Pharmaceutical innovation and advanced biotechnology in the biotech-pharmaceutical industry for antibody-drug conjugate development.

Antibody-drug conjugates (ADCs), from prototypes in the 1980s to first- and second-generation products in the 2000s, and now in their multiformats, have progressed tremendously to meet oncological challenges. Currently, 13 ADCs have been approved for medical practice, with over 200 candidates in clinical trials. Moreover, ADCs have evolved into different formats, including bispecific ADCs, probody-drug conjugates, pH-responsive ADCs, target-degrading ADCs, and immunostimulating ADCs. Technologies from biopharmaceutical industries have a crucial role in the clinical transition of these novel biotherapeutics. In this review, we highlight several features contributing to the prosperity of bioindustrial ADC development. Various proprietary technologies from biopharmaceutical companies are discussed. Such advances in biopharmaceutical industries are the backbone for the success of ADCs in development and clinical application.

A new mechanism of antibody diversity: formation of the natural antibodies containing LAIR1 and LILRB1 extracellular domains.

The recent discovery of public antibodies targeting Plasmodium falciparum-encoded repetitive interspersed families of polypeptides (RIFINs), which contain extracellular immunoglobulin-like domains from LAIR1 or LILRB1, constitutes a significant step forward in comprehending the reactivity of the Plasmodium parasite. These antibodies arise from unique B cell clones and demonstrate extensive cross-reactivity through their interaction with P. falciparum RIFINs. LAIR1 and LILRBs are specialized type I transmembrane glycoproteins, classified as immune inhibitory receptors, restricted to primates and mainly found on hematopoietic cells. They are instrumental in modulating interactions within the tumor microenvironment and across the immune system, and are increasingly recognized as important in anti-cancer immunotherapy and pathogen defense. The presence of LAIR1/LILRB1-containing antibodies offers new insights into malaria parasite evasion strategies and the immune system's response. Additionally, the innovative method of integrating extra exons into the antibody switch region is a noteworthy advancement, enriching the strategies for the generation of a varied array of bispecific and multispecific antibodies.

Broadly protective bispecific antibodies that simultaneously target influenza virus hemagglutinin and neuraminidase.

Monoclonal antibodies (mAbs) are an attractive therapeutic platform for the prevention and treatment of influenza virus infection. There are two major glycoproteins on the influenza virion surface: hemagglutinin (HA), which is responsible for viral attachment and entry, and neuraminidase (NA), which mediates viral egress by enzymatically cleaving sialic acid to release budding particles from the host cell surface. Broadly neutralizing antibodies (bNAbs) that target the conserved HA central stalk region, such as CR9114, can inhibit both viral entry and egress. More recently, broadly binding mAbs that engage and inhibit the NA active site, such as 1G01, have been described to prevent viral egress. Here, we engineered bispecific antibodies (bsAbs) that combine the variable domains of CR9114 and 1G01 into a single molecule and evaluated if simultaneous targeting of two different glycoproteins improved antiviral properties in vitro and in vivo. Several CR9114/1G01 bsAbs were generated with various configurations of the two sets of the variable domains ("bsAb formats"). We found that combinations employing the addition of a single-chain variable fragment in the hinge region of an IgG scaffold had the best properties in terms of expression, stability, and binding. Further characterization of selected bsAbs showed potent neutralizing and egress-inhibiting activity. One such bsAb ("hSC_CR9114_1G01") provided higher levels of prophylactic protection from mortality and morbidity upon challenge with H1N1 than either of the parental mAbs at low dosing (1 mg/kg). These results highlight the potential use of bsAbs that simultaneously target HA and NA as new influenza immunotherapeutics. IMPORTANCE: Infection by the influenza virus remains a global health burden. The approaches utilized here to augment the activity of broadly protective influenza virus antibodies may lead to a new class of immunotherapies with enhanced activity.

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.

Therapeutic antibody engineering for efficient targeted degradation of membrane proteins in lysosomes.

