Enhancing Nanobody Immunoassays through Ferritin Fusion: Construction of a Salmonella-Specific Fenobody for Improved Avidity and Sensitivity.

Nanobodies (Nbs) serve as powerful tools in immunoassays. However, their small size and monovalent properties pose challenges for practical application. Multimerization emerges as a significant strategy to address these limitations, enhancing the utilization of nanobodies in immunoassays. Herein, we report the construction of a Salmonella-specific fenobody (Fb) through the fusion of a nanobody to ferritin, resulting in a self-assembled 24-valent nanocage-like structure. The fenobody exhibits a 35-fold increase in avidity compared to the conventional nanobody while retaining good thermostability and specificity. Leveraging this advancement, three ELISA modes were designed using Fb as the capture antibody, along with unmodified Nb422 (FbNb-ELISA), biotinylated Nb422 (FbBio-ELISA), and phage-displayed Nb422 (FbP-ELISA) as the detection antibody, respectively. Notably, the FbNb-ELISA demonstrates a detection limit (LOD) of 3.56 × 104 CFU/mL, which is 16-fold lower than that of FbBio-ELISA and similar to FbP-ELISA. Moreover, a fenobody and nanobody sandwich chemiluminescent enzyme immunoassay (FbNb-CLISA) was developed by replacing the TMB chromogenic substrate with luminal, resulting in a 12-fold reduction in the LOD. Overall, the ferritin-displayed technology represents a promising methodology for enhancing the detection performance of nanobody-based sandwich ELISAs, thereby expanding the applicability of Nbs in food detection and other fields requiring multivalent modification.

Unsupervised evolution of protein and antibody complexes with a structure-informed language model.

Large language models trained on sequence information alone can learn high-level principles of protein design. However, beyond sequence, the three-dimensional structures of proteins determine their specific function, activity, and evolvability. Here, we show that a general protein language model augmented with protein structure backbone coordinates can guide evolution for diverse proteins without the need to model individual functional tasks. We also demonstrate that ESM-IF1, which was only trained on single-chain structures, can be extended to engineer protein complexes. Using this approach, we screened about 30 variants of two therapeutic clinical antibodies used to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We achieved up to 25-fold improvement in neutralization and 37-fold improvement in affinity against antibody-escaped viral variants of concern BQ.1.1 and XBB.1.5, respectively. These findings highlight the advantage of integrating structural information to identify efficient protein evolution trajectories without requiring any task-specific training data.

Characterization of two affinity matured Anti-Yersinia pestis F1 human antibodies with medical countermeasure potential.

Yersinia pestis, the causative agent of plague and a biological threat agent, presents an urgent need for novel medical countermeasures due to documented cases of naturally acquired antibiotic resistance and potential person-to-person spread during a pneumonic infection. Immunotherapy has been proposed as a way to circumvent current and future antibiotic resistance. Here, we describe the development and characterization of two affinity matured human antibodies (αF1Ig AM2 and αF1Ig AM8) that promote survival of mice after exposure to aerosolized Y. pestis. We share details of the error prone PCR and yeast display technology-based affinity maturation process that we used. The resultant matured antibodies have nanomolar affinity for Y. pestis F1 antigen, are produced in high yield, and are resilient to 37°C stress for up to 6 months. Importantly, in vitro assays using a murine macrophage cell line demonstrated that αF1Ig AM2 and αF1Ig AM8 are opsonic. Even more importantly, in vivo studies using pneumonic plague mouse models showed that 100% of the mice receiving 500 µg of IgGs αF1Ig AM2 and αF1Ig AM8 survived lethal challenge with aerosolized Y. pestis CO92. Combined, these results provide evidence of the quality and robustness of αF1Ig AM2 and αF1Ig AM8 and support their development as potential medical countermeasures against plague.

Early determination of potential critical quality attributes of therapeutic antibodies in developability studies through surface plasmon resonance-based relative binding activity assessment.

