antibody development. With a single selection step, an enrichment in the mutated antibody library, given by a decrease in almost 50% in sequence diversity, was achieved, and structural information useful in the study of the antibody-target interaction in the future was obtained.

Development, High-Throughput Profiling, and Biopanning of a Large Phage Display Single-Domain Antibody Library.

Immunoglobulin G-based monoclonal antibodies (mAbs) have been effective in treating various diseases, but their large molecular size can limit their penetration of tissue and efficacy in multifactorial diseases, necessitating the exploration of alternative forms. In this study, we constructed a phage display library comprising single-domain antibodies (sdAbs; or "VHHs"), known for their small size and remarkable stability, using a total of 1.6 × 109 lymphocytes collected from 20 different alpacas, resulting in approximately 7.16 × 1010 colonies. To assess the quality of the constructed library, next-generation sequencing-based high-throughput profiling was performed, analyzing approximately 5.65 × 106 full-length VHH sequences, revealing 92% uniqueness and confirming the library's diverse composition. Systematic characterization of the library revealed multiple sdAbs with high affinity for three therapeutically relevant antigens. In conclusion, our alpaca sdAb phage display library provides a versatile resource for diagnostics and therapeutics. Furthermore, the library's vast natural VHH antibody repertoire offers insights for generating humanized synthetic sdAb libraries, further advancing sdAb-based therapeutics.

Hybrid response to SARS-CoV-2 and Neisseria meningitidis C after an OMV-adjuvanted immunization in mice and their offspring.

COVID-19, caused by SARS-CoV-2, and meningococcal disease, caused by Neisseria meningitidis, are relevant infectious diseases, preventable through vaccination. Outer membrane vesicles (OMVs), released from Gram-negative bacteria, such as N. meningitidis, present adjuvant characteristics and may confer protection against meningococcal disease. Here, we evaluated in mice the humoral and cellular immune response to different doses of receptor binding domain (RBD) of SARS-CoV-2 adjuvanted by N. meningitidis C:2a:P1.5 OMVs and aluminum hydroxide, as a combined preparation for these pathogens. The immunization induced IgG antibodies of high avidity for RBD and OMVs, besides IgG that recognized the Omicron BA.2 variant of SARS-CoV-2 with intermediary avidity. Cellular immunity showed IFN-γ and IL-4 secretion in response to RBD and OMV stimuli, demonstrating immunologic memory and a mixed Th1/Th2 response. Offspring presented transferred IgG of similar levels and avidity as their mothers. Humoral immunity did not point to the superiority of any RBD dose, but the group immunized with a lower antigenic dose (0.5 µg) had the better cellular response. Overall, OMVs enhanced RBD immunogenicity and conferred an immune response directed to N. meningitidis too.

Measles IgG Avidity Assay.

Measles IgG avidity assays determine the overall strength of molecular binding between measles-specific IgG antibodies and measles virus antigens. Avidity results can distinguish recent from distant measles virus infections. Individuals who are immunologically naïve to measles virus develop low-avidity antibodies upon measles virus infection or first-time vaccination. Within 4-6 months, antibodies mature to high avidity. Measles avidity assays are most useful in the context of measles elimination. In such settings, avidity and epidemiological and clinical information are used to classify measles breakthrough infections for control and surveillance purposes and to assist in case confirmation when other laboratory results are inconclusive or nonexistent. We present a highly accurate end-titer measles avidity assay that delivers results based on IgG quality (avidity) that are independent of IgG concentration.

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries.

Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of nine different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

Microfluidic Diffusional Sizing (MDS) Measurements of Secretory Neutralizing Antibody Affinity Against SARS-CoV-2.