Targeted degradation of pathological proteins is a promising approach to enhance the effectiveness of therapeutic monoclonal antibodies (mAbs) in cancer therapy. In this study, we demonstrate that this objective can be efficiently achieved by the grafting of mannose 6-phosphate analogues called AMFAs2 onto the therapeutic antibodies trastuzumab and cetuximab, both directed against membrane antigens. The grafting of AMFAs confers to these antibodies the novel property of being internalized via the mannose 6-phosphate receptor (M6PR) pathway. AMFA conjugation to these mAbs significantly increases their cellular uptake and leads to enhanced degradation of the target antigens in cancer cells. This results in a drastic inhibition of cancer cell proliferation compared to unconjugated mAbs, as demonstrated in various cancer cell lines, and an increased therapeutic efficacy in mouse and zebrafish xenografted models. These findings highlight the potential of this technology to improve therapeutic outcomes in cancer treatment.

Unleashing the power of antibodies: Engineering for tomorrow's therapy.

Antibodies play a crucial role in host defense against various diseases. Antibody engineering is a multidisciplinary field that seeks to improve the quality of life of humans. In the context of disease, antibodies are highly specialized proteins that form a critical line of defense against pathogens and the disease caused by them. These infections trigger the innate arm of immunity by presenting on antigen-presenting cells such as dendritic cells. This ultimately links to the adaptive arm, where antibody production and maturation occur against that particular antigen. Upon binding with their specific antigens, antibodies trigger various immune responses to eliminate pathogens in a process called complement-dependent cytotoxicity and phagocytosis of invading microorganisms by immune cells or induce antibody-dependent cellular cytotoxicity is done by antibodies. These engineered antibodies are being used for various purposes, such as therapeutics, diagnostics, and biotechnology research. Cutting-edge techniques that include hybridoma technology, transgenic mice, display techniques like phage, yeast and ribosome displays, and next-generation sequencing are ways to engineer antibodies and mass production for the use of humankind. Considering the importance of antibodies in protecting from a diverse array of pathogens, investing in research holds great promise to develop future therapeutic targets to combat various diseases.

Engineering high affinity antigen-binders: Beyond conventional antibodies.

For decades, antibodies have remained the archetypal binding proteins that can be rapidly produced with high affinity and specificity against virtually any target. A conventional antibody is still considered the prototype of a binding molecule. It is therefore not surprising that antibodies are routinely used in basic scientific and biomedical research, analytical workflows, molecular diagnostics etc. and represent the fastest growing sector in the field of biotechnology. However, several limitations associated with conventional antibodies, including stringent requirement of animal immunizations, mammalian cells for expression, issues on stability and aggregation, bulkier size and the overall time and cost of production has propelled evolution of concepts along alternative antigen binders. Rapidly evolving protein engineering approaches and high throughput screening platforms have further complemented the development of myriads of classes of non-conventional protein binders including antibody derived as well as non-antibody based molecular scaffolds. These non-canonical binders are finding use across disciplines of which diagnostics and therapeutics are the most noteworthy.

Cytokine/Antibody Fusion Protein Design and Evaluation.

Cytokines constitute a class of secreted proteins that activate transmembrane receptors to coordinate a vast array of physiological processes, particularly those related to immune activity. Due to their vital role in immune regulation, cytokines have garnered great interest as potential therapeutic agents. Unfortunately, the clinical success of cytokine drugs has been limited by their multifunctional activities, which hinder therapeutic performance and lead to harmful toxicities. In addition, the strikingly short circulation half-life of cytokines further hampers their efficacy as drugs. To overcome the translational challenges associated with natural cytokines, significant efforts have focused on engineering cytokines to target their activities and improve their pharmacological properties. One such strategy is the design of fusion proteins that tether a cytokine to an anti-cytokine antibody that selectively biases its functions and extends its serum half-life. These cytokine/antibody fusion proteins (termed immunocytokines) assemble intramolecularly to bias cytokine signaling behavior through multi-layered structural and molecular effects. Here, we present a detailed workflow for the design, production, and functional validation of intramolecularly assembled immunocytokines. In-depth procedures are presented for gene manipulation, mammalian cell-based expression and purification, binding analysis via bio-layer interferometry, and interrogation of cytokine signaling activity on human primary cells. In contrast with immunocytokines in which the tethered cytokine and antibody do not bind one another, intramolecularly assembled immunocytokines require special considerations with respect to their production to avoid oligomerization and/or aggregation. The protocol herein was developed based on experience with immunocytokines that incorporate interleukin-2 (IL-2); however, this modular approach can be extended to any cytokine of interest for a broad range of biomedical applications. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Design and generation of immunocytokine genes Basic Protocol 2: Immunocytokine expression and purification Basic Protocol 3: Validation of immunocytokine assembly and binding by bio-layer interferometry Basic Protocol 4: Analysis of immunocytokine signaling on human primary cells.

