Animal Immunization, in Vitro Display Technologies, and Machine Learning for Antibody Discovery

A dual-targeting approach using a human bispecific antibody against the receptor-binding domain of the Middle East Respiratory Syndrome Coronavirus

The emergence of the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) has posed a significant global health concern due to its severe respiratory illness and high fatality rate. Currently, despite the potential for resurgence, there are no specific treatments for MERS-CoV, and only supportive care is available. Our study aimed to address this therapeutic gap by developing a potent neutralizing bispecific antibody (bsAb) against MERS-CoV. Initially, we isolated four human monoclonal antibodies (mAbs) that specifically target the MERS-CoV receptor-binding domain (RBD) using phage display technology and an established human antibody library. Among these four selected mAbs, our intensive in vitro functional analyses showed that the MERS-CoV RBD-specific mAb K111.3 exhibited the most potent neutralizing activity against MERS-CoV pseudoviral infection and the molecular interaction between MERS-CoV RBD and human dipeptidyl peptidase 4. Consequently, we engineered a novel bsAb, K207.C, by utilizing K111.3 as the IgG base and fusing it with the single-chain variable fragment of its non-competing pair, K111.1. This engineered bsAb showed significantly enhanced neutralization potential against MERS-CoV compared to its parental mAb. These findings suggest that K207.C may serve as a potential candidate for effective MERS-CoV neutralization, further highlighting the promise of the bsAb dual-targeting approach in MERS-CoV neutralization.

Advancements in ovarian cancer immunodiagnostics and therapeutics via phage display technology

Ovarian cancer, ranking as the seventh most prevalent malignancy among women globally, faces significant challenges in diagnosis and therapeutic intervention. The difficulties in early detection are amplified by the limitations and inefficacies inherent in current screening methodologies, highlighting a pressing need for more efficacious diagnostic and treatment strategies. Phage display technology emerges as a pivotal innovation in this context, utilizing extensive phage-peptide libraries to identify ligands with specificity for cancer cell markers, thus enabling precision-targeted therapeutic strategies. This technology promises a paradigm shift in ovarian cancer management, concentrating on targeted drug delivery systems to improve treatment accuracy and efficacy while minimizing adverse effects. Through a meticulous review, this paper evaluates the revolutionary potential of phage display in enhancing ovarian cancer therapy, representing a significant advancement in combating this challenging disease. Phage display technology is heralded as an essential instrument for developing effective immunodiagnostic and therapeutic approaches in ovarian cancer, facilitating early detection, precision-targeted medication, and the implementation of customized treatment plans.

In vivo neutralization of coral snake venoms with an oligoclonal nanobody mixture in a murine challenge model

Oligoclonal mixtures of broadly-neutralizing antibodies can neutralize complex compositions of similar and dissimilar antigens, making them versatile tools for the treatment of e.g., infectious diseases and animal envenomations. However, these biotherapeutics are complicated to develop due to their complex nature. In this work, we describe the application of various strategies for the discovery of cross-neutralizing nanobodies against key toxins in coral snake venoms using phage display technology. We prepare two oligoclonal mixtures of nanobodies and demonstrate their ability to neutralize the lethality induced by two North American coral snake venoms in mice, while individual nanobodies fail to do so. We thus show that an oligoclonal mixture of nanobodies can neutralize the lethality of venoms where the clinical syndrome is caused by more than one toxin family in a murine challenge model. The approaches described may find utility for the development of advanced biotherapeutics against snakebite envenomation and other pathologies where multi-epitope targeting is beneficial.

Trimeric Bet v 1-specific nanobodies cause strong suppression of IgE binding

Background

Around 20% of the population in Northern and Central Europe is affected by birch pollen allergy, with the major birch pollen allergen Bet v 1 as the main elicitor of allergic reactions. Together with its cross-reactive allergens from related trees and foods, Bet v 1 causes an impaired quality of life. Hence, new treatment strategies were elaborated, demonstrating the effectiveness of blocking IgG antibodies on Bet v 1-induced IgE-mediated reactions. A recent study provided evidence for the first time that Bet v 1-specific nanobodies reduce patients´ IgE binding to Bet v 1. In order to increase the potential to outcompete IgE recognition of Bet v 1 and to foster cross-reactivity and cross-protection, we developed Bet v 1-specific nanobody trimers and evaluated their capacity to suppress polyclonal IgE binding to corresponding allergens and allergen-induced basophil degranulation.

Methods

Nanobody trimers were engineered by adding isoleucine zippers, thus enabling trimeric formation. Trimers were analyzed for their cross-reactivity, binding kinetics to Bet v 1, and related allergens, and patients' IgE inhibition potential. Finally, their efficacy to prevent basophil degranulation was investigated.

Results

Trimers showed enhanced recognition of cross-reactive allergens and increased efficiency to reduce IgE-allergen binding compared to nanobody monomers. Furthermore, trimers displayed slow dissociation rates from allergens and suppressed allergen-induced mediator release.

Conclusion

We generated high-affine nanobody trimers that target Bet v 1 and related allergens. Trimers blocked IgE-allergen interaction by competing with IgE for allergen binding. They inhibited IgE-mediated release of biological mediators, demonstrating a promising potential to prevent allergic reactions caused by Bet v 1 and relatives.

