Antibody Structure and Function: The Basis for Engineering Therapeutics

Modulation of the high concentration viscosity of IgG1 antibodies using clinically validated Fc mutations

The self-association of therapeutic antibodies can result in elevated viscosity and create problems in manufacturing and formulation, as well as limit delivery by subcutaneous injection. The high concentration viscosity of some antibodies has been reduced by variable domain mutations or by the addition of formulation excipients. In contrast, the impact of Fc mutations on antibody viscosity has been minimally explored. Here, we studied the effect of a panel of common and clinically validated Fc mutations on the viscosity of two closely related humanized IgG1, κ antibodies, omalizumab (anti-IgE) and trastuzumab (anti-HER2). Data presented here suggest that both Fab-Fab and Fab-Fc interactions contribute to the high viscosity of omalizumab, in a four-contact model of self-association. Most strikingly, the high viscosity of omalizumab (176 cP) was reduced 10.7- and 2.2-fold by Fc modifications for half-life extension (M252Y:S254T:T256E) and aglycosylation (N297G), respectively. Related single mutations (S254T and T256E) each reduced the viscosity of omalizumab by ~6-fold. An alternative half-life extension Fc mutant (M428L:N434S) had the opposite effect in increasing the viscosity of omalizumab by 1.5-fold. The low viscosity of trastuzumab (8.6 cP) was unchanged or increased by ≤ 2-fold by the different Fc variants. Molecular dynamics simulations provided mechanistic insight into the impact of Fc mutations in modulating electrostatic and hydrophobic surface properties as well as conformational stability of the Fc. This study demonstrates that high viscosity of some IgG1 antibodies can be mitigated by Fc mutations, and thereby offers an additional tool to help design future antibody therapeutics potentially suitable for subcutaneous delivery.

LC-MS Approach to Decipher a Light Chain Chromatographic Peak Splitting of a Monoclonal Antibody

PURPOSE

Monoclonal antibodies (mAbs), like other protein therapeutics, are prone to various forms of degradation, some of which are difficult to distinguish from the native form yet may alter potency. A generalizable LC-MS approach was developed to enable quantitative analysis of isoAsp. In-depth understanding of product quality attributes (PQAs) enables optimization of the manufacturing process, better formulation selection, and decreases risk associated with product handling in the clinic or during shipment.

METHODS

Reversed-phase chromatographic peak splitting was observed when a mAb was exposed to elevated temperatures. Multiple LC-MS based methods were applied to identify the reason for peak splitting. The approach involved the use of complementary HPLC columns, multiple enzymatic digestions and different MS/MS ion dissociation methods. In addition, mAb potency was measured by enzyme-linked immunosorbent assay (ELISA).

RESULTS

The split peaks had identical masses, and the root cause of the peak splitting was identified as isomerization of an aspartic acid located in the complementarity-determining region (CDR) of the light chain. And the early eluting and late eluting peaks were collected and performed enzymatic digestion to confirm the isoAsp enrichment in the early eluting peak. In addition, decreased potency was observed in the same heat-stressed sample, and the increased isoAsp levels in the CDR correlate well with a decrease of potency.

CONCLUSION

Liquid chromatography-mass spectrometry (LC-MS) has been utilized extensively to assess PQAs of biological therapeutics. In this study, a generalizable LC-MS-based approach was developed to enable identification and quantitation of the isoAsp-containing peptides.

Minerals and essential amino acids of bovine colostrum: Phenotypic variability and predictive ability of mid- and near-infrared spectroscopy

Colostrum quality and volume are fundamental for calves because it is the primary supplier of antibodies and the first source of energy, carbohydrates, lipids, proteins, minerals, and vitamins for the newborn. Assessing the detailed composition (i.e., AA and mineral content) of bovine colostrum (BC) on-line and at a reasonable cost would help dairy stakeholders such as farmers or veterinarians for precision feeding purposes and industries producing preparations containing BC such as foodstuff, supplements, and medicaments. In the present study we evaluated mid- (MIRS) and near-infrared spectroscopy (NIRS) prediction ability for AA and mineral composition of individual BC. Second, we the investigated the major factors affecting the phenotypic variability of such traits also evaluating the correlations with the Ig concentration. Results demonstrated that MIRS and NIRS were able to provide sufficiently accurate predictions for all the AA. The coefficient of determination in external validation (R2V) fell, in fact, within the range of 0.70 to 0.86, with the exception of Ile, His, and Met. Only some minerals reached a sufficient accuracy (i.e., Ca, P, S, and Mg; R2V ≥ 0.66) using MIRS, and also S (R2V = 0.87) using NIRS. Phenotypically, both parity and calving season affected the variability of these BC composition traits. Heifers' colostrum was the one with the greatest concentration of Ca and P, the 2 most abundant minerals. These minerals were however very low in cows calving in summer compared with the rest of the year. The pattern of AA across parities and calving season was not linear, likely because their variability was scarcely (or not) affected by these effects. Finally, samples characterized by high IgG concentration were those presenting on average greater concentration of AA. Findings suggest that infrared spectroscopy has the potential to be used to predict certain AA and minerals, outlining the possibility of implementing on-site analyses for the evaluation of the broad-sense BC quality.

Recent Advances in the Development of Monoclonal Antibodies and Next-Generation Antibodies

mAbs are highly indispensable tools for diagnostic, prophylactic, and therapeutic applications. The first technique, hybridoma technology, was based on fusion of B lymphocytes with myeloma cells, which resulted in generation of single mAbs against a specific Ag. Along with hybridoma technology, several novel and alternative methods have been developed to improve mAb generation, ranging from electrofusion to the discovery of completely novel technologies such as B cell immortalization; phage, yeast, bacterial, ribosome, and mammalian display systems; DNA/RNA encoded Abs; single B cell technology; transgenic animals; and artificial intelligence/machine learning. This commentary outlines the evolution, methodology, advantages, and limitations of various mAb production techniques. Furthermore, with the advent of next-generation Ab technologies such as single-chain variable fragments, nanobodies, bispecific Abs, Fc-engineered Abs, Ab biosimilars, Ab mimetics, and Ab-drug conjugates, the healthcare and pharmaceutical sectors have become resourceful to develop highly specific mAb treatments against various diseases such as cancer and autoimmune and infectious diseases.

PEP-Patch: Electrostatics in Protein-Protein Recognition, Specificity, and Antibody Developability

The electrostatic properties of proteins arise from the number and distribution of polar and charged residues. Electrostatic interactions in proteins play a critical role in numerous processes such as molecular recognition, protein solubility, viscosity, and antibody developability. Thus, characterizing and quantifying electrostatic properties of a protein are prerequisites for understanding these processes. Here, we present PEP-Patch, a tool to visualize and quantify the electrostatic potential on the protein surface in terms of surface patches, denoting separated areas of the surface with a common physical property. We highlight its applicability to elucidate protease substrate specificity and antibody-antigen recognition and predict heparin column retention times of antibodies as an indicator of pharmacokinetics.