Differential Roles of Kinetic On- and Off-Rates in T-Cell Receptor Signal Integration Revealed with a Modified Fab'-DNA Ligand.

Antibody-derived T-cell receptor (TCR) agonists are commonly used to activate T cells. While antibodies can trigger TCRs regardless of clonotype, they bypass native T cell signal integration mechanisms that rely on monovalent, membrane-associated, and relatively weakly-binding ligand in the context of cellular adhesion. Commonly used antibodies and their derivatives bind much more strongly than native peptide-MHC (pMHC) ligands bind their cognate TCRs. Because ligand dwell time is a critical parameter that tightly correlates with physiological function of the TCR signaling system, there is a general need, both in research and therapeutics, for universal TCR ligands with controlled kinetic binding parameters. To this end, we have introduced point mutations into recombinantly expressed α-TCRß H57 Fab to modulate the dwell time of monovalent Fab binding to TCR. When tethered to a supported lipid bilayer via DNA complementation, these monovalent Fab'-DNA ligands activate T cells with potencies well-correlated with their TCR binding dwell time. Single-molecule tracking studies in live T cells reveal that individual binding events between Fab'-DNA ligands and TCRs elicit local signaling responses closely resembling native pMHC. The unique combination of high on- and off-rate of the H57 R97L mutant enables direct observations of cooperative interplay between ligand binding and TCR-proximal condensation of the linker for activation of T cells (LAT), which is not readily visualized with pMHC. This work provides insights into how T cells integrate kinetic information from synthetic ligands and introduces a method to develop affinity panels for polyclonal T cells, such as cells from a human patient.

Beyond bNAbs: Uses, Risks, and Opportunities for Therapeutic Application of Non-Neutralising Antibodies in Viral Infection.

The vast majority of antibodies generated against a virus will be non-neutralising. However, this does not denote an absence of protective capacity. Yet, within the field, there is typically a large focus on antibodies capable of directly blocking infection (neutralising antibodies, NAbs) of either specific viral strains or multiple viral strains (broadly-neutralising antibodies, bNAbs). More recently, a focus on non-neutralising antibodies (nNAbs), or neutralisation-independent effects of NAbs, has emerged. These can have additive effects on protection or, in some cases, be a major correlate of protection. As their name suggests, nNAbs do not directly neutralise infection but instead, through their Fc domains, may mediate interaction with other immune effectors to induce clearance of viral particles or virally infected cells. nNAbs may also interrupt viral replication within infected cells. Developing technologies of antibody modification and functionalisation may lead to innovative biologics that harness the activities of nNAbs for antiviral prophylaxis and therapeutics. In this review, we discuss specific examples of nNAb actions in viral infections where they have known importance. We also discuss the potential detrimental effects of such responses. Finally, we explore new technologies for nNAb functionalisation to increase efficacy or introduce favourable characteristics for their therapeutic applications.

Development of an effective single-chain variable fragment recognizing a novel epitope in the hepatitis C virus E2 protein that restricts virus entry into hepatocytes.

Previously, we reported a neutralizing monoclonal antibody, A8A11, raised against a novel conserved epitope within the hepatitis C virus (HCV) E2 protein, that could significantly reduce HCV replication. Here, we report the nucleotide sequence of A8A11 and demonstrate the efficacy of a single-chain variable fragment (scFv) protein that mimics the antibody, inhibits the binding of an HCV virus-like particle to hepatocytes, and reduces viral RNA replication in a cell culture system. More importantly, scFv A8A11 was found to effectively restrict the increase of viral RNA levels in the serum of HCV-infected chimeric mice harbouring human hepatocytes. These results suggest a promising approach to neutralizing-antibody-based therapeutic interventions against HCV infection.

A new era of antibody discovery: an in-depth review of AI-driven approaches.