Precise measurement of the binding activity changes of therapeutic antibodies is important to determine the potential critical quality attributes (CQAs) in developability assessment at the early stage of antibody development. Here, we report a surface plasmon resonance (SPR)-based relative binding activity method, which incorporates both binding affinity and binding response and allows us to determine relative binding activity of antibodies with high accuracy and precision. We applied the SPR-based relative binding activity method in multiple forced degradation studies of antibody developability assessment. The current developability assessment strategy provided comprehensive, precise characterization of antibody binding activity in the stability studies, enabling us to perform correlation analysis and establish the structure-function relationship between relative binding activity and quality attributes. The impact of a given quality attribute on binding activity could be confidently determined without isolating antibody variants. We identified several potential CQAs, including Asp isomerization, Asn deamidation, and fragmentation. Some potential CQAs affected binding affinity of antibody and resulted in a reduction of binding activity. Certain potential CQAs impaired antibody binding to antigen and led to a loss of binding activity. A few potential CQAs could influence both binding affinity and binding response and cause a substantial decrease in antibody binding activity. Specifically, we identified low abundance Asn33 deamidation in the light chain complementarity-determining region as a potential CQA, in which all the stressed antibody samples showed Asn33 deamidation abundances ranging from 4.2% to 27.5% and a mild binding affinity change from 1.76 nM to 2.16 nM.

Serological immune biomarker for disease severity in dengue-infected pediatric hospitalized patients.

Clinical manifestation of dengue disease ranges from asymptomatic, febrile fever without warning sign (DOS) to serious outcome dengue with warning sign (DWS) and severe disease (SD) leading to shock syndrome and death. The role of antibody response in natural dengue infection is complex and not completely understood. Here, we aimed to assess serological marker for disease severity. Antibody response of dengue-confirmed pediatric patients with acute secondary infection were evaluated against infecting virus, immature virus, and recombinant envelop protein. Immature virus antibody titers were significantly higher in DWS as compared to DOS (p = 0.0006). However, antibody titers against recombinant envelop protein were higher in DOS as compared to DWS, and antibody avidity was significantly higher against infecting virus in DOS. Serum samples of DOS patients displayed higher in vitro neutralization potential in plaque assay as compared to DWS, whereas DWS serum samples showed higher antibody-dependent enhancement in the in vitro enhancement assays. Thus, antibodies targeting immature virus can predict disease severity and could be used in early forecast of disease outcome using an enzyme-linked immunoassay assay system which is less laborious and cheaper than plaque assay system for correlates of protection and could help optimize medical care and resources.

AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences.

The optimization of therapeutic antibodies through traditional techniques, such as candidate screening via hybridoma or phage display, is resource-intensive and time-consuming. In recent years, computational and artificial intelligence-based methods have been actively developed to accelerate and improve the development of therapeutic antibodies. In this study, we developed an end-to-end sequence-based deep learning model, termed AttABseq, for the predictions of the antigen-antibody binding affinity changes connected with antibody mutations. AttABseq is a highly efficient and generic attention-based model by utilizing diverse antigen-antibody complex sequences as the input to predict the binding affinity changes of residue mutations. The assessment on the three benchmark datasets illustrates that AttABseq is 120% more accurate than other sequence-based models in terms of the Pearson correlation coefficient between the predicted and experimental binding affinity changes. Moreover, AttABseq also either outperforms or competes favorably with the structure-based approaches. Furthermore, AttABseq consistently demonstrates robust predictive capabilities across a diverse array of conditions, underscoring its remarkable capacity for generalization across a wide spectrum of antigen-antibody complexes. It imposes no constraints on the quantity of altered residues, rendering it particularly applicable in scenarios where crystallographic structures remain unavailable. The attention-based interpretability analysis indicates that the causal effects of point mutations on antibody-antigen binding affinity changes can be visualized at the residue level, which might assist automated antibody sequence optimization. We believe that AttABseq provides a fiercely competitive answer to therapeutic antibody optimization.

Antibody design using deep learning: from sequence and structure design to affinity maturation.