Evaluation of the Ability of AlphaFold to Predict the Three-Dimensional Structures of Antibodies and Epitopes

Being able to accurately predict the three-dimensional structure of an Ab can facilitate Ab characterization and epitope prediction, with important diagnostic and clinical implications. In this study, we evaluated the ability of AlphaFold to predict the structures of 222 recently published, high-resolution Fab H and L chain structures of Abs from different species directed against different Ags. We show that although the overall Ab prediction quality is in line with the results of CASP14, regions such as the complementarity-determining regions (CDRs) of the H chain, which are prone to higher variation, are predicted less accurately. Moreover, we discovered that AlphaFold mispredicts the bending angles between the variable and constant domains. To evaluate the ability of AlphaFold to model Ab-Ag interactions based only on sequence, we used AlphaFold-Multimer in combination with ZDOCK to predict the structures of 26 known Ab-Ag complexes. ZDOCK, which was applied on bound components of both the Ab and the Ag, succeeded in assembling 11 complexes, whereas AlphaFold succeeded in predicting only 2 of 26 models, with significant deviations in the docking contacts predicted in the rest of the molecules. Within the 11 complexes that were successfully predicted by ZDOCK, 9 involved short-peptide Ags (18-mer or less), whereas only 2 were complexes of Ab with a full-length protein. Docking of modeled unbound Ab and Ag was unsuccessful. In summary, our study provides important information about the abilities and limitations of using AlphaFold to predict Ab-Ag interactions and suggests areas for possible improvement.

Long-term Prophylaxis Against Aerosolized Marburg Virus in Nonhuman Primates With an Afucosylated Monoclonal Antibody

Marburg virus (MARV) causes a hemorrhagic fever disease in human and nonhuman primates with high levels of morbidity and mortality. Concerns about weaponization of aerosolized MARV have spurred the development of nonhuman primate (NHP) models of aerosol exposure. To address the potential threat of aerosol exposure, a monoclonal antibody that binds MARV glycoprotein was tested, MR186YTE, for its efficacy as a prophylactic. MR186YTE was administered intramuscularly to NHPs at 15 or 5 mg/kg 1 month prior to MARV aerosol challenge. Seventy-five percent (3/4) of the 15 mg/kg dose group and 50% (2/4) of the 5 mg/kg dose group survived. Serum analyses showed that the NHP dosed with 15 mg/kg that succumbed to infection developed an antidrug antibody response and therefore had no detectable MR186YTE at the time of challenge. These results suggest that intramuscular dosing of mAbs may be a clinically useful prophylaxis for MARV aerosol exposure.

Identifying promising sequences for protein engineering using a deep transformer protein language model

Protein engineers aim to discover and design novel sequences with targeted, desirable properties. Given the near limitless size of the protein sequence landscape, it is no surprise that these desirable sequences are often a relative rarity. This makes identifying such sequences a costly and time-consuming endeavor. In this work, we show how to use a deep transformer protein language model to identify sequences that have the most promise. Specifically, we use the model's self-attention map to calculate a Promise Score that weights the relative importance of a given sequence according to predicted interactions with a specified binding partner. This Promise Score can then be used to identify strong binders worthy of further study and experimentation. We use the Promise Score within two protein engineering contexts-Nanobody (Nb) discovery and protein optimization. With Nb discovery, we show how the Promise Score provides an effective way to select lead sequences from Nb repertoires. With protein optimization, we show how to use the Promise Score to select site-specific mutagenesis experiments that identify a high percentage of improved sequences. In both cases, we also show how the self-attention map used to calculate the Promise Score can indicate which regions of a protein are involved in intermolecular interactions that drive the targeted property. Finally, we describe how to fine-tune the transformer protein language model to learn a predictive model for the targeted property, and discuss the capabilities and limitations of fine-tuning with and without knowledge transfer within the context of protein engineering.

Quenchbodies That Enable One-Pot Detection of Antigens: A Structural Perspective

Quenchbody (Q-body) is a unique, reagentless, fluorescent antibody whose fluorescent intensity increases in an antigen-concentration-dependent manner. Q-body-based homogeneous immunoassay is superior to conventional immunoassays as it does not require multiple immobilization, reaction, and washing steps. In fact, simply mixing the Q-body and the sample containing the antigen enables the detection of the target antigen. To date, various Q-bodies have been developed to detect biomarkers of interest, including haptens, peptides, proteins, and cells. This review sought to describe the principle of Q-body-based immunoassay and the use of Q-body for various immunoassays. In particular, the Q-bodies were classified from a structural perspective to provide useful information for designing Q-bodies with an appropriate objective.

A photoaffinity glycan-labeling approach to investigate immunoglobulin glycan-binding partners

Glycans play a pivotal role in biology. However, because of the low-affinity of glycan-protein interactions, many interaction pairs remain unknown. Two important glycoproteins involved in B-cell biology are the B-cell receptor and its secreted counterpart, antibodies. It has been indicated that glycans expressed by these B-cell-specific molecules can modulate immune activation via glycan-binding proteins. In several autoimmune diseases, an increased prevalence of variable domain glycosylation of IgG autoantibodies has been observed. Especially, the hallmarking autoantibodies in rheumatoid arthritis, anti-citrullinated protein antibodies, carry a substantial amount of variable domain glycans. The variable domain glycans expressed by these autoantibodies are N-linked, complex-type, and α2-6 sialylated, and B-cell receptors carrying variable domain glycans have been hypothesized to promote selection of autoreactive B cells via interactions with glycan-binding proteins. Here, we use the anti-citrullinated protein antibody response as a prototype to study potential in solution and in situ B-cell receptor-variable domain glycan interactors. We employed SiaDAz, a UV-activatable sialic acid analog carrying a diazirine moiety that can form covalent bonds with proximal glycan-binding proteins. We show, using oligosaccharide engineering, that SiaDAz can be readily incorporated into variable domain glycans of both antibodies and B-cell receptors. Our data show that antibody variable domain glycans are able to interact with inhibitory receptor, CD22. Interestingly, although we did not detect this interaction on the cell surface, we captured CD79 β glycan-B-cell receptor interactions. These results show the utility of combining photoaffinity labeling and oligosaccharide engineering for identifying antibody and B-cell receptor interactions and indicate that variable domain glycans appear not to be lectin cis ligands in our tested conditions.

Effects of glycans and hinge on dynamics in the IgG1 Fc

The crystallizable fragment (Fc) domain of immunoglobulin subclass IgG1 antibodies is engineered for a wide variety of pharmaceutical applications. Two important structural variables in Fc constructs are the hinge region connecting the Fc to the antigen binding fragments (Fab) and the glycans present in various glycoforms. These components affect receptor binding interactions that mediate immune activation. To design new antibody drugs, a robust in silico method for linking stability to structural changes is necessary. In this work, all-atom simulations were used to compare the dynamic behavior of the four structural variants arising from presence or absence of the hinge and glycans. We expressed the simplest of these constructs, the 'minimal Fc' with no hinge and no glycans, in Escherichia coli and report its crystal structure. The 'maximal Fc' that includes full hinge and G0F/G1F glycans is based on a previously reported structure, Protein Data Bank (PDB) ID: 5VGP. These, along with two intermediate structures (with only the glycans or with only the hinge) were used to independently measure the stability effects of the two structural variables using umbrella sampling simulations. Principal component analysis (PCA) was used to determine free energy effects along the Fc's dominant mode of motion. This work provides a comprehensive picture of the effects of hinge and glycans on Fc dynamics and stability.Communicated by Ramaswamy H. Sarma.