Given their high affinity and specificity for a range of macromolecules, antibodies are widely used in the treatment of autoimmune diseases, cancers, inflammatory diseases, and Alzheimer's disease (AD). Traditional experimental methods are time-consuming, expensive, and labor-intensive. Recent advances in artificial intelligence (AI) technologies provide complementary methods that can reduce the time and costs required for antibody design by minimizing failures and increasing the success rate of experimental tests. In this review, we scrutinize the plethora of AI-driven methodologies that have been deployed over the past 4 years for modeling antibody structures, predicting antibody-antigen interactions, optimizing antibody affinity, and generating novel antibody candidates. We also briefly address the challenges faced in integrating AI-based models with traditional antibody discovery pipelines and highlight the potential future directions in this burgeoning field.

Evaluation of the cyclic single-chain Fv antibody derived from nivolumab by biophysical analyses and in vitro cell-based bioassay.

Single-chain Fv (scFv) is a recombinant small antibody in which a polypeptide linker connects the variable regions of the light chain (VL) and the heavy chain (VH). The practical use of scFv, however, has been prevented by its tendency to aggregate due to interchain VL-VH interactions. We recently developed a cyclic scFv whose N-terminus and C-terminus were connected by protein ligation techniques. Biophysical comparisons between cyclic and linear scFv have been conducted, but cell biological evaluations remain unexplored. Here we studied the properties of cyclic and linear scFv derived from nivolumab. Biophysical studies revealed that the thermal stability was not changed but that the antigen-binding activity was approximately 3-fold higher as a result of circularization. A cell-based PD-1/PD-L1 interaction inhibitory assay revealed that the biological activity of scFv was markedly higher in the circularized form. In addition, biophysical analysis of scFv proteins incubated in the presence of serum revealed that circularization suppressed the decrease in antigen-binding activity. It could be assumed that circularization of scFv improved stability in the presence of serum, which in turn would suggest the applicability of cyclic scFv as a biopharmaceutical format.

NanoBERTa-ASP: predicting nanobody paratope based on a pretrained RoBERTa model.

BACKGROUND: Nanobodies, also known as VHH or single-domain antibodies, are unique antibody fragments derived solely from heavy chains. They offer advantages of small molecules and conventional antibodies, making them promising therapeutics. The paratope is the specific region on an antibody that binds to an antigen. Paratope prediction involves the identification and characterization of the antigen-binding site on an antibody. This process is crucial for understanding the specificity and affinity of antibody-antigen interactions. Various computational methods and experimental approaches have been developed to predict and analyze paratopes, contributing to advancements in antibody engineering, drug development, and immunotherapy. However, existing predictive models trained on traditional antibodies may not be suitable for nanobodies. Additionally, the limited availability of nanobody datasets poses challenges in constructing accurate models. METHODS: To address these challenges, we have developed a novel nanobody prediction model, named NanoBERTa-ASP (Antibody Specificity Prediction), which is specifically designed for predicting nanobody-antigen binding sites. The model adopts a training strategy more suitable for nanobodies, based on an advanced natural language processing (NLP) model called BERT (Bidirectional Encoder Representations from Transformers). To be more specific, the model utilizes a masked language modeling approach named RoBERTa (Robustly Optimized BERT Pretraining Approach) to learn the contextual information of the nanobody sequence and predict its binding site. RESULTS: NanoBERTa-ASP achieved exceptional performance in predicting nanobody binding sites, outperforming existing methods, indicating its proficiency in capturing sequence information specific to nanobodies and accurately identifying their binding sites. Furthermore, NanoBERTa-ASP provides insights into the interaction mechanisms between nanobodies and antigens, contributing to a better understanding of nanobodies and facilitating the design and development of nanobodies with therapeutic potential. CONCLUSION: NanoBERTa-ASP represents a significant advancement in nanobody paratope prediction. Its superior performance highlights the potential of deep learning approaches in nanobody research. By leveraging the increasing volume of nanobody data, NanoBERTa-ASP can further refine its predictions, enhance its performance, and contribute to the development of novel nanobody-based therapeutics. Github repository: https://github.com/WangLabforComputationalBiology/NanoBERTa-ASP.

A selection and optimization strategy for single-domain antibodies targeting the PHF6 linear peptide within the tau intrinsically disordered protein.