SARS-CoV-2 has rampantly spread around the globe and continues to cause unprecedented loss through ongoing waves of (re)infection. Increasing our understanding of the protection against infection with SARS-CoV-2 is critical to ending the pandemic. Serological assays have been widely used to assess immune responses, but secretory antibodies, the essential first line of defense, have been studied to only a limited extent. Of particular interest and importance are neutralizing antibodies, which block the binding of the spike protein of SARS-CoV-2 to the human receptor angiotensin-converting enzyme-2 (ACE2) and thus are essential for immune defense. Here, we employed Microfluidic Diffusional Sizing (MDS), an immobilization-free technology, to characterize neutralizing antibody affinity to SARS-CoV-2 spike receptor-binding domain (RBD) and spike trimer in saliva. Affinity measurement was obtained through a contrived sample and buffer using recombinant SARS-CoV-2 RBD and monoclonal antibody. Limited saliva samples demonstrated that MDS applies to saliva neutralizing antibody measurement. The ability to disrupt a complex of ACE2-Fc and spike trimer is shown. Using a quantitative assay on the patient sample, we determined the affinity and binding site concentration of the neutralizing antibodies.

A dual computational and experimental strategy to enhance TSLP antibody affinity for improved asthma treatment.

Thymic stromal lymphopoietin is a key cytokine involved in the pathogenesis of asthma and other allergic diseases. Targeting TSLP and its signaling pathways is increasingly recognized as an effective strategy for asthma treatment. This study focused on enhancing the affinity of the T6 antibody, which specifically targets TSLP, by integrating computational and experimental methods. The initial affinity of the T6 antibody for TSLP was lower than the benchmark antibody AMG157. To improve this, we utilized alanine scanning, molecular docking, and computational tools including mCSM-PPI2 and GEO-PPI to identify critical amino acid residues for site-directed mutagenesis. Subsequent mutations and experimental validations resulted in an antibody with significantly enhanced blocking capacity against TSLP. Our findings demonstrate the potential of computer-assisted techniques in expediting antibody affinity maturation, thereby reducing both the time and cost of experiments. The integration of computational methods with experimental approaches holds great promise for the development of targeted therapeutic antibodies for TSLP-related diseases.

In vivo affinity maturation of mouse B cells reprogrammed to express human antibodies.

Mice adoptively transferred with mouse B cells edited via CRISPR to express human antibody variable chains could help evaluate candidate vaccines and develop better antibody therapies. However, current editing strategies disrupt the heavy-chain locus, resulting in inefficient somatic hypermutation without functional affinity maturation. Here we show that these key B-cell functions can be preserved by directly and simultaneously replacing recombined mouse heavy and kappa chains with those of human antibodies, using a single Cas12a-mediated cut at each locus and 5' homology arms complementary to distal V segments. Cells edited in this way to express the human immunodeficiency virus type 1 (HIV-1) broadly neutralizing antibody 10-1074 or VRC26.25-y robustly hypermutated and generated potent neutralizing plasma in vaccinated mice. The 10-1074 variants isolated from the mice neutralized a global panel of HIV-1 isolates more efficiently than wild-type 10-1074 while maintaining its low polyreactivity and long half-life. We also used the approach to improve the potency of anti-SARS-CoV-2 antibodies against recent Omicron strains. In vivo affinity maturation of B cells edited at their native loci may facilitate the development of broad, potent and bioavailable antibodies.

Matrixed CDR grafting: A neoclassical framework for antibody humanization and developability.

Discovery and optimization of a biotherapeutic monoclonal antibody requires a careful balance of target engagement and physicochemical developability properties. To take full advantage of the sequence diversity provided by different antibody discovery platforms, a rapid and reliable process for humanization of antibodies from nonhuman sources is required. Canonically, maximizing homology of the human variable region (V-region) to the original germline was believed to result in preservation of binding, often without much consideration for inherent molecular properties. We expand on this approach by grafting the complementary determining regions (CDRs) of a mouse anti-LAG3 antibody into an extensive matrix of human variable heavy chain (VH) and variable light chain (VL) framework regions with substantially broader sequence homology to assess the impact on complementary determining region-framework compatibility through progressive evaluation of expression, affinity, biophysical developability, and function. Specific VH and VL framework sequences were associated with major expression and purification phenotypes. Greater VL sequence conservation was correlated with retained or improved affinity. Analysis of grafts that bound the target demonstrated that initial developability criteria were significantly impacted by VH, but not VL. In contrast, cell binding and functional characteristics were significantly impacted by VL, but not VH. Principal component analysis of all factors identified multiple grafts that exhibited more favorable antibody properties, notably with nonoptimal sequence conservation. Overall, this study demonstrates that modern throughput systems enable a more thorough, customizable, and systematic analysis of graft-framework combinations, resulting in humanized antibodies with improved global properties that may progress through development more quickly and with a greater probability of success.