Structure and Dynamics Guiding Design of Antibody Therapeutics and Vaccines

Antibodies and other new antibody-like formats have emerged as one of the most rapidly growing classes of biotherapeutic proteins. Understanding the structural features that drive antibody function and, consequently, their molecular recognition is critical for engineering antibodies. Here, we present the structural architecture of conventional IgG antibodies alongside other formats. We emphasize the importance of considering antibodies as conformational ensembles in solution instead of focusing on single-static structures because their functions and properties are strongly governed by their dynamic nature. Thus, in this review, we provide an overview of the unique structural and dynamic characteristics of antibodies with respect to their antigen recognition, biophysical properties, and effector functions. We highlight the numerous technical advances in antibody structure prediction and design, enabled by the vast number of experimentally determined high-quality structures recorded with cryo-EM, NMR, and X-ray crystallography. Lastly, we assess antibody and vaccine design strategies in the context of structure and dynamics.

Emerging Applications of Nanobiosensors in Pathogen Detection in Water and Food

Food and waterborne illnesses are still a major concern in health and food safety areas. Every year, almost 0.42 million and 2.2 million deaths related to food and waterborne illness are reported worldwide, respectively. In foodborne pathogens, bacteria such as Salmonella, Shiga-toxin producer Escherichia coli, Campylobacter, and Listeria monocytogenes are considered to be high-concern pathogens. High-concern waterborne pathogens are Vibrio cholerae, leptospirosis, Schistosoma mansoni, and Schistosima japonicum, among others. Despite the major efforts of food and water quality control to monitor the presence of these pathogens of concern in these kinds of sources, foodborne and waterborne illness occurrence is still high globally. For these reasons, the development of novel and faster pathogen-detection methods applicable to real-time surveillance strategies are required. Methods based on biosensor devices have emerged as novel tools for faster detection of food and water pathogens, in contrast to traditional methods that are usually time-consuming and are unsuitable for large-scale monitoring. Biosensor devices can be summarized as devices that use biochemical reactions with a biorecognition section (isolated enzymes, antibodies, tissues, genetic materials, or aptamers) to detect pathogens. In most cases, biosensors are based on the correlation of electrical, thermal, or optical signals in the presence of pathogen biomarkers. The application of nano and molecular technologies allows the identification of pathogens in a faster and high-sensibility manner, at extremely low-pathogen concentrations. In fact, the integration of gold, silver, iron, and magnetic nanoparticles (NP) in biosensors has demonstrated an improvement in their detection functionality. The present review summarizes the principal application of nanomaterials and biosensor-based devices for the detection of pathogens in food and water samples. Additionally, it highlights the improvement of biosensor devices through nanomaterials. Nanomaterials offer unique advantages for pathogen detection. The nanoscale and high specific surface area allows for more effective interaction with pathogenic agents, enhancing the sensitivity and selectivity of the biosensors. Finally, biosensors' capability to functionalize with specific molecules such as antibodies or nucleic acids facilitates the specific detection of the target pathogens.

Studying Intermolecular Interactions in an Antibody-Drug Conjugate Through Chemical Screening and Computational Modeling

Antibody-drug conjugates (ADCs) combine the selectivity of antibodies with the cytotoxicity of drug payloads to yield highly targeted and potent therapeutics. Owing to the need to chemically modify residues for attachment of the payload and their more complex structure compared to either component alone, ADCs can present additional challenges related to stability of the final drug product. Here, we report for the first time the use of high-throughput experimental screens and computational techniques to tune the conformational and colloidal behavior of a monomethyl auristatin F-based ADC. The ADC, which exhibits high opalescence with strongly attractive protein-protein interactions, is transformed into a more stable structure by experimentally traversing a library of more than ∼100 formulations. A significant reduction in turbidity and increase in diffusion interaction parameter is observed by varying properties such as pH and ionic strength. Computational modeling rationalized these changes and pointed to the presence of attractive electrostatic interactions between ADC molecules facilitated by the drug payload and histidine residues. Taken together, the experimental and computational work presented provides a general roadmap of studies to perform during ADC development to find stable formulations, while the mechanistic learnings can be applied towards the design and stabilization of other IgG1-based ADCs.

Progress on Phage Display Technology: Tailoring Antibodies for Cancer Immunotherapy

The search for innovative anti-cancer drugs remains a challenge. Over the past three decades, antibodies have emerged as an essential asset in successful cancer therapy. The major obstacle in developing anti-cancer antibodies is the need for non-immunogenic antibodies against human antigens. This unique requirement highlights a disadvantage to using traditional hybridoma technology and thus demands alternative approaches, such as humanizing murine monoclonal antibodies. To overcome these hurdles, human monoclonal antibodies can be obtained directly from Phage Display libraries, a groundbreaking tool for antibody selection. These libraries consist of genetically engineered viruses, or phages, which can exhibit antibody fragments, such as scFv or Fab on their capsid. This innovation allows the in vitro selection of novel molecules directed towards cancer antigens. As foreseen when Phage Display was first described, nowadays, several Phage Display-derived antibodies have entered clinical settings or are undergoing clinical evaluation. This comprehensive review unveils the remarkable progress in this field and the possibilities of using clever strategies for phage selection and tailoring the refinement of antibodies aimed at increasingly specific targets. Moreover, the use of selected antibodies in cutting-edge formats is discussed, such as CAR (chimeric antigen receptor) in CAR T-cell therapy or ADC (antibody drug conjugate), amplifying the spectrum of potential therapeutic avenues.

AFsample: improving multimer prediction with AlphaFold using massive sampling

SUMMARY

The AlphaFold2 neural network model has revolutionized structural biology with unprecedented performance. We demonstrate that by stochastically perturbing the neural network by enabling dropout at inference combined with massive sampling, it is possible to improve the quality of the generated models. We generated ∼6000 models per target compared with 25 default for AlphaFold-Multimer, with v1 and v2 multimer network models, with and without templates, and increased the number of recycles within the network. The method was benchmarked in CASP15, and compared with AlphaFold-Multimer v2 it improved the average DockQ from 0.41 to 0.55 using identical input and was ranked at the very top in the protein assembly category when compared with all other groups participating in CASP15. The simplicity of the method should facilitate the adaptation by the field, and the method should be useful for anyone interested in modeling multimeric structures, alternate conformations, or flexible structures.

AVAILABILITY AND IMPLEMENTATION

AFsample is available online at http://wallnerlab.org/AFsample.

State-of-the-Art Approaches to Heterologous Expression of Bispecific Antibodies Targeting Solid Tumors

Bispecific antibodies (bsAbs) are some of the most promising biotherapeutics due to the versatility provided by their structure and functional features. bsAbs simultaneously bind two antigens or two epitopes on the same antigen. Moreover, they are capable of directing immune effector cells to cancer cells and delivering various compounds (radionuclides, toxins, and immunologic agents) to the target cells, thus offering a broad spectrum of clinical applications. Current review is focused on the technologies used in bsAb engineering, current progress and prospects of these antibodies, and selection of various heterologous expression systems for bsAb production. We also discuss the platforms development of bsAbs for the therapy of solid tumors.

