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.

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.

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.

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.

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.

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.

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.

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 .

Epitope-Based Specific Antibody Modifications.

Modified antibodies have essential roles in analytic, diagnostic, and therapeutic uses, and thus, these antibodies are required to have optimal physical and biological properties. Consequently, the development of methods for site-selective antibody modification is crucial. Herein, we used epitope-based affinity labeling to introduce a Fab region-selective antibody modification method. Although labeling that exploits the high affinity between an antibody and its epitope may appear straightforward, it remains challenging probably because of the loss of target affinity caused by modification around the epitope-binding site. By thoroughly screening the modifying agent structure, reaction conditions, and purification methods, we developed an efficient method for the selective modification of the Fab region of the antibody while maintaining the high affinity for the epitope.

Analysis of antibodies avidity for Tityus serrulatus scorpion venom in antivenom production and its potential for application as a potency test.

Antivenoms are the only specific medication for neutralizing toxins present in venom of animals such scorpions and snakes through antigen-antibody binding. Several analyses are carried out throughout its production in order to ensure the quality and effectiveness of the antivenom that will be administered to the patient. One of these is the potency assay, which is performed to assess the ability of antivenoms to neutralize the toxic effects of the venom injected in mice. The substitution of in vivo for in vitro assays such as ELISA has been presented by other authors, bringing several advantages such as the reduction in the use of animals, in costs and in the duration of the assays. However, the avidity index of antivenom antibodies determined by ELISA has not yet been applied for this purpose. Therefore, the objective of this study was to evaluate the avidity of sera from hyperimmunized horses with crude Tityus serrulatus venom, a scorpion species associated with the most serious accidents in Brazil, and its potential for application as a potency test replacing the in vivo assay. The avidity ELISA proved to be interesting for monitoring the binding strength of antibodies produced by horses in hyperimmune plasma production programs. It was possible to verify oscillations in antibody avidity that occurred along the immunization cycles, differences between novice and veteran horses, maturation of antibody avidity, and correlation between avidity index and antibody titre. Similar results were obtained for crude venom and purified Ts1 toxin. In addition, the avidity ELISA apparently demonstrated potential for application as a potency test in the initial stage of antivenom production. However, more studies are necessary.

Antibody Affinity and Stability Maturation by Error-Prone PCR.

Human antibodies are the most important class of biologicals, and antibodies - human and nonhuman - are indispensable as research agents and for diagnostic assays. When generating antibodies, they sometimes show the desired specificity profile but lack sufficient affinity for the desired application. In this article, a phage display-based method and protocol to increase the affinity of recombinant antibody fragments is given.The given protocol starts with the construction of a mutated antibody gene library by error-prone PCR. Subsequently, the selection of high-affinity variants is performed by panning on immobilized antigen with washing conditions optimized for off-rate-dependent selection. A screening ELISA protocol to identify antibodies with improved affinity and an additional protocol to select antibodies with improved thermal stability is described.

Revealing the SARS-CoV-2 Spike Protein and Specific Antibody Immune Complex Formation Mechanism for Precise Evaluation of Antibody Affinity.

The profound understanding and detailed evaluation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (SCoV2-S) protein and specific antibody interaction mechanism is of high importance in the development of immunosensors for COVID-19. In the present work, we studied a model system of immobilized SCoV2-S protein and specific monoclonal antibodies by molecular dynamics of immune complex formation in real time. We simultaneously applied spectroscopic ellipsometry and quartz crystal microbalance with dissipation to reveal the features and steps of the immune complex formation. We showed direct experimental evidence based on acoustic and optical measurements that the immune complex between covalently immobilized SCoV2-S and specific monoclonal antibodies is formed in two stages. Based on these findings it was demonstrated that applying a two-step binding mathematical model for kinetics analysis leads to a more precise determination of interaction rate constants than that determined by the 1:1 Langmuir binding model. Our investigation showed that the equilibrium dissociation constants (KD) determined by a two-step binding model and the 1:1 Langmuir model could differ significantly. The reported findings can facilitate a deeper understanding of antigen-antibody immune complex formation steps and can open a new way for the evaluation of antibody affinity towards corresponding antigens.

Pathogenesis of refractory ITP: Overview.

A subset of individuals with 'primary' or 'idiopathic' immune thrombocytopenia (ITP) who fail to respond to conventional first- and second-line agents or who lose responsiveness are considered to have 'refractory' disease (rITP), placing them at increased risk of bleeding and complications of intensive treatment. However, the criteria used to define the refractory state vary among studies, which complicates research and clinical investigation. Moreover, it is unclear whether rITP is simply 'more severe' ITP, or if there are specific pathogenic pathways that are more likely to result in refractory disease, and whether the presence or development of rITP can be established or anticipated based on these differences. This paper reviews potential biological features that may be associated with rITP, including genetic and epigenetic risk factors, dysregulation of T cells and cytokine networks, antibody affinity and specificity, activation of complement, impaired platelet production and alterations in platelet viability and clearance. These findings indicate the need for longitudinal studies using novel clinically available methodologies to identify and monitor pathogenic T cells, platelet antibodies and other clues to the development of refractory disease.