Structural properties of immune complexes formed by viral antigens and specific antibodies shape the inflammatory response of macrophages

Data on the course of viral infections revealed severe inflammation as a consequence of antiviral immune response. Despite extensive research, there are insufficient data on the role of innate immune cells in promoting inflammation mediated by immune complexes (IC) of viral antigens and their specific antibodies. Recently, we demonstrated that antigens of human polyomaviruses (PyVs) induce an inflammatory response in macrophages. Here, we investigated macrophage activation by IC. We used primary murine macrophages as a cell model, virus-like particles (VLPs) of PyV capsid protein as antigens, and a collection of murine monoclonal antibodies (mAbs) of IgG1, IgG2a, IgG2b subclasses. The inflammatory response was investigated by analysing inflammatory chemokines and activation of NLRP3 inflammasome. We observed a diverse pattern of chemokine secretion in macrophages treated with different IC compared to VLPs alone. To link IC properties with cell activation status, we characterised the IC by advanced optical and acoustic techniques. Ellipsometry provided precise real-time kinetics of mAb-antigen interactions, while quartz crystal microbalance measurements showed changes in conformation and viscoelastic properties during IC formation. These results revealed differences in mAb-antigen interaction and mAb binding parameters of the investigated IC. We found that IC-mediated cell activation depends more on IC characteristics, including mAb affinity, than on mAb affinity for the activating Fc receptor. IC formed by the highest affinity mAb showed a significant enhancement of inflammasome activation. This may explain the hyperinflammation related to viral infection and vaccination. Our findings demonstrate that IC promote the viral antigen-induced inflammatory response depending on antibody properties.

Benchmarking antibody clustering methods using sequence, structural, and machine learning similarity measures for antibody discovery applications

Antibodies are proteins produced by our immune system that have been harnessed as biotherapeutics. The discovery of antibody-based therapeutics relies on analyzing large volumes of diverse sequences coming from phage display or animal immunizations. Identification of suitable therapeutic candidates is achieved by grouping the sequences by their similarity and subsequent selection of a diverse set of antibodies for further tests. Such groupings are typically created using sequence-similarity measures alone. Maximizing diversity in selected candidates is crucial to reducing the number of tests of molecules with near-identical properties. With the advances in structural modeling and machine learning, antibodies can now be grouped across other diversity dimensions, such as predicted paratopes or three-dimensional structures. Here we benchmarked antibody grouping methods using clonotype, sequence, paratope prediction, structure prediction, and embedding information. The results were benchmarked on two tasks: binder detection and epitope mapping. We demonstrate that on binder detection no method appears to outperform the others, while on epitope mapping, clonotype, paratope, and embedding clusterings are top performers. Most importantly, all the methods propose orthogonal groupings, offering more diverse pools of candidates when using multiple methods than any single method alone. To facilitate exploring the diversity of antibodies using different methods, we have created an online tool-CLAP-available at (clap.naturalantibody.com) that allows users to group, contrast, and visualize antibodies using the different grouping methods.

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.

Discovery of broadly-neutralizing antibodies against brown recluse spider and Gadim scorpion sphingomyelinases using consensus toxins as antigens

Broadly-neutralizing monoclonal antibodies are becoming increasingly important tools for treating infectious diseases and animal envenomings. However, designing and developing broadly-neutralizing antibodies can be cumbersome using traditional low-throughput iterative protein engineering methods. Here, we present a new high-throughput approach for the standardized discovery of broadly-neutralizing monoclonal antibodies relying on phage display technology and consensus antigens representing average sequences of related proteins. We showcase the utility of this approach by applying it to toxic sphingomyelinases from the venoms of species from very distant orders of the animal kingdom, the recluse spider and Gadim scorpion. First, we designed a consensus sphingomyelinase and performed three rounds of phage display selection, followed by DELFIA-based screening and ranking, and benchmarked this to a similar campaign involving cross-panning against recombinant versions of the native toxins. Second, we identified two scFvs that not only bind the consensus toxins, but which can also neutralize sphingomyelinase activity of native whole venom in vitro. Finally, we conclude that the phage display campaign involving the use of the consensus toxin was more successful in yielding cross-neutralizing scFvs than the phage display campaign involving cross-panning.

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

A breakthrough of immunoassay format for hapten: recent insights into noncompetitive immunoassays to detect small molecules

Immunoassay based on the antibodies specific for targets has advantages of high sensitivity, simplicity and low cost, therefore it has received more attention in recent years, especially for the rapid detection of small molecule chemicals present in foods, diagnostics and environments. However, limited by low molecular weight and only one antigenic determinant existed, immunoassays for these small molecule chemicals, namely hapten substances, were commonly performed in a competitive immunoassay format, whose sensitivities were obviously lower than the sandwich enzyme-linked immunosorbent assay generally adaptable for the protein targets. In order to break through the bottleneck of detection format, researchers have designed and established several novel noncompetitive immunoassays for the haptens in the past few years. In this review, we focused on the four representative types of noncompetitive immunoassay formats and described their characteristics and applications in rapid detection of small molecules. Meanwhile, a systematic discussion on the current technologies challenges and the possible solutions were also summarized. This review aims to provide an updated overview of the current state-of-the-art in noncompetitive immunoassay for small molecules, and inspire the development of novel designs for small molecule detection.

ExpoSeq: simplified analysis of high-throughput sequencing data from antibody discovery campaigns

Summary

High-throughput sequencing (HTS) offers a modern, fast, and explorative solution to unveil the full potential of display techniques, like antibody phage display, in molecular biology. However, a significant challenge lies in the processing and analysis of such data. Furthermore, there is a notable absence of open-access user-friendly software tools that can be utilized by scientists lacking programming expertise. Here, we present ExpoSeq as an easy-to-use tool to explore, process, and visualize HTS data from antibody discovery campaigns like an expert while only requiring a beginner's knowledge.