Design of an alternate antibody fragment format that can be produced in the cytoplasm of Escherichia coli

With increased accessibility and tissue penetration, smaller antibody formats such as antibody fragments (Fab) and single chain variable fragments (scFv) show potential as effective and low-cost choices to full-length antibodies. These formats derived from the modular architecture of antibodies could prove to be game changers for certain therapeutic and diagnostic applications. Microbial hosts have shown tremendous promise as production hosts for antibody fragment formats. However, low target protein yields coupled with the complexity of protein folding result in production limitations. Here, we report an alternative antibody fragment format 'FabH3' designed to overcome some key bottlenecks associated with the folding and production of Fabs. The FabH3 molecule is based on the Fab format with the constant domains replaced by engineered immunoglobulin G1 (IgG1) CH3 domains capable of heterodimerization based on the electrostatic steering approach. We show that this alternative antibody fragment format can be efficiently produced in the cytoplasm of E. coli using the catalyzed disulfide-bond formation system (CyDisCo) in a natively folded state with higher soluble yields than its Fab counterpart and a comparable binding affinity against the target antigen.

Design of Tetra-Peptide Ligands of Antibody Fc Regions Using In Silico Combinatorial Library Screening

Peptides, or short chains of amino-acid residues, are becoming increasingly important as active ingredients of drugs and as crucial probes and/or tools in medical, biotechnological, and pharmaceutical research. Situated at the interface between small molecules and larger macromolecular systems, they pose a difficult challenge for computational methods. We report an in silico peptide library generation and prioritization workflow using CmDock for identifying tetrapeptide ligands that bind to Fc regions of antibodies that is analogous to known in vitro recombinant peptide libraries' display and expression systems. The results of our in silico study are in accordance with existing scientific literature on in vitro peptides that bind to antibody Fc regions. In addition, we postulate an evolving in silico library design workflow that will help circumvent the combinatorial problem of in vitro comprehensive peptide libraries by focusing on peptide subunits that exhibit favorable interaction profiles in initial in silico peptide generation and testing.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

FcRN receptor antagonists in the management of myasthenia gravis

Myasthenia gravis (MG) is an autoimmune disorder characterized by autoantibodies specifically directed against proteins located within the postsynaptic membrane of the neuromuscular junction. These pathogenic autoantibodies can be reduced by therapies such as plasma exchange, IVIG infusions and other immunosuppressive agents. However, there are significant side effects associated with most of these therapies. Since there is a better understanding of the molecular structure and the biological properties of the neonatal Fc receptors (FcRn), it possesses an attractive profile in treating myasthenia gravis. FcRn receptors prevent the catabolism of IgG by impeding their lysosomal degradation and facilitating their extracellular release at physiological pH, consequently extending the IgG half-life. Thus, the catabolism of IgG can be enhanced by blocking the FcRn, leading to outcomes similar to those achieved through plasma exchange with no significant safety concerns. The available studies suggest that FcRn holds promise as a versatile therapeutic intervention, capable of delivering beneficial outcomes in patients with distinct characteristics and varying degrees of MG severity. Efgartigimod is already approved for the treatment of generalized MG, rozanolixizumab is under review by health authorities, and phase 3 trials of nipocalimab and batoclimab are underway. Here, we will review the available data on FcRn therapeutic agents in the management of MG.

Defining and Studying B Cell Receptor and TCR Interactions

BCRs (Abs) and TCRs (or adaptive immune receptors [AIRs]) are the means by which the adaptive immune system recognizes foreign and self-antigens, playing an integral part in host defense, as well as the emergence of autoimmunity. Importantly, the interaction between AIRs and their cognate Ags defies a simple key-in-lock paradigm and is instead a complex many-to-many mapping between an individual's massively diverse AIR repertoire, and a similarly diverse antigenic space. Understanding how adaptive immunity balances specificity with epitopic coverage is a key challenge for the field, and terms such as broad specificity, cross-reactivity, and polyreactivity remain ill-defined and are used inconsistently. In this Immunology Notes and Resources article, a group of experimental, structural, and computational immunologists define commonly used terms associated with AIR binding, describe methodologies to study these binding modes, as well as highlight the implications of these different binding modes for therapeutic design.

Stability of Monoclonal Antibodies as Solid Formulation for Auto-Injectors: A Pilot Study

Drug adherence is a significant medical issue, often responsible for sub-optimal outcomes during the treatment of chronic diseases such as rheumatoid or psoriatic arthritis. Monoclonal antibodies (which are exclusively given parenterally) have been proven to be an effective treatment in these cases. The use of auto-injectors is an effective strategy to improve drug adherence in parenteral treatments since these pen-like devices offer less discomfort and increased user-friendliness over conventional syringe-based delivery. This study aims to investigate the feasibility of including a monoclonal antibody as a solid formulation inside an auto-injector pen. Specifically, the objective was to evaluate the drug stability after a concentration (to reduce the amount of solvent and space needed) and freeze-drying procedure. A preliminary screening of excipients to improve stability was also performed. The nano-DSC results showed that mannitol improved the stability of the concentrated, freeze-dried antibody in comparison to its counterpart without it. However, a small instability of the CH2 domain was still found for mannitol samples, which will warrant further investigation. The present results serve as a stepping stone towards advancing future drug delivery systems that will ultimately improve the patient experience and associated drug adherence.

In-depth size and charge variants characterization of monoclonal antibody with native mass spectrometry

Although the reversed-phase liquid chromatography (RPLC) is the most used separation front for mass spectrometry, many other separation modes are critical for enabling characterization of the protein therapeutics. Specifically, chromatographic separations under native conditions, such as those based on size exclusion chromatography (SEC) and ion-exchange chromatography (IEX), are used for characterizing important biophysical properties of protein variants in drug substance and drug product. Because most native state separation modes use non-volatile buffers with high salt concentration, optical detection has been traditionally used. However, there is an increasing need to understand and identify the optical underlying peaks by mass spectrometry for structure elucidation. For size variant separation by SEC, the native MS helps to understand the nature of the high molecular weight species, as well as clipping sites for low molecular weight fragments. For charge variant separation by IEX, native MS can reveal the post-translational modifications or other important factors contributing to charge heterogeneity at the intact level. Here, we demonstrate the power of native MS by direct coupling of SEC and IEX eluent to a time-of-flight mass spectrometer to characterize bevacizumab and NISTmAb. Our studies exemplify the effectiveness of native SEC-MS for characterizing bevacizumab's high molecular weight species at less than 0.3% (based on SEC/UV peak area%) and analyzing the fragment pathway with single amino acid difference for its low molecular weight species at less than 0.05%. Good IEX charge variant separation was obtained with consistent UV and MS profiles. The identity of separated acidic and basic variants were elucidated by native MS at intact level. We successfully differentiated several charge variants including glycoform variants that have not been reported before. In addition, native MS allowed identification of higher molecular weight species as late eluted variants. Overall, the SEC and IEX separation combined with high resolution and high sensitivity native MS, which is significantly different from the traditional RPLC-MS workflows, can be an effective tool that offers valuable insights for us to understand protein therapeutics at native state.

KA-Search, a method for rapid and exhaustive sequence identity search of known antibodies

Antibodies with similar amino acid sequences, especially across their complementarity-determining regions, often share properties. Finding that an antibody of interest has a similar sequence to naturally expressed antibodies in healthy or diseased repertoires is a powerful approach for the prediction of antibody properties, such as immunogenicity or antigen specificity. However, as the number of available antibody sequences is now in the billions and continuing to grow, repertoire mining for similar sequences has become increasingly computationally expensive. Existing approaches are limited by either being low-throughput, non-exhaustive, not antibody specific, or only searching against entire chain sequences. Therefore, there is a need for a specialized tool, optimized for a rapid and exhaustive search of any antibody region against all known antibodies, to better utilize the full breadth of available repertoire sequences. We introduce Known Antibody Search (KA-Search), a tool that allows for the rapid search of billions of antibody variable domains by amino acid sequence identity across either the variable domain, the complementarity-determining regions, or a user defined antibody region. We show KA-Search in operation on the [Formula: see text]2.4 billion antibody sequences available in the OAS database. KA-Search can be used to find the most similar sequences from OAS within 30 minutes and a representative subset of 10 million sequences in less than 9 seconds. We give examples of how KA-Search can be used to obtain new insights about an antibody of interest. KA-Search is freely available at https://github.com/oxpig/kasearch .