Deep learning has achieved impressive results in various fields such as computer vision and natural language processing, making it a powerful tool in biology. Its applications now encompass cellular image classification, genomic studies and drug discovery. While drug development traditionally focused deep learning applications on small molecules, recent innovations have incorporated it in the discovery and development of biological molecules, particularly antibodies. Researchers have devised novel techniques to streamline antibody development, combining in vitro and in silico methods. In particular, computational power expedites lead candidate generation, scaling and potential antibody development against complex antigens. This survey highlights significant advancements in protein design and optimization, specifically focusing on antibodies. This includes various aspects such as design, folding, antibody-antigen interactions docking and affinity maturation.

Insight into the avidity-affinity relationship of the bivalent, pH-dependent interaction between IgG and FcRn.

Monoclonal antibodies (mAbs) as therapeutics necessitate favorable pharmacokinetic properties, including extended serum half-life, achieved through pH-dependent binding to the neonatal Fc receptor (FcRn). While prior research has mainly investigated IgG-FcRn binding kinetics with a focus on single affinity values, it has been shown that each IgG molecule can engage two FcRn molecules throughout an endosomal pH gradient. As such, we present here a more comprehensive analysis of these interactions with an emphasis on both affinity and avidity by taking advantage of switchSENSE technology, a surface-based biosensor where recombinant FcRn was immobilized via short DNA nanolevers, mimicking the membranous orientation of the receptor. The results revealed insight into the avidity-to-affinity relationship, where assessing binding through a pH gradient ranging from pH 5.8 to 7.4 showed that the half-life extended IgG1-YTE has an affinity inflection point at pH 7.2, reflecting its engineering for improved FcRn binding compared with the wild-type counterpart. Furthermore, IgG1-YTE displayed a pH switch for the avidity enhancement factor at pH 6.2, reflecting strong receptor binding to both sides of the YTE-containing Fc, while avidity was abolished at pH 7.4. When compared with classical surface plasmon resonance (SPR) technology and complementary methods, the use of switchSENSE demonstrated superior capabilities in differentiating affinity from avidity within a single measurement. Thus, the methodology provides reliable kinetic rate parameters for both binding modes and their direct relationship as a function of pH. Also, it deciphers the potential effect of the variable Fab arms on FcRn binding, in which SPR has limitations. Our study offers guidance for how FcRn binding properties can be studied for IgG engineering strategies.

HIV-related immune activation attenuates polyfunctional IgG and memory B-cell responses to Tdap immunization during pregnancy.

BACKGROUND: Maternal pertussis vaccination with Tdap vaccine is recommended to protect newborns from severe postnatal infection. HIV-exposed uninfected (HEU) infants have a higher incidence of pertussis infection and may particularly benefit from maternal immunization. The impact of HIV infection on the quality of IgG and memory B cell (MBC) responses to Tdap vaccination in pregnant women (PW) living with HIV (PWH) is unknown. METHODS: In this observational study, humoral immune responses to Tdap vaccination, including IgG levels, Fc-dependent effector functions, and MBC frequencies, were measured before and after vaccination in 40 PWH and 42 HIV-uninfected PW. Placental transfer of IgG and avidity were assessed in cord blood (CB). Soluble and cellular immune activation markers were quantified at baseline. FINDINGS: One month after vaccination, PWH had lower frequencies of MBC compared with HIV-uninfected PW. At delivery, PWH had attenuated pertussis-specific IgG levels and Fc-dependent effector functions. Reduced levels of maternal vaccine polyfunctional IgG and IgG avidity were transferred to HEU as compared to HIV-unexposed newborns. After adjustment with ethnicity, maternal antibody levels and gestational age at vaccination, HIV infection was independently associated with decreased levels of PT specific-IgG in CB. Both maternal and neonatal pertussis-specific IgG responses as well as PT-specific IgG avidity were inversely correlated with maternal sCD14 levels before vaccination among PWH. INTERPRETATION: Maternal HIV infection is associated with attenuated humoral immune responses to Tdap vaccination that correlate with sCD14. Suboptimal transfer of maternal immunity may further increase the risk of severe pertussis infection in HEU infants. FUNDING: This work was supported by IRIS Fund managed by the Foundation Roi Baudouin [2017J1820690206902], Association Vésale pour la Recherche Médicale and the Medical Council of CHU Saint-Pierre and has been funded in part with Federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, US Department of Health and Human Services, under Award No. U19AI145825. N.D. is a clinical researcher and A.M. is Research Director at the Fonds de la Recherche Scientifique (F.R.S.-FNRS), Belgium. M.E.A. was partially supported by NIHNIAID1U19AI14825. This article is published with the support of the Fondation Universitaire of Belgium.