Availability and implementation

The pipeline is distributed via GitHub and PyPI, and it can either be installed as a package with pip or the user can choose to clone the repository.

Extracellular targeted protein degradation: an emerging modality for drug discovery

Targeted protein degradation (TPD) has emerged in the past decade as a major new drug modality to remove intracellular proteins with bispecific small molecules that recruit the protein of interest (POI) to an E3 ligase for degradation in the proteasome. Unlike classic occupancy-based drugs, intracellular TPD (iTPD) eliminates the target and works catalytically, and so can be more effective and sustained, with lower dose requirements. Recently, this approach has been expanded to the extracellular proteome, including both secreted and membrane proteins. Extracellular targeted protein degradation (eTPD) uses bispecific antibodies, conjugates or small molecules to degrade extracellular POIs by trafficking them to the lysosome for degradation. Here, we focus on recent advances in eTPD, covering degrader systems, targets, molecular designs and parameters to advance them. Now almost any protein, intracellular or extracellular, is addressable in principle with TPD.

Contextualising the developability risk of antibodies with lambda light chains using enhanced therapeutic antibody profiling

Antibodies with lambda light chains (λ-antibodies) are generally considered to be less developable than those with kappa light chains (κ-antibodies). Though this hypothesis has not been formally established, it has led to substantial systematic biases in drug discovery pipelines and thus contributed to kappa dominance amongst clinical-stage therapeutics. However, the identification of increasing numbers of epitopes preferentially engaged by λ-antibodies shows there is a functional cost to neglecting to consider them as potential lead candidates. Here, we update our Therapeutic Antibody Profiler (TAP) tool to use the latest data and machine learning-based structure prediction, and apply it to evaluate developability risk profiles for κ-antibodies and λ-antibodies based on their surface physicochemical properties. We find that while human λ-antibodies on average have a higher risk of developability issues than κ-antibodies, a sizeable proportion are assigned lower-risk profiles by TAP and should represent more tractable candidates for therapeutic development. Through a comparative analysis of the low- and high-risk populations, we highlight opportunities for strategic design that TAP suggests would enrich for more developable λ-antibodies. Overall, we provide context to the differing developability of κ- and λ-antibodies, enabling a rational approach to incorporate more diversity into the initial pool of immunotherapeutic candidates.

Towards better antivenoms: navigating the road to new types of snakebite envenoming therapies

Snakebite envenoming is a significant global health challenge, and for over a century, traditional plasma-derived antivenoms from hyperimmunized animals have been the primary treatment against this infliction. However, these antivenoms have several inherent limitations, including the risk of causing adverse reactions when administered to patients, batch-to-batch variation, and high production costs. To address these issues and improve treatment outcomes, the development of new types of antivenoms is crucial. During this development, key aspects such as improved clinical efficacy, enhanced safety profiles, and greater affordability should be in focus. To achieve these goals, modern biotechnological methods can be applied to the discovery and development of therapeutic agents that can neutralize medically important toxins from multiple snake species. This review highlights some of these agents, including monoclonal antibodies, nanobodies, and selected small molecules, that can achieve broad toxin neutralization, have favorable safety profiles, and can be produced on a large scale with standardized manufacturing processes. Considering the inherent strengths and limitations related to the pharmacokinetics of these different agents, a combination of them might be beneficial in the development of new types of antivenom products with improved therapeutic properties. While the implementation of new therapies requires time, it is foreseeable that the application of biotechnological advancements represents a promising trajectory toward the development of improved therapies for snakebite envenoming. As research and development continue to advance, these new products could emerge as the mainstay treatment in the future.

Phage display assisted discovery of a pH-dependent anti-α-cobratoxin antibody from a natural variable domain library

Recycling IgG antibodies bind to their target antigen at physiological pH in the blood stream and release them upon endocytosis when pH levels drop, allowing the IgG antibodies to be recycled into circulation via FcRn-mediated cellular pathways, while the antigens undergo lysosomal degradation. This enables recycling antibodies to achieve comparable therapeutic effect at lower doses than their non-recycling counterparts. The development of such antibodies is typically achieved by histidine doping of their variable regions or by performing in vitro antibody selection campaigns utilizing histidine doped libraries. Both are strategies that may introduce sequence liabilities. Here, we present a methodology that employs a naïve antibody phage display library, consisting of natural variable domains, to discover antibodies that bind α-cobratoxin from the venom of Naja kaouthia in a pH-dependent manner. As a result, an antibody was discovered that exhibits a 7-fold higher off-rate at pH 5.5 than pH 7.4 in bio-layer interferometry experiments. Interestingly, no histidine residues were found in its variable domains, and in addition, the antibody showed pH-dependent binding to a histidine-devoid antigen mutant. As such, the results demonstrate that pH-dependent antigen-antibody binding may not always be driven by histidine residues. By employing molecular dynamics simulations, different protonation states of titratable residues were found, which potentially could be responsible for the observed pH-dependent antigen binding properties of the antibody. Finally, given the typically high diversity of naïve antibody libraries, the methodology presented here can likely be applied to discover recycling antibodies against different targets ab initio without the need for histidine doping.