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.

A Diagnostic Challenge: Toxoplasmosis Masquerading as Tonsillar Neoplasm.

This case report elucidates an uncommon manifestation of toxoplasmosis characterized by an ulcerative oropharyngeal lesion and cervical lymphadenopathy, which intriguingly simulated a tonsillar neoplasm. The patient, a 28-year-old immunocompetent woman, reported symptoms such as a persistent sore throat, unilateral neck pain, and otalgia. Despite the initial clinical impressions, a diagnostic left subtotal tonsillectomy revealed no malignancy but marked acute and chronic inflammation. A comprehensive investigation subsequently indicated a recent primary infection with Toxoplasma gondii, as evidenced by the presence of high IgM antibodies and low IgG avidity. This unique case underlines the significance of incorporating toxoplasmosis into the differential diagnosis of oropharyngeal lesions, thereby necessitating a meticulous approach to laboratory testing. Laryngoscope, 134:1741-1743, 2024.

Toxoplasma gondii IgG avidity for the diagnosis of primary infection in pregnant women: Comparison between chemiluminescent microparticle immunoassay and enzyme-linked immunosorbent assay.

INTRODUCTION: This study evaluated whether IgG avidity measured by chemiluminescent microparticle immunoassay (CMIA) compared with enzyme-linked immunosorbent assay (ELISA) was useful to detect primary T. gondii infection during pregnancy and to estimate the risk for congenital T. gondii infection. METHODS: One hundred six women with positive tests for T. gondii IgG and T. gondii IgM, comprising 21 women (19.8%) with low (<30%), 6 (5.7%) with borderline (30%-35%), and 79 (74.5%) with high (>35%) IgG avidity measured by ELISA were selected. Their stored sera were used for T. gondii IgG avidity measurements by CMIA. RESULTS: In CMIA, 72 (67.9%) women had low (<50%), 12 (11.3%) had borderline (50%-59.9%), and 22 (20.8%) had high (≥60%) IgG avidity. The ratio of low T. gondii IgG avidity index in CMIA was more than three-fold than that in ELISA. Eighteen (85.7%) of 21 women with ELISA low avidity also had CMIA low avidity, and 26 (96.3%) of 27 women with ELISA low or borderline avidity corresponded to CMIA low or borderline avidity, whereas 21 (26.6%) of 79 women with ELISA high avidity were diagnosed with CMIA low avidity. All three cases with congenital T. gondii infection showed coincidentally low IgG avidity in both methods. A positive correlation in IgG avidity indices was found between of ELISA and CMIA. CONCLUSIONS: CMIA for T. gondii avidity measurements compared with ELISA was clinically useful to detect pregnant women at a high risk of developing congenital T. gondii infection.

A Generic Antibody-Blocking Protein That Enables pH-Switchable Activation of Antibody Activity.

Molecular strategies that allow for reversible control of antibody activity have drawn considerable interest for both therapeutic and diagnostic applications. Protein M is a generic antibody-binding protein that binds to the Fv domain of IgGs and, in doing so, blocks antigen binding. However, the dissociation of protein M is essentially irreversible, which has precluded its use as an antibody affinity reagent and molecular mask to control antibody activity. Here, we show that introduction of 8 histidine residues on the Fv binding interface of protein M results in a variant that shows pH-switchable IgG binding. This protein M-8his variant provides an attractive and universal affinity resin for the purification of IgGs, antibody fragments (Fab and single-chain variable fragments (scFv)), and antibody conjugates. Moreover, protein M-8his enables the pH-dependent blocking of therapeutic antibodies, allowing the selective targeting of cells at pH 6.0.