Current status and future expectations of nanobodies in oncology trials

INTRODUCTION

Monoclonal antibodies have revolutionized personalized medicine for cancer in recent decades. Despite their broad application in oncology, their large size and complexity may interfere with successful tumor targeting for certain applications of cancer diagnosis and therapy. Nanobodies have unique structural and pharmacological features compared to monoclonal antibodies and have successfully been used as complementary anti-cancer diagnostic and/or therapeutic tools.

AREAS COVERED

Here, an overview is given of the nanobody-based diagnostics and therapeutics that have been or are currently being tested in oncological clinical trials. Furthermore, preclinical developments, which are likely to be translated into the clinic in the near future, are highlighted.

EXPERT OPINION

Overall, the presented studies show the application potential of nanobodies in the field of oncology, making it likely that more nanobodies will be clinically approved in the upcoming future.

Detection of Antibody-Dependent Cell-Mediated Cytotoxicity-Supporting Antibodies by NK-92-CD16A Cell Externalization of CD107a: Recognition of Antibody Afucosylation and Assay Optimization

Antibody-dependent cell-mediated cytotoxicity (ADCC) by natural killer (NK) lymphocytes eliminates cells infected with viruses. Anti-viral ADCC requires three components: (1) antibody; (2) effector lymphocytes with the Fc-IgG receptor CD16A; and (3) viral proteins in infected cell membranes. Fc-afucosylated antibodies bind with greater affinity to CD16A than fucosylated antibodies; individuals' variation in afucosylation contributes to differences in ADCC. Current assays for afucosylated antibodies involve expensive methods. We report an improved bioassay for antibodies that supports ADCC, which encompasses afucosylation. This assay utilizes the externalization of CD107a by NK-92-CD16A cells after antibody recognition. We used anti-CD20 monoclonal antibodies, GA101 WT or glycoengineered (GE), 10% or ~50% afucosylated, and CD20-positive Raji target cells. CD107a increased detection 7-fold compared to flow cytometry to detect Raji-bound antibodies. WT and GE antibody effective concentrations (EC50s) for CD107a externalization differed by 20-fold, with afucosylated GA101-GE more detectable. The EC50s for CD107a externalization vs. 51Cr cell death were similar for NK-92-CD16A and blood NK cells. Notably, the % CD107a-positive cells were negatively correlated with dead Raji cells and were nearly undetectable at high NK:Raji ratios required for cytotoxicity. This bioassay is very sensitive and adaptable to assess anti-viral antibodies but unsuitable as a surrogate assay to monitor cell death after ADCC.

BNT162b2 Vaccination after SARS-CoV-2 Infection Changes the Dynamics of Total and Neutralizing Antibodies against SARS-CoV-2: A 6-Month Prospective Cohort Study

This study aimed to analyze the dynamics, duration, and production of total and neutralizing antibodies induced by the BNT162b2 vaccine and the possible effect of gender and prior SARS-CoV-2 infection on the generation of these antibodies. Total antibodies were quantified via chemiluminescent microparticle immunoassay (CMIA), and neutralizing antibodies were quantified using the cPass SARS-CoV-2 kit. Individuals with a history of COVID-19 produced twice as many antibodies than vaccinated individuals without prior SARS-CoV-2 infection, with an exponential increase observed in just six days. In those without a COVID-19 history, similar antibody production was reached 45 days after vaccination. Although total antibodies decline considerably in the first two months, the neutralizing antibodies and their inhibitory capacity (>96%) persist up to 6 months after the first dose. There was a tendency for higher total antibodies in women than men, but not at the inhibition capacity level. We suggest that the decline in total antibodies should not be considered as an indicator of loss of protective immunity because most antibodies decay two months after the second dose, but neutralizing antibodies remain constant for at least six months. Therefore, these latter antibodies could be better indicators for estimating the time-dependent vaccine efficacy.

Identification of Small Molecules Affecting the Secretion of Therapeutic Antibodies with the Retention Using Selective Hook (RUSH) System

Unlocking cell secretion capacity is of paramount interest for the pharmaceutical industry focused on biologics. Here, we leveraged retention using a selective hook (RUSH) system for the identification of human osteosarcoma U2OS cell secretion modulators, through automated, high-throughput screening of small compound libraries. We created a U2OS cell line which co-expresses a variant of streptavidin addressed to the lumen-facing membrane of the endoplasmic reticulum (ER) and a recombinant anti-PD-L1 antibody. The heavy chain of the antibody was modified at its C-terminus, to which a furin cleavage site, a green fluorescent protein (GFP), and a streptavidin binding peptide (SBP) were added. We show that the U2OS cell line stably expresses the streptavidin hook and the recombinant antibody bait, which is retained in the ER through the streptavidin-SBP interaction. We further document that the addition of biotin to the culture medium triggers the antibody release from the ER, its trafficking through the Golgi where the GFP-SBP moiety is clipped off, and eventually its release in the extra cellular space, with specific antigen-binding properties. The use of this clone in screening campaigns led to the identification of lycorine as a secretion enhancer, and nigericin and tyrphostin AG-879 as secretion inhibitors. Altogether, our data support the utility of this approach for the identification of agents that could be used to improve recombinant production yields and also for a better understanding of the regulatory mechanism at work in the conventional secretion pathway.

Molecular Farming of Pembrolizumab and Nivolumab

Immune checkpoint inhibitors (ICIs) are a class of immunotherapy agents capable of alleviating the immunosuppressive effects exerted by tumorigenic cells. The programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) immune checkpoint is one of the most ubiquitous checkpoints utilized by tumorigenic cells for immune evasion by inducing apoptosis and inhibiting the proliferation and cytokine production of T lymphocytes. Currently, the most frequently used ICIs targeting the PD-1/PD-L1 checkpoint include monoclonal antibodies (mAbs) pembrolizumab and nivolumab that bind to PD-1 on T lymphocytes and inhibit interaction with PD-L1 on tumorigenic cells. However, pembrolizumab and nivolumab are costly, and thus their accessibility is limited in low- and middle-income countries (LMICs). Therefore, it is essential to develop novel biomanufacturing platforms capable of reducing the cost of these two therapies. Molecular farming is one such platform utilizing plants for mAb production, and it has been demonstrated to be a rapid, low-cost, and scalable platform that can be potentially implemented in LMICs to diminish the exorbitant prices, ultimately leading to a significant reduction in cancer-related mortalities within these countries.