CD47Binder: Identify CD47 Binding Peptides by Combining Next-Generation Phage Display Data and Multiple Peptide Descriptors

CD47/SIRPα pathway is a new breakthrough in the field of tumor immunity after PD-1/PD-L1. While current monoclonal antibody therapies targeting CD47/SIRPα have demonstrated some anti-tumor effectiveness, there are several inherent limitations associated with these formulations. In the paper, we developed a predictive model that combines next-generation phage display (NGPD) and traditional machine learning methods to distinguish CD47 binding peptides. First, we utilized NGPD biopanning technology to screen CD47 binding peptides. Second, ten traditional machine learning methods based on multiple peptide descriptors and three deep learning methods were used to build computational models for identifying CD47 binding peptides. Finally, we proposed an integrated model based on support vector machine. During the five-fold cross-validation, the integrated predictor demonstrated specificity, accuracy, and sensitivity of 0.755, 0.764, and 0.772, respectively. Furthermore, an online bioinformatics tool called CD47Binder has been developed for the integrated predictor. This tool is readily accessible on http://i.uestc.edu.cn/CD47Binder/cgi-bin/CD47Binder.pl .

Deep learning in preclinical antibody drug discovery and development

Antibody drugs have become a key part of biotherapeutics. Patients suffering from various diseases have benefited from antibody therapies. However, its development process is rather long, expensive and risky. To speed up the process, reduce cost and improve success rate, artificial intelligence, especially deep learning methods, have been widely used in all aspects of preclinical antibody drug development, from library generation to hit identification, developability screening, lead selection and optimization. In this review, we systematically summarize antibody encodings, deep learning architectures and models used in preclinical antibody drug discovery and development. We also critically discuss challenges and opportunities, problems and possible solutions, current applications and future directions of deep learning in antibody drug development.

Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects

Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.

Discovery of a human monoclonal antibody that cross-neutralizes venom phospholipase A2s from three different snake genera

Despite the considerable global impact of snakebite envenoming, available treatments remain suboptimal. Here, we report the discovery of a broadly-neutralizing human monoclonal antibody, using a phage display-based cross-panning strategy, capable of reducing the cytotoxic effects of venom phospholipase A2s from three different snake genera from different continents. This highlights the potential of utilizing monoclonal antibodies to develop more effective, safer, and globally accessible polyvalent antivenoms that can be widely used to treat snakebite envenoming.

Establishment of an indirect ELISA for Mycobacterium tuberculosis MTB39A protein antibody

The MTB39A protein is a member of the unique Mycobacterium tuberculosis (MTB) PE/PPE protein family and is the main candidate for tuberculosis (TB) diagnosis. The aim of this study was to establish a novel indirect ELISA (iELISA) method that uses antibodies against MTB. The MTB39A gene sequence was synthesized according to the MTB39A nucleotide sequence of the MTB H37Rv strain (GenBank accession number: NC_000962.3) and cloned into the pET28a( +) vector. After correct sequencing, it was transferred to Escherichia coli BL21 (DE3) receptor cells for expression and purification, and the purified recombinant protein was identified by SDS-PAGE and western blotting. The purified MTB39A protein was used as the capture antibody, and a rabbit polyclonal antibody against the MTB MTB39A protein was used as the detection antibody to establish an indirect ELISA method. The ELISA conditions were optimized, and the optimal coating concentration of the MTB39A antigen was determined to be 0.5 μg/mL. The optimal dilution of MTB39A rabbit polyclonal antibody was 1:4096, and the optimal dilution of HRP-goat anti-rabbit IgG was 1:4000. The results showed that this indirect ELISA method has high sensitivity, specificity and efficacy for MTB39A protein detection. Moreover, this indirect ELISA method has optimal stability and can be used for the initial detection of MTB antibodies in clinical human and bovine serum samples. The establishment of this assay provides a new method for the rapid diagnosis of MTB and technical support for the prevention and control of tuberculosis. KEY POINTS: • MTB MTB39A protein was expressed in a prokaryotic expression system. • Rabbit polyclonal antibody against MTB39A was prepared. • To establish an iELISA based on the MTB39A protein for the detection of MTB antibodies.

AI/ML combined with next-generation sequencing of VHH immune repertoires enables the rapid identification of de novo humanized and sequence-optimized single domain antibodies: a prospective case study

Introduction: In this study, we demonstrate the feasibility of yeast surface display (YSD) and nextgeneration sequencing (NGS) in combination with artificial intelligence and machine learning methods (AI/ML) for the identification of de novo humanized single domain antibodies (sdAbs) with favorable early developability profiles. Methods: The display library was derived from a novel approach, in which VHH-based CDR3 regions obtained from a llama (Lama glama), immunized against NKp46, were grafted onto a humanized VHH backbone library that was diversified in CDR1 and CDR2. Following NGS analysis of sequence pools from two rounds of fluorescence-activated cell sorting we focused on four sequence clusters based on NGS frequency and enrichment analysis as well as in silico developability assessment. For each cluster, long short-term memory (LSTM) based deep generative models were trained and used for the in silico sampling of new sequences. Sequences were subjected to sequence- and structure-based in silico developability assessment to select a set of less than 10 sequences per cluster for production. Results: As demonstrated by binding kinetics and early developability assessment, this procedure represents a general strategy for the rapid and efficient design of potent and automatically humanized sdAb hits from screening selections with favorable early developability profiles.

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.