The use of variable domain of the heavy-chain of the heavy-chain-only antibodies (VHHs) as disease-modifying biomolecules in neurodegenerative disorders holds promises, including targeting of aggregation-sensitive proteins. Exploitation of their clinical values depends however on the capacity to deliver VHHs with optimal physico-chemical properties for their specific context of use. We described previously a VHH with high therapeutic potential in a family of neurodegenerative diseases called tauopathies. The activity of this promising parent VHH named Z70 relies on its binding within the central region of the tau protein. Accordingly, we carried out random mutagenesis followed by yeast two-hybrid screening to obtain optimized variants. The VHHs selected from this initial screen targeted the same epitope as VHH Z70 as shown using NMR spectroscopy and had indeed improved binding affinities according to dissociation constant values obtained by surface plasmon resonance spectroscopy. The improved affinities can be partially rationalized based on three-dimensional structures and NMR data of three complexes consisting of an optimized VHH and a peptide containing the tau epitope. Interestingly, the ability of the VHH variants to inhibit tau aggregation and seeding could not be predicted from their affinity alone. We indeed showed that the in vitro and in cellulo VHH stabilities are other limiting key factors to their efficacy. Our results demonstrate that only a complete pipeline of experiments, here described, permits a rational selection of optimized VHH variants, resulting in the selection of VHH variants with higher affinities and/or acting against tau seeding in cell models.

Reduction of monoclonal antibody viscosity using interpretable machine learning.

Early identification of antibody candidates with drug-like properties is essential for simplifying the development of safe and effective antibody therapeutics. For subcutaneous administration, it is important to identify candidates with low self-association to enable their formulation at high concentration while maintaining low viscosity, opalescence, and aggregation. Here, we report an interpretable machine learning model for predicting antibody (IgG1) variants with low viscosity using only the sequences of their variable (Fv) regions. Our model was trained on antibody viscosity data (>100 mg/mL mAb concentration) obtained at a common formulation pH (pH 5.2), and it identifies three key Fv features of antibodies linked to viscosity, namely their isoelectric points, hydrophobic patch sizes, and numbers of negatively charged patches. Of the three features, most predicted antibodies at risk for high viscosity, including antibodies with diverse antibody germlines in our study (79 mAbs) as well as clinical-stage IgG1s (94 mAbs), are those with low Fv isoelectric points (Fv pIs < 6.3). Our model identifies viscous antibodies with relatively high accuracy not only in our training and test sets, but also for previously reported data. Importantly, we show that the interpretable nature of the model enables the design of mutations that significantly reduce antibody viscosity, which we confirmed experimentally. We expect that this approach can be readily integrated into the drug development process to reduce the need for experimental viscosity screening and improve the identification of antibody candidates with drug-like properties.

The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design.

Synthetic antibodies (Abs) represent a category of artificial proteins capable of closely emulating the functions of natural Abs. Their in vitro production eliminates the need for an immunological response, streamlining the process of Ab discovery, engineering, and development. These artificially engineered Abs offer novel approaches to antigen recognition, paratope site manipulation, and biochemical/biophysical enhancements. As a result, synthetic Abs are fundamentally reshaping conventional methods of Ab production. This mirrors the revolution observed in molecular biology and genomics as a result of deep sequencing, which allows for the swift and cost-effective sequencing of DNA and RNA molecules at scale. Within this framework, deep sequencing has enabled the exploration of whole genomes and transcriptomes, including particular gene segments of interest. Notably, the fusion of synthetic Ab discovery with advanced deep sequencing technologies is redefining the current approaches to Ab design and development. Such combination offers opportunity to exhaustively explore Ab repertoires, fast-tracking the Ab discovery process, and enhancing synthetic Ab engineering. Moreover, advanced computational algorithms have the capacity to effectively mine big data, helping to identify Ab sequence patterns/features hidden within deep sequencing Ab datasets. In this context, these methods can be utilized to predict novel sequence features thereby enabling the successful generation of de novo Ab molecules. Hence, the merging of synthetic Ab design, deep sequencing technologies, and advanced computational models heralds a new chapter in Ab discovery, broadening our comprehension of immunology and streamlining the advancement of biological therapeutics.

Biparatopic antibodies: therapeutic applications and prospects.