Optimization of the Imaged cIEF Method for Monitoring the Charge Heterogeneity of Antibody-Maytansine Conjugate

The aim of this study was to develop a whole-column imaging-detection capillary isoelectric focusing (icIEF) method for the analytical characterization of charge heterogeneity of a novel humanized anti-EphA2 antibody conjugated to a maytansine derivative. In addition to focusing time, sample composition was optimized: pH range, percent of carrier ampholytes, conjugated antibody concentration, and urea concentration. A good separation of charge isoforms was obtained with 4% carrier ampholytes of a large (3-10) and narrow pH range (8-10.5) (1 : 1 ratio), conjugated antibody concentration (0.3-1 mg/ml) with a good linearity (R²: 0.9905), 2 M of urea concentration, and 12 minute for focusing. The optimized icIEF method demonstrated a good interday repeatability with RSD values: <1% (pI), <8% (% peak area), and 7% (total peak areas). The optimized icIEF was useful as an analytical characterization tool to assess the charged isoform profile of a discovery batch of the studied maytansinoid-antibody conjugate in comparison to its naked antibody. It exhibited a large pI range (7.5-9.0), while its naked antibody showed a narrow pI range (8.9-9.0). In the discovery batch of maytansinoid-antibody conjugate, 2% of charge isoforms had the same pI as the pI of naked antibody isoforms.

Host-microbe interaction and pathogen exclusion mediated by an aggregation-prone surface layer protein of Lactobacillus helveticus

Probiotic surface layer proteins (Slps) have multiple functions and bacterial adhesion to host cells is one of them. The precise role of Slps in cellular adhesion is not well understood due to its low native protein yield and self-aggregative nature. Here, we report the recombinant expression and purification of biologically active Slp of Lactobacillus helveticus NCDC 288 (SlpH) in high yield. SlpH is a highly basic protein (pI = 9.4), having a molecular weight of 45 kDa. Circular Dichroism showed a prevalence of beta-strands in SlpH structure and resistance to low pH. SlpH showed binding to human intestinal tissue, enteric Caco-2 cell line, and porcine gastric mucin, but not with fibronectin, collagen type IV and laminin. SlpH inhibited the binding of the enterotoxigenic E. coli by 70 % and 76 % and that of Salmonella Typhimurium SL1344 by 71 % and 75 % to enteric Caco-2 cell line in the exclusion and competition assays, respectively. The pathogen exclusion and competition activity and tolerance to harsh gastrointestinal conditions show the potential for developing SlpH as a prophylactic or therapeutic agent against enteric pathogens.

Molecular Complementarity of Proteomimetic Materials for Target-Specific Recognition and Recognition-Mediated Complex Functions

As biomolecules essential for sustaining life, proteins are generated from long chains of 20 different α-amino acids that are folded into unique 3D structures. In particular, many proteins have molecular recognition functions owing to their binding pockets, which have complementary shapes, charges, and polarities for specific targets, making these biopolymers unique and highly valuable for biomedical and biocatalytic applications. Based on the understanding of protein structures and microenvironments, molecular complementarity can be exhibited by synthesizable and modifiable materials. This has prompted researchers to explore the proteomimetic potentials of a diverse range of materials, including biologically available peptides and oligonucleotides, synthetic supramolecules, inorganic molecules, and related coordination networks. To fully resemble a protein, proteomimetic materials perform the molecular recognition to mediate complex molecular functions, such as allosteric regulation, signal transduction, enzymatic reactions, and stimuli-responsive motions; this can also expand the landscape of their potential bio-applications. This review focuses on the recognitive aspects of proteomimetic designs derived for individual materials and their conformations. Recent progress provides insights to help guide the development of advanced protein mimicry with material heterogeneity, design modularity, and tailored functionality. The perspectives and challenges of current proteomimetic designs and tools are also discussed in relation to future applications.

Toehold-Mediated Shape Transition of Nucleic Acid Nanoparticles

We introduce a toehold-mediated strand displacement strategy for regulated shape-switching of nucleic acid nanoparticles (NANPs) enabling their sequential transformation from triangular to hexagonal architectures at isothermal conditions. The successful shape transitions were confirmed by electrophoretic mobility shift assays, atomic force microscopy, and dynamic light scattering. Furthermore, implementation of split fluorogenic aptamers allowed for monitoring the individual transitions in real time. Three distinct RNA aptamers─malachite green (MG), broccoli, and mango─were embedded within NANPs as reporter domains to confirm shape transitions. While MG "lights up" within the square, pentagonal, and hexagonal constructs, the broccoli is activated only upon formation of pentagon and hexagon NANPs, and mango reports only the presence of hexagons. Moreover, the designed RNA fluorogenic platform can be employed to construct a logic gate that performs an AND operation with three single-stranded RNA inputs by implementing a non-sequential polygon transformation approach. Importantly, the polygonal scaffolds displayed promising potential as drug delivery agents and biosensors. All polygons exhibited effective cellular internalization followed by specific gene silencing when decorated with fluorophores and RNAi inducers. This work offers a new perspective for the design of toehold-mediated shape-switching nanodevices to activate different light-up aptamers for the development of biosensors, logic gates, and therapeutic devices in the nucleic acid nanotechnology.

Recent Progress of Rational Modified Nanocarriers for Cytosolic Protein Delivery

Therapeutic proteins garnered significant attention in the field of disease treatment. In comparison to small molecule drugs, protein therapies offer distinct advantages, including high potency, specificity, low toxicity, and reduced carcinogenicity, even at minimal concentrations. However, the full potential of protein therapy is limited by inherent challenges such as large molecular size, delicate tertiary structure, and poor membrane penetration, resulting in inefficient intracellular delivery into target cells. To address these challenges and enhance the clinical applications of protein therapies, various protein-loaded nanocarriers with tailored modifications were developed, including liposomes, exosomes, polymeric nanoparticles, and nanomotors. Despite these advancements, many of these strategies encounter significant issues such as entrapment within endosomes, leading to low therapeutic efficiency. In this review, we extensively discussed diverse strategies for the rational design of nanocarriers, aiming to overcome these limitations. Additionally, we presented a forward-looking viewpoint on the innovative generation of delivery systems specifically tailored for protein-based therapies. Our intention was to offer theoretical and technical support for the development and enhancement of nanocarriers capable of facilitating cytosolic protein delivery.

ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins

Immune receptor proteins play a key role in the immune system and have shown great promise as biotherapeutics. The structure of these proteins is critical for understanding their antigen binding properties. Here, we present ImmuneBuilder, a set of deep learning models trained to accurately predict the structure of antibodies (ABodyBuilder2), nanobodies (NanoBodyBuilder2) and T-Cell receptors (TCRBuilder2). We show that ImmuneBuilder generates structures with state of the art accuracy while being far faster than AlphaFold2. For example, on a benchmark of 34 recently solved antibodies, ABodyBuilder2 predicts CDR-H3 loops with an RMSD of 2.81Å, a 0.09Å improvement over AlphaFold-Multimer, while being over a hundred times faster. Similar results are also achieved for nanobodies, (NanoBodyBuilder2 predicts CDR-H3 loops with an average RMSD of 2.89Å, a 0.55Å improvement over AlphaFold2) and TCRs. By predicting an ensemble of structures, ImmuneBuilder also gives an error estimate for every residue in its final prediction. ImmuneBuilder is made freely available, both to download ( https://github.com/oxpig/ImmuneBuilder ) and to use via our webserver ( http://opig.stats.ox.ac.uk/webapps/newsabdab/sabpred ). We also make available structural models for ~150 thousand non-redundant paired antibody sequences ( https://doi.org/10.5281/zenodo.7258553 ).