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

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

A mammalian cell display platform based on scFab transposition

In vitro display technologies have been successfully utilized for the discovery and evolution of monoclonal antibodies (mAbs) for diagnostic and therapeutic applications, with phage display and yeast display being the most commonly used platforms due to their simplicity and high efficiency. As their prokaryotic or lower eukaryotic host organisms typically have no or different post-translational modifications, several mammalian cell-based display and screening technologies for isolation and optimization of mAbs have emerged and are being developed. We report here a novel and useful mammalian cell display platform based on the PiggyBac transposon system to display mAbs in a single-chain Fab (scFab) format on the surface of HEK293F cells. Immune rabbit antibody libraries encompassing ~7 × 107 independent clones were generated in an all-in-one transposon vector, stably delivered into HEK293F cells and displayed as an scFab with rabbit variable and human constant domains. After one round of magnetic activated cell sorting and two rounds of fluorescence activated cell sorting, mAbs with high affinity in the subnanomolar range and cross-reactivity to the corresponding human and mouse antigens were identified, demonstrating the power of this platform for antibody discovery. We developed a highly efficient mammalian cell display platform based on the PiggyBac transposon system for antibody discovery, which could be further utilized for humanization as well as affinity and specificity maturation.

Orally delivered single-domain antibodies against gastrointestinal pathogens

Single-domain antibodies (sdAbs) are exceptionally stable fragments derived from the antigen-binding domains of immunoglobulins. They can withstand extreme pH, high temperature, and proteolysis, making them suitable for controlling gastrointestinal (GI) infections in humans and animals. sdAbs may function in their native soluble form, although different derived protein formats and the use of delivery vehicles can be useful for improved oral delivery. We discuss selected examples of the use of orally delivered sdAbs for protecting humans and animals against GI infections caused by pathogenic bacteria, viruses, and parasites. We finally provide perspectives on how sdAbs may be applied industrially and what challenges should be overcome for orally delivered sdAbs to reach the market.

Cross-reactivity trends when selecting scFv antibodies against snake toxins using a phage display-based cross-panning strategy

Antibodies with cross-reactive binding and broad toxin-neutralizing capabilities are advantageous for treating indications such as infectious diseases and animal envenomings. Such antibodies have been successfully selected against closely related antigens using phage display technology. However, the mechanisms driving antibody cross-reactivity typically remain to be elucidated. Therefore, we sought to explore how a previously reported phage display-based cross-panning strategy drives the selection of cross-reactive antibodies using seven different snake toxins belonging to three protein (sub-)families: phospholipases A2, long-chain α-neurotoxins, and short-chain α-neurotoxins. We showcase how cross-panning can increase the chances of discovering cross-reactive single-chain variable fragments (scFvs) from phage display campaigns. Further, we find that the feasibility of discovering cross-reactive antibodies using cross-panning cannot easily be predicted by analyzing the sequence, structural, or surface similarity of the antigens alone. However, when antigens share the (exact) same functions, this seems to increase the chances of selecting cross-reactive antibodies, which may possibly be due to the existence of structurally similar motifs on the antigens.

Nanobodies as Diagnostic and Therapeutic Tools for Cardiovascular Diseases (CVDs)

The aim of this review is to summarize some of the most recent work in the field of cardiovascular disease (CVD) diagnosis and therapy, focusing mainly on the role of nanobodies in the development of non-invasive imaging methods, diagnostic devices, and advanced biotechnological therapy tools. In the context of the increased number of people suffering from CVDs due to a variety of factors such as sedentariness, poor nutrition, stress, and smoking, there is an urgent need for new and improved diagnostic and therapeutic methods. Nanobodies can be easily produced in prokaryotes, lower eukaryotes, and plant and mammalian cells, and offer great advantages. In the diagnosis domain, they are mainly used as labeled probes that bind to certain surface receptors or other target molecules and give important information on the severity and extent of atherosclerotic lesions, using imaging methods such as contrast-enhanced ultrasound molecular imaging (CEUMI), positron emission tomography (PET), single-photon emission computed tomography coupled with computed tomography (SPECT/CT), and PET/CT. As therapy tools, nanobodies have been used either for transporting drug-loaded vesicles to specific targets or as inhibitors for certain enzymes and receptors, demonstrated to be involved in various CVDs.

Accelerating therapeutic protein design with computational approaches toward the clinical stage

Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.

Animal-derived products in science and current alternatives

More than fifty years after the 3Rs definition and despite the continuous implementation of regulatory measures, animals continue to be widely used in basic research. Their use comprises not only in vivo experiments with animal models, but also the production of a variety of supplements and products of animal origin for cell and tissue culture, cell-based assays, and therapeutics. The animal-derived products most used in basic research are fetal bovine serum (FBS), extracellular matrix proteins such as Matrigel™, and antibodies. However, their production raises several ethical issues regarding animal welfare. Additionally, their biological origin is associated with a high risk of contamination, resulting, frequently, in poor scientific data for clinical translation. These issues support the search for new animal-free products able to replace FBS, Matrigel™, and antibodies in basic research. In addition, in silico methodologies play an important role in the reduction of animal use in research by refining the data previously to in vitro and in vivo experiments. In this review, we depicted the current available animal-free alternatives in in vitro research.

Application of germline antibody features to vaccine development, antibody discovery, antibody optimization and disease diagnosis

Although the efficacy and commercial success of vaccines and therapeutic antibodies have been tremendous, designing and discovering new drug candidates remains a labor-, time- and cost-intensive endeavor with high risks. The main challenges of vaccine development are inducing a strong immune response in broad populations and providing effective prevention against a group of highly variable pathogens. Meanwhile, antibody discovery faces several great obstacles, especially the blindness in antibody screening and the unpredictability of the developability and druggability of antibody drugs. These challenges are largely due to poorly understanding of germline antibodies and the antibody responses to pathogen invasions. Thanks to the recent developments in high-throughput sequencing and structural biology, we have gained insight into the germline immunoglobulin (Ig) genes and germline antibodies and then the germline antibody features associated with antigens and disease manifestation. In this review, we firstly outline the broad associations between germline antibodies and antigens. Moreover, we comprehensively review the recent applications of antigen-specific germline antibody features, physicochemical properties-associated germline antibody features, and disease manifestation-associated germline antibody features on vaccine development, antibody discovery, antibody optimization, and disease diagnosis. Lastly, we discuss the bottlenecks and perspectives of current and potential applications of germline antibody features in the biotechnology field.