Biparatopic antibodies (bpAbs) bind distinct, non-overlapping epitopes on an antigen. This unique binding mode enables new mechanisms of action beyond monospecific and bispecific antibodies (bsAbs) that can make bpAbs effective therapeutics for various indications, including oncology and infectious diseases. Biparatopic binding can lead to superior affinity and specificity, promote antagonism, lock target conformation, and result in higher-order target clustering. Such antibody-target complexes can elicit strong agonism, increase immune effector function, or result in rapid target downregulation and lysosomal trafficking. These are not only attractive properties for therapeutic antibodies but are increasingly being explored for other modalities such as antibody-drug conjugates, T-cell engagers and chimeric antigen receptors. Recent advances in bpAb engineering have enabled the construction of ever more sophisticated formats that are starting to show promise in the clinic.

A novel conditional active biologic anti-EpCAM x anti-CD3 bispecific antibody with synergistic tumor selectivity for cancer immunotherapy.

Epithelial cell adhesion molecule (EpCAM) is a transmembrane glycoprotein that plays several roles in cancer biology. EpCAM is an attractive therapeutic target because of its expression in most solid tumors. However, targeting EpCAM has been challenging because it is also highly expressed in normal epithelial tissues. Initial attempts to develop EpCAM-specific T-cell engagers were unsuccessful due to severe cytokine release effects, as well as serious on-target, off-tumor drug-related toxicities. We developed novel, conditionally active biological (CAB) bispecific antibodies that bind to both EpCAM and CD3 in an acidic tumor microenvironment. In healthy tissues, binding to EpCAM and CD3 is greatly reduced by a novel, dual CAB selection, where each binding domain is independently blocked by the presence of physiological chemicals known as Protein-associated Chemical Switches (PaCS). The CAB anti-EpCAM T-cell engagers displayed the anticipated bispecific binding properties and mediated the potent lysis of EpCAM-positive cancer cell lines through the recruitment of T cells in the tumor microenvironment. Xenograft studies showed that the efficacy of CAB bispecific antibodies is similar to that of a non-CAB anti-EpCAM bispecific antibody, but they have markedly reduced toxicity in non-human primates, indicating an unprecedentedly widened therapeutic index of over 100-fold. These preclinical results indicate that the dual CAB bispecific antibody is potentially both a powerful and safe therapeutic platform and a promising T cell-engaging treatment for patients with EpCAM-expressing tumors.

Development of a novel conditionally active EpCAM-specific T-cell engager with enhanced safety and tolerability for treatment of solid tumors.

Enhancing bezlotoxumab binding to C. difficile toxin B2: insights from computational simulations and mutational analyses for antibody design.

Clostridioides difficile infection (CDI) is a significant concern caused by widespread antibiotic use, resulting in diarrhea and inflammation from the gram-positive anaerobic bacterium C. difficile. Although bezlotoxumab (Bez), a monoclonal antibody (mAb), was developed to address CDI recurrences, the recurrence rate remains high, partly due to reduced neutralization efficiency against toxin B2. In this study, we aimed to enhance the binding of Bez to C. difficile toxin B2 by combining computational simulations and mutational analyses. We identified specific mutations in Bez, including S28R, S31W/K, Y32R, S56W and G103D/S in the heavy chain (Hc), and S32F/H/R/W/Y in the light chain (Lc), which significantly improved binding to toxin B2 and formed critical protein-protein interactions. Through molecular dynamics simulations, several single mutations, such as HcS28R, LcS32H, LcS32R, LcS32W and LcS32Y, exhibited superior binding affinities to toxin B2 compared to Bez wild-type (WT), primarily attributed to Coulombic interactions. Combining the HcS28R mutation with four different mutations at residue LcS32 led to even greater binding affinities in double mutants (MTs), particularly HcS28R/LcS32H, HcS28R/LcS32R and HcS28R/LcS32Y, reinforcing protein-protein binding. Analysis of per-residue decomposition free energy highlighted key residues contributing significantly to enhanced binding interactions, emphasizing the role of electrostatic interactions. These findings offer insights into rational Bez MT design for improved toxin B2 binding, providing a foundation for developing more effective antibodies to neutralize toxin B2 and combat-related infections.Communicated by Ramaswamy H. Sarma.