Affinity maturation of antibody fragments: A review encompassing the development from random approaches to computational rational optimization.

Routinely screened antibody fragments usually require further in vitro maturation to achieve the desired biophysical properties. Blind in vitro strategies can produce improved ligands by introducing random mutations into the original sequences and selecting the resulting clones under more and more stringent conditions. Rational approaches exploit an alternative perspective that aims first at identifying the specific residues potentially involved in the control of biophysical mechanisms, such as affinity or stability, and then to evaluate what mutations could improve those characteristics. The understanding of the antigen-antibody interactions is instrumental to develop this process the reliability of which, consequently, strongly depends on the quality and completeness of the structural information. Recently, methods based on deep learning approaches critically improved the speed and accuracy of model building and are promising tools for accelerating the docking step. Here, we review the features of the available bioinformatic instruments and analyze the reports illustrating the result obtained with their application to optimize antibody fragments, and nanobodies in particular. Finally, the emerging trends and open questions are summarized.

Protein engineering of a nanoCLAMP antibody mimetic scaffold as a platform for producing bioprocess-compatible affinity capture ligands.

Protein A affinity chromatography is widely used for the large-scale purification of antibodies because of its high yield, selectivity, and compatibility with NaOH sanitation. A general platform to produce robust affinity capture ligands for proteins beyond antibodies would improve bioprocessing efficiency. We previously developed nanoCLAMPs (nano Clostridial Antibody Mimetic Proteins), a class of antibody mimetic proteins useful as lab-scale affinity capture reagents. This work describes a protein engineering campaign to develop a more robust nanoCLAMP scaffold compatible with harsh bioprocessing conditions. The campaign generated an improved scaffold with dramatically improved resistance to heat, proteases, and NaOH. To isolate additional nanoCLAMPs based on this scaffold, we constructed a randomized library of 1 × 1010 clones and isolated binders to several targets. We then performed an in-depth characterization of nanoCLAMPs recognizing yeast SUMO, a fusion partner used for the purification of recombinant proteins. These second-generation nanoCLAMPs typically had a Kd of <80 nM, a Tm of >70 °C, and a t1/2 in 0.1 mg/ml trypsin of >20 h. Affinity chromatography resins bearing these next-generation nanoCLAMPs enabled single-step purifications of SUMO fusions. Bound target proteins could be eluted at neutral or acidic pH. These affinity resins maintained binding capacity and selectivity over 20 purification cycles, each including 10 min of cleaning-in-place with 0.1 M NaOH, and remained functional after exposure to 100% DMF and autoclaving. The improved nanoCLAMP scaffold will enable the development of robust, high-performance affinity chromatography resins against a wide range of protein targets.

ARMADiLLO: a web server for analyzing antibody mutation probabilities.

Antibodies are generated by B cells that evolve receptor specificity to pathogens through rounds of mutation and selection in a process called affinity maturation. Somatic hypermutation is mediated by an enzyme with DNA sequence context-dependent targeting and substitution resulting in variable probabilities of amino acid substitutions during affinity maturation. We have previously developed a program called Antigen Receptor Mutation Analyzer for the Detection of Low Likelihood Occurrences (ARMADiLLO) that performs simulations of the somatic hypermutation process to estimate the probabilities of observed antibody mutations. Here we describe the ARMADiLLO web server (https://armadillo.dhvi.duke.edu), an easy-to-use web interface that analyzes input antibody sequences and displays the probability estimates for all possible amino acid changes over the full length of an antibody sequence. The probability of antibody mutations can be used by immunologists studying B cell ontogenies and by vaccine designers that are pursuing strategies to elicit broadly neutralizing antibodies which are enriched with developmentally rate-limiting improbable mutations. The ARMADiLLO web server also contains precomputed results reporting the probability of amino acid substitutions in all human V gene segments and in a collection of HIV broadly neutralizing antibodies.

Protein arginine methyltransferase 1 regulates B cell fate after positive selection in the germinal center in mice.

Positively selected germinal center B cells (GCBC) can either resume proliferation and somatic hypermutation or differentiate. The mechanisms dictating these alternative cell fates are incompletely understood. We show that the protein arginine methyltransferase 1 (Prmt1) is upregulated in murine GCBC by Myc and mTORC-dependent signaling after positive selection. Deleting Prmt1 in activated B cells compromises antibody affinity maturation by hampering proliferation and GCBC light zone to dark zone cycling. Prmt1 deficiency also results in enhanced memory B cell generation and plasma cell differentiation, albeit the quality of these cells is compromised by the GCBC defects. We further demonstrate that Prmt1 intrinsically limits plasma cell differentiation, a function co-opted by B cell lymphoma (BCL) cells. Consistently, PRMT1 expression in BCL correlates with poor disease outcome, depends on MYC and mTORC1 activity, is required for cell proliferation, and prevents differentiation. Collectively, these data identify PRMT1 as a determinant of normal and cancerous mature B cell proliferation and differentiation balance.