Generation and Next-Generation Sequencing-Based Characterization of a Large Human Combinatorial Antibody Library

Antibody phage display is a key technology for the discovery and development of target-specific monoclonal antibodies (mAbs) for use in research, diagnostics, and therapy. The construction of a high-quality antibody library, with larger and more diverse antibody repertoires, is essential for the successful development of phage display-derived mAbs. In this study, a large human combinatorial single-chain variable fragment library (1.5 × 10¹¹ colonies) was constructed from Epstein-Barr virus-infected human peripheral blood mononuclear cells stimulated with a combination of two of the activators of human B cells, the Toll-like receptor 7/8 agonist R848 and interleukin-2. Next-generation sequencing analysis with approximately 1.9 × 10⁶ and 2.7 × 10⁶ full-length sequences of heavy chain variable (VH) and κ light chain variable (Vκ) domains, respectively, revealed that the library consists of unique VH (approximately 94%) and Vκ (approximately 91%) sequences with greater diversity than germline sequences. Lastly, multiple unique mAbs with high affinity and broad cross-species reactivity could be isolated from the library against two therapeutically relevant target antigens, validating the library quality. These findings suggest that the novel antibody library we have developed may be useful for the rapid development of target-specific phage display-derived recombinant human mAbs for use in therapeutic and diagnostic applications.

DNA-Encoded Immunoassay in Picoliter Drops: A Minimal Cell-Free Approach

Immunoassays have emerged as indispensable bioanalytical tools but necessitate long preliminary steps for the selection, production, and purification of the antibody(ies) to be used. Here is explored the paradigm shift of creating a rapid and purification-free assay in picoliter drops where the antibody is expressed from coding DNA and its binding to antigens concomitantly characterized in situ. Efficient synthesis in bulk of various functional variable domains of heavy-chain only antibodies (VHH) using reconstituted cell-free expression media, including an anti-green fluorescent protein VHH, is shown first. A microfluidic device is then used to generate monodisperse drops (30 pL) containing all the assay components, including a capture scaffold, onto which the accumulation of VHH:antigen produces a specific fluorescent signal. This allows to assess, in parallel or sequentially at high throughput (500 Hz), the VHH-antigen binding and its specificity in less than 3 h, directly from a VHH-coding DNA, for multiple VHH sequences, various antigens and down to DNA concentrations as low as 12 plasmids per drop. It is anticipated that the ultraminiaturized format, robustness, and programmability of this novel cell-free immunoassay concept will constitute valuable assets in fields as diverse as antibody discovery, point-of-care diagnostics, synthetic biology, and/or bioanalytical assays.

Development of a human antibody fragment cross-neutralizing scorpion toxins

Previously, it was demonstrated that from the single chain fragment variable (scFv) 3F it is possible to generate variants capable of neutralizing the Cn2 and Css2 toxins, as well as their respective venoms (Centruroides noxius and Centruroides suffusus). Despite this success, it has not been easy to modify the recognition of this family of scFvs toward other dangerous scorpion toxins. The analysis of toxin-scFv interactions and in vitro maturation strategies allowed us to propose a new maturation pathway for scFv 3F to broaden recognition toward other Mexican scorpion toxins. From maturation processes against toxins CeII9 from C. elegans and Ct1a from C. tecomanus, the scFv RAS27 was developed. This scFv showed an increased affinity and cross-reactivity for at least 9 different toxins while maintaining recognition for its original target, the Cn2 toxin. In addition, it was confirmed that it can neutralize at least three different toxins. These results constitute an important advance since it was possible to improve the cross-reactivity and neutralizing capacity of the scFv 3F family of antibodies.

Discovery and optimization of a broadly-neutralizing human monoclonal antibody against long-chain α-neurotoxins from snakes

Snakebite envenoming continues to claim many lives across the globe, necessitating the development of improved therapies. To this end, broadly-neutralizing human monoclonal antibodies may possess advantages over current plasma-derived antivenoms by offering superior safety and high neutralization capacity. Here, we report the establishment of a pipeline based on phage display technology for the discovery and optimization of high affinity broadly-neutralizing human monoclonal antibodies. This approach yielded a recombinant human antibody with superior broadly-neutralizing capacities in vitro and in vivo against different long-chain α-neurotoxins from elapid snakes. This antibody prevents lethality induced by Naja kaouthia whole venom at an unprecedented low molar ratio of one antibody per toxin and prolongs the survival of mice injected with Dendroaspis polylepis or Ophiophagus hannah whole venoms.

Toward real-world automated antibody design with combinatorial Bayesian optimization

Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.

Get what you want: Identifying antibodies against pre-defined epitopes on Frizzled receptors

In this issue of Structure, Ge et al. report an epitope-directed strategy to select antibodies specific for Frizzled subtypes. Structural and biochemical analyses provide mechanistic insights into the target binding of the isolated antibodies that could guide the design of reagents and therapeutics targeting distinct Frizzled receptors.