CDR grafting and site-directed mutagenesis approach for the generation and affinity maturation of Anti-CD20 nanobody.

BACKGROUND: Recently, new and advanced techniques have been adopted to design and produce nanobodies, which are used in diagnostic and immunotherapy treatments. Traditionally, nanobodies are prepared from camelid immune libraries that require animal treatments. However, such approaches require large library sizes and complicated selection procedures. The current study has employed CDR grafting and site-directed mutagenesis techniques to create genetically engineered nanobodies against the tumor marker CD20 (anti-CD20 nanobodies) used in leukemia treatment. METHODS AND RESULTS: In this study, we utilized the swapping method to graft CDRs from the VH Rituximab antibody to VHH CDRs. We aimed to enhance the binding affinity of the nanobodies by substituting the amino acids (Y101R-Y102R-Y107R) in the VHH-CDR3. To assess the binding capacity of the mutated nanobodies, we conducted an ELISA test. Moreover, through flow cytometry analysis, we compared the fluorescence intensity of the grafted CD20 and mutant nanobodies with that of the commercially available human anti-CD20 in Raji cells. The results showed a significant difference in the fluorescence intensity of the grafted nanobodies and mutant nanobodies when compared to the commercially available human anti-CD20. CONCLUSION: The approach we followed in this study makes it possible to create multiple anti-CD20 nanobodies with varying affinities without the need for extensive selection efforts. Additionally, our research has demonstrated that computational tools are highly reliable in designing functional nanobodies.

TRIM21 and Fc-engineered antibodies: decoding its complex antibody binding mode with implications for viral neutralization.

TRIM21 is a pivotal effector in the immune system, orchestrating antibody-mediated responses and modulating immune signaling. In this comprehensive study, we focus on the interaction of TRIM21 with Fc engineered antibodies and subsequent implications for viral neutralization. Through a series of analytical techniques, including biosensor assays, mass photometry, and electron microscopy, along with structure predictions, we unravel the intricate mechanisms governing the interplay between TRIM21 and antibodies. Our investigations reveal that the TRIM21 capacity to recognize, bind, and facilitate the proteasomal degradation of antibody-coated viruses is critically dependent on the affinity and avidity interplay of its interactions with antibody Fc regions. We suggest a novel binding mechanism, where TRIM21 binding to one Fc site results in the detachment of PRYSPRY from the coiled-coil domain, enhancing mobility due to its flexible linker, thereby facilitating the engagement of the second site, resulting in avidity due to bivalent engagement. These findings shed light on the dual role of TRIM21 in antiviral immunity, both in recognizing and directing viruses for intracellular degradation, and demonstrate its potential for therapeutic exploitation. The study advances our understanding of intracellular immune responses and opens new avenues for the development of antiviral strategies and innovation in tailored effector functions designed to leverage TRIM21s unique binding mode.

Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen.

Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and in silico antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (Tonset, Tm, Tagg), affinity (KD) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in Tm1), with most clones retaining the favorable developability profile of the parental antibody. Additional in silico tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of in silico antibody optimization without the need to predict the antibody-antigen interface, which is notoriously difficult in the absence of crystal structures.

A benchmark for evaluation of structure-based online tools for antibody-antigen binding affinity.