Structural characterization of a pathogenic antibody underlying vaccine-induced immune thrombotic thrombocytopenia (VITT)

Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a rare but extremely dangerous side effect that has been reported for several adenoviral (Ad)-vectored COVID-19 vaccines. VITT pathology had been linked to production of antibodies that recognize platelet factor 4 (PF4), an endogenous chemokine. In this work we characterize anti-PF4 antibodies obtained from a VITT patient's blood. Intact-mass MS measurements indicate that a significant fraction of this ensemble is comprised of antibodies representing a limited number of clones. MS analysis of large antibody fragments (the light chain, as well as the Fc/2 and Fd fragments of the heavy chain) confirms the monoclonal nature of this component of the anti-PF4 antibodies repertoire, and reveals the presence of a fully mature complex biantennary N-glycan within its Fd segment. Peptide mapping using two complementary proteases and LC-MS/MS analysis were used to determine the amino acid sequence of the entire light chain and over 98% of the heavy chain (excluding a short N-terminal segment). The sequence analysis allows the monoclonal antibody to be assigned to IgG2 subclass and verify that the light chain belongs to the λ-type. Incorporation of enzymatic de- N -glycosylation into the peptide mapping routine allows the N -glycan in the Fab region of the antibody to be localized to the framework 3 region of the V H domain. This novel N -glycosylation site (absent in the germline sequence) is a result of a single mutation giving rise to an NDT motif in the antibody sequence. Peptide mapping also provides a wealth of information on lower-abundance proteolytic fragments derived from the polyclonal component of the anti-PF4 antibody ensemble, revealing the presence of all four subclasses (IgG1 through IgG4) and both types of the light chain (λ and κ). The structural information reported in this work will be indispensable for understanding the molecular mechanism of VITT pathogenesis.

Production of monoclonal antibodies for therapeutic purposes: A review

Monoclonal antibodies (mAbs) have been used in the development of immunotherapies that target a variety of diseases, such as cancer, autoimmune diseases, and even viral infections; they play a key role in immunization and are expected after vaccination. However, some conditions do not promote the development of neutralizing antibodies. Production and use of mAbs, generated in biofactories, represent vast potential as aids in immunological responses when the organism cannot produce them on their own, these convey unique specificity by recognizing and targeting specific antigen. Antibodies can be defined as heterotetrametric glycoproteins of symmetric nature, and they participate as effector proteins in humoral responses. Additionally, there are different types of mAbs (murine, chimeric, humanized, human, mAbs as Antibody-drug conjugates and bispecific mAbs) discussed in the present work. When these molecules are produced in vitro as mAbs, several common techniques, such as hybridomas or phage display are used. There are several preferred cell lines that function as biofactories, for the production of mAbs, the selection of which rely on the variation of adaptability, productivity and both phenotypic and genotypic shifts. After the cell expression systems and culture techniques are used, there are diverse specialized downstream processes to achieve desired yield and isolation as well as product quality and characterization. Novel perspectives regarding these protocols represent a potential improvement for mAbs high-scale production.

Uses and Challenges of Antiviral Polyclonal and Monoclonal Antibody Therapies

Viral diseases represent a major public health concerns and ever-present risks for developing into future pandemics. Antiviral antibody therapeutics, either alone or in combination with other therapies, emerged as valuable preventative and treatment options, including during global emergencies. Here we will discuss polyclonal and monoclonal antiviral antibody therapies, focusing on the unique biochemical and physiological properties that make them well-suited as therapeutic agents. We will describe the methods of antibody characterization and potency assessment throughout development, highlighting similarities and differences between polyclonal and monoclonal products as appropriate. In addition, we will consider the benefits and challenges of antiviral antibodies when used in combination with other antibodies or other types of antiviral therapeutics. Lastly, we will discuss novel approaches to the characterization and development of antiviral antibodies and identify areas that would benefit from additional research.

Screening Clones for Monoclonal Antibody Production Using Droplet Microfluidics Interfaced to Electrospray Ionization Mass Spectrometry

As one of the most critical steps in process development for protein therapeutics, clone selection and cell culture optimization require a large number of samples to be screened for high titer and desirable molecular profiles. Typical analytical techniques, such as chromatographic approaches, often take minutes per sample which are inefficient for large-scale screenings. Droplet microfluidics coupled to mass spectrometry (MS) represents an attractive approach due to its low volume requirements, high-throughput capabilities, label-free nature, and ability to handle complex mixtures. In this work, we coupled a modified protein cleanup protocol with a droplet-MS workflow for mAb titer screening to guide clone selection. With this droplet approach we achieved a throughput of 0.04 samples/s with an LoD of 0.15 mg/mL and an LoQ of 0.45 mg/mL. To test its performance in a real-world setting, this workflow was applied to a 35-clone screen, where the top 20% producing clones were identified. In addition, we coupled our sample cleanup protocol to a high-resolution MS and compared the glycan profiles of the high titer clones. This work demonstrates that droplet-MS provides a rapid way of clone screening and cell culture optimization based on titer and molecular structure of the expressed proteins. Future work is aimed at increasing the throughput and automation of this droplet-MS technique.

Fluorescence coupling strategies in fluorescence-activated droplet sorting (FADS) for ultrahigh-throughput screening of enzymes, metabolites, and antibodies

Fluorescence-activated droplet sorting (FADS) has emerged as a powerful tool for ultrahigh-throughput screening of enzymes, metabolites, and antibodies. Fluorescence coupling strategies (FCSs) are key to the development of new FADS methods through their coupling of analyte properties such as concentration, activities, and affinity with fluorescence signals. Over the last decade, a series of FCSs have been developed, greatly expanding applications of FADS. Here, we review recent advances in FCS for different analyte types, providing a critical comparison of the available FCSs and further classification into four categories according to their principles. We also summarize successful FADS applications employing FCSs in enzymes, metabolites, and antibodies. Further, we outline possible future developments in this area.

Pandemic preparedness strategies must go beyond vaccines

Monoclonal antibodies can fill a critical gap to help stop the next infectious disease outbreak from becoming the next pandemic.

IgG1 and IgG4 antibodies sample initial structure dependent local conformational states and exhibit non-identical Fab dynamics

We have investigated the dynamics of two [Formula: see text]-immunoglobulin molecules, IgG1 and IgG4, using long all atom molecular dynamics simulations. We first show that the de novo structures of IgG1 and IgG4 predicted using AlphaFold, with no interactions between the fragment crystallizable (Fc) domain and the antigen fragment binding domain (Fab), eventually relaxes to a state with persistent Fc-Fab interactions that mirrors experimentally resolved structures. We quantified the conformational space sampled by antibody trajectories spawned from six different initial structures and show that the individual trajectories only sample states bound by a local minimum and display very little mixing in their conformational states. Furthermore, the dynamics of the individual Fab domains are strongly dependent on the initial crystal structure and isotype. In all conditions, we observe non-identical dynamics between the Fab arms in an antibody. For a six-bead coarse grained model, we show that non-covalent Fc-Fab interactions can modulate the stiffnesses associated with Fc-Fab distances, angles, and dihedral angles by up to three orders of magnitude. Our results clearly illustrate the inherent complexities in studying antibody dynamics and highlight the need to include non-identical Fab dynamics as an inherent feature in computational models of therapeutic antibodies.