The evolutionary and functional significance of germline immunoglobulin gene variation

The recombination between immunoglobulin (IG) gene segments determines an individual's naïve antibody repertoire and, consequently, (auto)antigen recognition. Emerging evidence suggests that mammalian IG germline variation impacts humoral immune responses associated with vaccination, infection, and autoimmunity - from the molecular level of epitope specificity, up to profound changes in the architecture of antibody repertoires. These links between IG germline variants and immunophenotype raise the question on the evolutionary causes and consequences of diversity within IG loci. We discuss why the extreme diversity in IG loci remains a mystery, why resolving this is important for the design of more effective vaccines and therapeutics, and how recent evidence from multiple lines of inquiry may help us do so.

Approaches to expand the conventional toolbox for discovery and selection of antibodies with drug-like physicochemical properties

Antibody drugs should exhibit not only high-binding affinity for their target antigens but also favorable physicochemical drug-like properties. Such drug-like biophysical properties are essential for the successful development of antibody drug products. The traditional approaches used in antibody drug development require significant experimentation to produce, optimize, and characterize many candidates. Therefore, it is attractive to integrate new methods that can optimize the process of selecting antibodies with both desired target-binding and drug-like biophysical properties. Here, we summarize a selection of techniques that can complement the conventional toolbox used to de-risk antibody drug development. These techniques can be integrated at different stages of the antibody development process to reduce the frequency of physicochemical liabilities in antibody libraries during initial discovery and to co-optimize multiple antibody features during early-stage antibody engineering and affinity maturation. Moreover, we highlight biophysical and computational approaches that can be used to predict physical degradation pathways relevant for long-term storage and in-use stability to reduce the need for extensive experimentation.

Assessing developability early in the discovery process for novel biologics

Beyond potency, a good developability profile is a key attribute of a biological drug. Selecting and screening for such attributes early in the drug development process can save resources and avoid costly late-stage failures. Here, we review some of the most important developability properties that can be assessed early on for biologics. These include the influence of the source of the biologic, its biophysical and pharmacokinetic properties, and how well it can be expressed recombinantly. We furthermore present in silico, in vitro, and in vivo methods and techniques that can be exploited at different stages of the discovery process to identify molecules with liabilities and thereby facilitate the selection of the most optimal drug leads. Finally, we reflect on the most relevant developability parameters for injectable versus orally delivered biologics and provide an outlook toward what general trends are expected to rise in the development of biologics.

One-Pot Droplet RT-OE-PCR for the Generation of Natively Paired Antibody Immune Libraries

Classical yeast surface display (YSD) antibody immune libraries are generated by a separate amplification of heavy- and light-chain antibody variable regions (VH and VL, respectively) and subsequent random recombination during the molecular cloning procedure. However, each B cell receptor comprises a unique VH-VL combination, which has been selected and affinity matured in vivo for optimal stability and antigen binding. Thus, the native variable chain pairing is important for the functioning and biophysical properties of the respective antibody. Herein, we present a method for the amplification of cognate VH-VL sequences, compatible with both next-generation sequencing (NGS) and YSD library cloning. We employ a single B cell encapsulation in water-in-oil droplets, followed by a one-pot reverse transcription overlap extension PCR (RT-OE-PCR), resulting in a paired VH-VL repertoire from more than a million B cells in a single day.

Production and purification of recombinant monoclonal antibodies from human cells based on a primary sequence

There are challenges to using commercially available antibodies generated in animals, including concerns with reproducibility, high costs, and ethical issues. Here, we present a protocol for generating and purifying recombinant antibodies from human HEK293 suspension culture cells from a primary sequence. We describe the steps to generate antibody heavy and light chain plasmids, followed by transfection of the plasmids into cells and purification of antibodies. This protocol can produce high-yield recombinant monoclonal antibodies at a relatively low cost. For complete details on the use and execution of this protocol, please refer to DeLuca et al. (2021).¹.

Multi-state modeling of antibody-antigen complexes with SAXS profiles and deep-learning models

Antibodies are an established class of human therapeutics. Epitope characterization is an important part of therapeutic antibody discovery. However, structural characterization of antibody-antigen complexes remains challenging. On the one hand, X-ray crystallography or cryo-electron microscopy provide atomic resolution characterization of the epitope, but the data collection process is typically long and the success rate is low. On the other hand, computational methods for modeling antibody-antigen structures from the individual components frequently suffer from a high false positive rate, rarely resulting in a unique solution. Recent deep learning models for structure prediction are also successful in predicting protein-protein complexes. However, they do not perform well for antibody-antigen complexes. Small Angle X-ray Scattering (SAXS) is a reliable technique for rapid structural characterization of protein samples in solution albeit at low resolution. Here, we present an integrative approach for modeling antigen-antibody complexes using the antibody sequence, antigen structure, and experimentally determined SAXS profiles of the antibody, antigen, and the complex. The method models antibody structures using a novel deep-learning approach, NanoNet. The structures of the antibodies and antigens are represented using multiple 3D conformations to account for compositional and conformational heterogeneity of the protein samples that are used to collect the SAXS data. The complexes are predicted by integrating the SAXS profiles with scoring functions for protein-protein interfaces that are based on statistical potentials and antibody-specific deep-learning models. We validated the method via application to four Fab:EGFR and one Fab:PCSK9 antibody:antigen complexes with experimentally available SAXS datasets. The integrative approach returns accurate predictions (interface RMSD<4Å) in the top five predictions for four out of five complexes (respective interface RMSD values of 1.95, 2.18, 2.66 and 3.87Å), providing support for the utility of such a computational pipeline for epitope characterization during therapeutic antibody discovery.

Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction

Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody-antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.

Antibodies as Snakebite Antivenoms: Past and Future

Snakebite envenomation is considered a neglected tropical disease, affecting tens of thousands of people each year. The recommended treatment is the use of antivenom, which is composed of immunoglobulins or immunoglobulin fragments obtained from the plasma of animals hyperimmunized with one (monospecific) or several (polyspecific) venoms. In this review, the efforts made in the improvement of the already available antivenoms and the development of new antivenoms, focusing on snakes of medical importance from sub-Saharan Africa and Latin America, are described. Some antivenoms currently used are composed of whole IgGs, whereas others use F(ab’)2 fragments. The classic methods of attaining snake antivenoms are presented, in addition to new strategies to improve their effectiveness. Punctual changes in immunization protocols, in addition to the use of cross-reactivity between venoms from different snakes for the manufacture of more potent and widely used antivenoms, are presented. It is known that venoms are a complex mixture of components; however, advances in the field of antivenoms have shown that there are key toxins that, if effectively blocked, are capable of reversing the condition of in vivo envenomation. These studies provide an opportunity for the use of monoclonal antibodies in the development of new-generation antivenoms. Thus, monoclonal antibodies and their fragments are described as a possible alternative for the production of antivenoms, regardless of the venom. This review also highlights the challenges associated with their development.

Versatile Application of Nanobodies for Food Allergen Detection and Allergy Immunotherapy

The unique characteristics of camelid heavy-chain only antibody (HCAb) derived nanobodies (Nbs) have facilitated their employment as tools for research and application in extensive fields including food safety inspection, diagnosis and therapy of diseases, etc., to develop immune detecting techniques or alternative candidates of conventional antibodies as diagnostic and therapeutic reagents. The wide application in the fields of food allergen inspection and immunotherapy has not been addressed as not much results published in the literature. The robust properties and straightforward selecting strategy of Nbs impel the advantageous employment compared with monoclonal antibodies (mAbs) to establish immunoassay and serve as blocking antibodies to compete immunoglobulin E (IgE) binding epitopes on food allergens. More and more efforts have been invested to develop specific Nbs against food allergen proteins, such as macadamia allergen of Mac i 1, peanut allergen of Ara h 3, and lupine allergen of Lup an 1, which demonstrated the potential of Nbs for research and application in food allergen surveillance. Meanwhile, the paratopes of Nbs preferably targeting the unique epitopes of food allergens can provide more possibilities to serve as blocking antibodies to shield IgE binding epitopes for food allergy immunotherapy. Regardless, the research and application of Nbs in the field of food allergen and allergic reactions are expected to attract dramatic focus and produce promising research outputs.

Designing antibodies as therapeutics

Antibody therapeutics are a large and rapidly expanding drug class providing major health benefits. We provide a snapshot of current antibody therapeutics including their formats, common targets, therapeutic areas, and routes of administration. Our focus is on selected emerging directions in antibody design where progress may provide a broad benefit. These topics include enhancing antibodies for cancer, antibody delivery to organs such as the brain, gastrointestinal tract, and lungs, plus antibody developability challenges including immunogenicity risk assessment and mitigation and subcutaneous delivery. Machine learning has the potential, albeit as yet largely unrealized, for a transformative future impact on antibody discovery and engineering.

PDL1Binder: Identifying programmed cell death ligand 1 binding peptides by incorporating next-generation phage display data and different peptide descriptors

Monoclonal antibody drugs targeting the PD-1/PD-L1 pathway have showed efficacy in the treatment of cancer patients, however, they have many intrinsic limitations and inevitable drawbacks. Peptide inhibitors as alternatives might compensate for the drawbacks of current PD-1/PD-L1 interaction blockers. Identifying PD-L1 binding peptides by random peptide library screening is a time-consuming and labor-intensive process. Machine learning-based computational models enable rapid discovery of peptide candidates targeting the PD-1/PD-L1 pathway. In this study, we first employed next-generation phage display (NGPD) biopanning to isolate PD-L1 binding peptides. Different peptide descriptors and feature selection methods as well as diverse machine learning methods were then incorporated to implement predictive models of PD-L1 binding. Finally, we proposed PDL1Binder, an ensemble computational model for efficiently obtaining PD-L1 binding peptides. Our results suggest that predictive models of PD-L1 binding can be learned from deep sequencing data and provide a new path to discover PD-L1 binding peptides. A web server was implemented for PDL1Binder, which is freely available at http://i.uestc.edu.cn/pdl1binder/cgi-bin/PDL1Binder.pl.

Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery

Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody–antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.

Advances in antibody phage display technology

Phage display technology can be used for the discovery of antibodies for research, diagnostic, and therapeutic purposes. In this review, we present and discuss key parameters that can be optimized when performing phage display selection campaigns, including the use of different antibody formats and advanced strategies for antigen presentation, such as immobilization, liposomes, nanodiscs, virus-like particles, and whole cells. Furthermore, we provide insights into selection strategies that can be used for the discovery of antibodies with complex binding requirements, such as targeting a specific epitope, cross-reactivity, or pH-dependent binding. Lastly, we provide a description of specialized phage display libraries for the discovery of bispecific antibodies and pH-sensitive antibodies. Together, these methods can be used to improve antibody discovery campaigns against all types of antigen. Teaser: This review provides an overview of the different strategies that can be exploited to improve the success rate of antibody phage display discovery campaigns, addressing key parameters, such as antigen presentation, selection methodologies, and specialized libraries.