Determining the affinities of high-affinity antibodies using KinExA and surface plasmon resonance.

Accurate and efficient affinity measurement techniques are essential for the biophysical characterization of therapeutic monoclonal antibodies, one of the fastest growing drug classes. Surface plasmon resonance (SPR) is widely used for determining antibody affinity, but does not perform well with extremely high affinity (low picomolar to femtomolar range) molecules. In this study, we compare the SPR-based Carterra LSA and the kinetic exclusion assay (KinExA) for measuring the affinities of 48 antibodies generated against the SARS-CoV-2 receptor-binding domain. These data reveal that high-affinity antibodies can be generated straight from selections using high-quality in vitro library platforms with 54% correspondence between affinities measured using LSA and KinExA. Generally, where there was a 2-fold or greater difference between LSA and KinExA, KinExA reported that affinities were tighter. We highlight the differences between LSA and KinExA, identifying the benefits and pitfalls of each in terms of dynamic range and throughput. Furthermore, we demonstrate for the first time that single-point screening with KinExA can significantly improve throughput while maintaining a strong correlation with full binding curve equilibrium measurements, enabling the accurate rank-ordering of clones with exceptionally tight binding properties.

Framework-Directed Amino-Acid Insertions Generated over 55-Fold Affinity-Matured Antibody Fragments That Enabled Sensitive Luminescent Immunoassays of Cortisol.

We generated three single-chain Fv fragments (scFvs) specific to cortisol according to our original affinity-maturation strategy and verified their utility in developing immunoassays. These scFv mutants (m-scFvs) had insertion of one, four, or six amino acid(s) in the framework region 1 of the VH-domain and showed >55-fold higher affinity (Ka, 2.0 - 2.2 × 1010 M-1) than the unmodified scFv (wt-scFv). Each m-scFv was fused with NanoLuc luciferase (NLuc) for the use in enzyme-linked immunosorbent assays (ELISAs). In these ELISA, the m-scFv-NLuc fusions were competitively reacted with immobilized cortisol residues and cortisol standards, and then the bound NLuc activity was monitored luminometrically. The luminescent ELISAs generated dose-response curves with extremely low midpoints (approx. 3 pg/assay) and were >150-fold more sensitive than the colorimetric ELISAs using wt-scFv and >8000-fold more sensitive than the ELISA using the parental native antibody. The luminescent ELISAs showed acceptable cross-reactivity patterns with related steroids, and the determination of control sera afforded cortisol levels in the reference range with satisfactory parallelism.

DG-Affinity: predicting antigen-antibody affinity with language models from sequences.

BACKGROUND: Antibody-mediated immune responses play a crucial role in the immune defense of human body. The evolution of bioengineering has led the progress of antibody-derived drugs, showing promising efficacy in cancer and autoimmune disease therapy. A critical step of this development process is obtaining the affinity between antibodies and their binding antigens. RESULTS: In this study, we introduce a novel sequence-based antigen-antibody affinity prediction method, named DG-Affinity. DG-Affinity uses deep neural networks to efficiently and accurately predict the affinity between antibodies and antigens from sequences, without the need for structural information. The sequences of both the antigen and the antibody are first transformed into embedding vectors by two pre-trained language models, then these embeddings are concatenated into an ConvNeXt framework with a regression task. The results demonstrate the superiority of DG-Affinity over the existing structure-based prediction methods and the sequence-based tools, achieving a Pearson's correlation of over 0.65 on an independent test dataset. CONCLUSIONS: Compared to the baseline methods, DG-Affinity achieves the best performance and can advance the development of antibody design. It is freely available as an easy-to-use web server at https://www.digitalgeneai.tech/solution/affinity .