Structural mechanism of Fab domain dissociation as a measure of interface stability

Therapeutic antibodies should not only recognize antigens specifically, but also need to be free from developability issues, such as poor stability. Thus, the mechanistic understanding and characterization of stability are critical determinants for rational antibody design. In this study, we use molecular dynamics simulations to investigate the melting process of 16 antigen binding fragments (Fabs). We describe the Fab dissociation mechanisms, showing a separation in the V_H-V_L and in the C_H1-C_L domains. We found that the depths of the minima in the free energy curve, corresponding to the bound states, correlate with the experimentally determined melting temperatures. Additionally, we provide a detailed structural description of the dissociation mechanism and identify key interactions in the CDR loops and in the C_H1-C_L interface that contribute to stabilization. The dissociation of the V_H-V_L or C_H1-C_L domains can be represented by conformational changes in the bend angles between the domains. Our findings elucidate the melting process of antigen binding fragments and highlight critical residues in both the variable and constant domains, which are also strongly germline dependent. Thus, our proposed mechanisms have broad implications in the development and design of new and more stable antigen binding fragments.

Stimbiotics Supplementation Promotes Growth Performance by Improving Plasma Immunoglobulin and IGF-1 Levels and Regulating Gut Microbiota Composition in Weaned Piglets

This study was conducted to investigate the effects of dietary supplementation with stimbiotics (STB) on growth performance, diarrhoea incidence, plasma antioxidant capacity, immunoglobulin concentration and hormone levels, and faecal microorganisms in weaned piglets. Compared with the control (CT) group, the addition of STB improved the body weight (BW) of piglets on days 28 and 42 (p < 0.05) and increased daily weight gain and daily feed intake from days 14–28 and throughout the trial period (p < 0.05). Correspondingly, the plasma insulin-like growth factor 1 (IGF-1) level on day 42 was significantly improved by STB (p < 0.05). VistaPros (VP) group levels of immunoglobulin (Ig) A and G were significantly higher on days 14 and 42 (p < 0.05) than the CT group levels. In addition, the activity of plasma catalase tended to be increased on day 14 (p = 0.053) in the VP group, as for superoxide dismutase, glutathione peroxidase, and malondialdehyde, STB did not significantly affect their levels (p > 0.05). Moreover, dietary STB increased the relative abundance of beneficial bacteria, including norank_f_Muribaculaceae, Rikenellaceae_RC9_gut_group, Parabacteroides, and unclassified_f__Oscillospiraceae. In summary, STB improved the immunity and IGF-1 levels in the plasma of weaned piglets and consequently promoted the growth performance of weaned piglets.

Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction

Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.

Antibody engineering and its therapeutic applications

As a natural function, antibodies defend the host from infected cells and pathogens by recognizing their pathogenic determinants. Antibodies (Abs) gained wide acceptance with an enormous impact on human health and have predominantly captured the arena of bio-therapeutics and bio-diagnostics. The scope of Ab-based biologics is vast, and it is likely to solve many unmet clinical needs in future. The majority of attention is now devoted to developing innovative technologies for manufacturing and engineering Abs, better suited to satisfy human needs. The advent of Ab engineering technologies (AET) led to phenomenal developments leading to the generation of Abs-/Ab-derived molecules with desirable functional properties proportional to their expanding requirements. Evolution brought by AET, from the naturally occurring Ab forms to several advanced Ab formats and derivatives, was much needed as it is of great interest to the pharmaceutical industry. Thus, numerous advancements in AET have propelled success in therapeutic Ab development, along with the potential for ever-increasing improvements. Unique characteristics of Abs, such as its diversity, specificity, structural integrity and an array of possible applications, together inspire continuous innovation in the field. Overall, the AET could assist in conquer of several limitations of Abs in terms of their applicability in the field of therapeutics, diagnostics and research; AET has so far led to the production of next-generation Abs, which have revolutionized these arenas. Here in this review, we discuss the various distinguished engineering platforms for Ab development and the progress in modern therapeutics by the so-called "next-generation Abs."

Application Progress of the Single Domain Antibody in Medicine

The camelid-derived single chain antibody (sdAb), also termed VHH or nanobody, is a unique, functional heavy (H)-chain antibody (HCAb). In contrast to conventional antibodies, sdAb is a unique antibody fragment consisting of a heavy-chain variable domain. It lacks light chains and a first constant domain (CH1). With a small molecular weight of only 12~15 kDa, sdAb has a similar antigen-binding affinity to conventional Abs but a higher solubility, which exerts unique advantages for the recognition and binding of functional, versatile, target-specific antigen fragments. In recent decades, with their unique structural and functional features, nanobodies have been considered promising agents and alternatives to traditional monoclonal antibodies. As a new generation of nano-biological tools, natural and synthetic nanobodies have been used in many fields of biomedicine, including biomolecular materials, biological research, medical diagnosis and immune therapies. This article briefly overviews the biomolecular structure, biochemical properties, immune acquisition and phage library construction of nanobodies and comprehensively reviews their applications in medical research. It is expected that this review will provide a reference for the further exploration and unveiling of nanobody properties and function, as well as a bright future for the development of drugs and therapeutic methods based on nanobodies.

Structural Characterization of Nanobodies during Germline Maturation

Camelid heavy-chain antibody variable domains (VHH), nanobodies, are the smallest-known functional antibody fragments with high therapeutic potential. In this study, we investigate a VHH binding to hen egg-white lysozyme (HEL). We structurally and dynamically characterized the conformational diversity of four VHH variants to elucidate the antigen-binding process. For two of these antibodies, not only are the dissociation constants known, but also the experimentally determined crystal structures of the VHH in complex with HEL are available. We performed well-tempered metadynamics simulations in combination with molecular dynamics simulations to capture a broad conformational space and to reconstruct the thermodynamics and kinetics of conformational transitions in the antigen-binding site, the paratope. By kinetically characterizing the loop movements of the paratope, we found that, with an increase in affinity, the state populations shift towards the binding competent conformation. The contacts contributing to antigen binding, and those who contribute to the overall stability, show a clear trend towards less variable but more intense contacts. Additionally, these investigated nanobodies clearly follow the conformational selection paradigm, as the binding competent conformation pre-exists within the structural ensembles without the presence of the antigen.

Bioprocess Development and Characterization of a 13C-Labeled Hybrid Bispecific Antibody Produced in Escherichia coli

Monoclonal antibodies (mAbs) are highly efficacious therapeutics; however, due to their large, dynamic nature, structural perturbations and regional modifications are often difficult to study. Moreover, the homodimeric, symmetrical nature of mAbs makes it difficult to elucidate which heavy chain (HC)-light chain (LC) pairs are responsible for any structural changes, stability concerns, and/or site-specific modifications. Isotopic labeling is an attractive means for selectively incorporating atoms with known mass differences to enable identification/monitoring using techniques such as mass spectrometry (MS) and nuclear magnetic resonance (NMR). However, the isotopic incorporation of atoms into proteins is typically incomplete. Here we present a strategy for incorporating ¹³C-labeling of half antibodies using an Escherichia coli fermentation system. Unlike previous attempts to generate isotopically labeled mAbs, we provide an industry-relevant, high cell density process that yielded >99% ¹³C-incorporation using ¹³C-glucose and ¹³C-celtone. The isotopic incorporation was performed on a half antibody designed with knob-into-hole technology to enable assembly with its native (naturally abundant) counterpart to generate a hybrid bispecific (BsAb) molecule. This work is intended to provide a framework for producing full-length antibodies, of which half are isotopically labeled, in order to study the individual HC-LC pairs.