The prediction of binding affinity changes caused by missense mutations can elucidate antigen-antibody interactions. A few accessible structure-based online computational tools have been proposed. However, selecting suitable software for particular research is challenging, especially research on the SARS-CoV-2 spike protein with antibodies. Therefore, benchmarking of the mutation-diverse SARS-CoV-2 datasets is critical. Here, we collected the datasets including 1216 variants about the changes in binding affinity of antigens from 22 complexes for SARS-CoV-2 S proteins and 22 monoclonal antibodies as well as applied them to evaluate the performance of seven binding affinity prediction tools. The tested tools' Pearson correlations between predicted and measured changes in binding affinity were between -0.158 and 0.657, while accuracy in classification tasks on predicting increasing or decreasing affinity ranged from 0.444 to 0.834. These tools performed relatively better on predicting single mutations, especially at epitope sites, whereas poor performance on extremely decreasing affinity. The tested tools were relatively insensitive to the experimental techniques used to obtain structures of complexes. In summary, we constructed a list of datasets and evaluated a range of structure-based online prediction tools that will explicate relevant processes of antigen-antibody interactions and enhance the computational design of therapeutic monoclonal antibodies.

Affinity gaps among B cells in germinal centers drive the selection of MPER precursors.

Current prophylactic human immunodeficiency virus 1 (HIV-1) vaccine research aims to elicit broadly neutralizing antibodies (bnAbs). Membrane-proximal external region (MPER)-targeting bnAbs, such as 10E8, provide exceptionally broad neutralization, but some are autoreactive. Here, we generated humanized B cell antigen receptor knock-in mouse models to test whether a series of germline-targeting immunogens could drive MPER-specific precursors toward bnAbs. We found that recruitment of 10E8 precursors to germinal centers (GCs) required a minimum affinity for germline-targeting immunogens, but the GC residency of MPER precursors was brief due to displacement by higher-affinity endogenous B cell competitors. Higher-affinity germline-targeting immunogens extended the GC residency of MPER precursors, but robust long-term GC residency and maturation were only observed for MPER-HuGL18, an MPER precursor clonotype able to close the affinity gap with endogenous B cell competitors in the GC. Thus, germline-targeting immunogens could induce MPER-targeting antibodies, and B cell residency in the GC may be regulated by a precursor-competitor affinity gap.

A structure-function analysis shows SARS-CoV-2 BA.2.86 balances antibody escape and ACE2 affinity.

BA.2.86, a recently described sublineage of SARS-CoV-2 Omicron, contains many mutations in the spike gene. It appears to have originated from BA.2 and is distinct from the XBB variants responsible for many infections in 2023. The global spread and plethora of mutations in BA.2.86 has caused concern that it may possess greater immune-evasive potential, leading to a new wave of infection. Here, we examine the ability of BA.2.86 to evade the antibody response to infection using a panel of vaccinated or naturally infected sera and find that it shows marginally less immune evasion than XBB.1.5. We locate BA.2.86 in the antigenic landscape of recent variants and look at its ability to escape panels of potent monoclonal antibodies generated against contemporary SARS-CoV-2 infections. We demonstrate, and provide a structural explanation for, increased affinity of BA.2.86 to ACE2, which may increase transmissibility.

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.

Understanding the Specific Implications of Amino Acids in the Antibody Development.

As the demand for immunotherapy to treat and manage cancers, infectious diseases and other disorders grows, a comprehensive understanding of amino acids and their intricate role in antibody engineering has become a prime requirement. Naturally produced antibodies may not have the most suitable amino acids at the complementarity determining regions (CDR) and framework regions, for therapeutic purposes. Therefore, to enhance the binding affinity and therapeutic properties of an antibody, the specific impact of certain amino acids on the antibody's architecture must be thoroughly studied. In antibody engineering, it is crucial to identify the key amino acid residues that significantly contribute to improving antibody properties. Therapeutic antibodies with higher binding affinity and improved functionality can be achieved through modifications or substitutions with highly suitable amino acid residues. Here, we have indicated the frequency of amino acids and their association with the binding free energy in CDRs. The review also analyzes the experimental outcome of two studies that reveal the frequency of amino acids in CDRs and provides their significant correlation between the outcomes. Additionally, it discusses the various bond interactions within the antibody structure and antigen binding. A detailed understanding of these amino acid properties should assist in the analysis of antibody sequences and structures needed for designing and enhancing the overall performance of therapeutic antibodies.

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.

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.