Germinal centers output clonally diverse plasma cell populations expressing high- and low-affinity antibodies.

Germinal centers (GCs) form in lymph nodes after immunization or infection to facilitate antibody affinity maturation and memory and plasma cell (PC) development. PC differentiation is thought to involve stringent selection for GC B cells expressing the highest-affinity antigen receptors, but how this plays out during complex polyclonal responses is unclear. We combine temporal lineage tracing with antibody characterization to gain a snapshot of PCs developing during influenza infection. GCs co-mature B cell clones with antibody affinities spanning multiple orders of magnitude; however, each generates PCs with similar efficiencies, including weak binders. Within lineages, PC selection is not restricted to variants with the highest-affinity antibodies. Differentiation is commonly associated with proliferative expansion to produce "nodes" of identical PCs. Immunization-induced GCs generate fewer PCs but still of low- and high-antibody affinities. We propose that generating low-affinity antibody PCs reflects an evolutionary compromise to facilitate diverse serum antibody responses.

Evaluating the chaos game representation of proteins for applications in machine learning models: prediction of antibody affinity and specificity as a case study.

CONTEXT: Machine learning techniques are becoming increasingly important in the selection and optimization of therapeutic molecules, as well as for the selection of formulation components and the prediction of long-term stability. Compared to first-principle models, machine learning techniques are easier to implement, and can identify correlations that would be hard to describe at a mechanistic level, but strongly rely on high-quality input training data. Here, we evaluate the potential of the "chaos game" representation to provide input data for machine learning models. The chaos game is an algorithm originally developed for the production of fractal structures, and later on applied also to the representation of biological sequences, such as genes and proteins. Our results show that the combination of the chaos game representation with convolutional neural networks results in comparable accuracy to other machine learning approaches, thus indicating that chaos game representations could be a valid alternative to existing featurization strategies for machine learning models of biological sequences. METHODS: We implement the chaos game in Python 3.8.10, and use it to produce fractal as well as novel expanding representations of protein sequences. We then feed the resulting images to a convolutional neural network, built in Python 3.8.10, using TensorFlow 2.9.1, Keras 2.9.0, and the scikit-learn 1.1.1 packages. We select as case study a recently published dataset for the antibody emibetuzumab, with the objective of co-optimizing antibodies variants with both high affinity and low non-specific binding.

Comparison of two serological screening strategies for cytomegalovirus primary infection in the first trimester of pregnancy.

INTRODUCTION: CMV serology screening in the first trimester pregnancy is based on IgG and IgM testing followed by IgG avidity in cases with positive IgM. However, the sensitivity of this strategy to diagnose maternal primary infection has been questioned. The objective of the study was to compare this strategy 1 with a strategy 2 consisting of running avidity test on all samples with positive IgG (ignoring IgM results) using fully automated current generation CMV IgG, IgM and IgG avidity assays. POPULATION AND METHODS: 1516 consecutive pregnant women between 12 and 14 weeks were screened in one maternity. Strategy 1 was done prospectively with LIAISON® CMV IgG II and LIAISON® CMV IgM II, followed by LIAISON® CMV IgG Avidity II and VIDAS® CMV IgG avidity II testing in cases with positive or equivocal IgM. Strategy 2 was done retrospectively on the same population and consisted of running avidity with the LIAISON® CMV IgG Avidity II in all samples with positive IgG. RESULTS: The sensitivity to diagnose a confirmed or a possible maternal primary infection in the first trimester was 91.6 % and 83 % for strategy 1 and 2 respectively (p > 0.99). Strategy 1 missed one possible primary infection and strategy 2 missed 2 confirmed primary infection. Inconclusive results happened in 0 and 0.7 % of samples with strategy 1 and 2 respectively. CONCLUSION: This study suggests that strategy 1 has better sensitivity and practicability than strategy 2. However, to achieve a good performance with strategy 1, using highly sensitive IgM assay is mandatory.