Engineering aggregation-resistant antibodies

Stable and reusable calcium-responsive biopolymer for affinity precipitation of therapeutic antibodies

Affinity precipitation is an attractive method for protein purification due to its many advantages, including the rapid capture of target proteins, simple processing, high specificity, and ease of scale-up. We previously reported a robust antibody purification method using Ca2+-dependent precipitation of ZZ-hCSQ2, a fusion protein of human calsequestrin 2, and the antibody-binding protein ZZ. However, the stability of this fusion protein was not sufficiently high for industrial use because the antibody recovery yield decreased to 60% after being reused 10 times. To identify a more stable calsequestrin (CSQ), we calculated Rosetta energy values for the folding stabilities of various CSQ homologs and selected human CSQ1 (hCSQ1) with lowest energy value (-992.6) as the new CSQ platform. We also identified that the linker sequence between ZZ and CSQ was vulnerable to proteases and alkaline pH by N-terminal protein sequencing. Therefore, we changed the linker to four asparagine (4N) sequences, which were shorter and less flexible than the previous glycine-rich linker. The new version of ZZ-CSQ, ZZ-4N-hCSQ1, was stable in a protease-containing conditioned medium obtained from the cultured Chinese hamster ovary cell or high pH condition (0.1M sodium hydroxide) for more than 5 days and could be reused at least 25 times for antibody purification without loss of recovery yield. The antibodies purified by ZZ-4N-hCSQ1 precipitation also showed greater purity (~33.6-fold lower host cell DNA and ~6.4-fold lower host cell protein) than those purified by protein A chromatography. These data suggest that ZZ-4N-hCSQ1 precipitation is more efficient and can achieve cost-effectiveness of up to 12.5-fold cheaper than previous antibody purification methods and can lower the production costs of therapeutic antibodies.

Dialysis rolled scaffold bioreactor allows extended production of monoclonal antibody with reduced media use

Rapidly expanding biopharmaceutical market demands more cost-effective platforms to produce protein therapeutics. To this end, novel approaches, such as perfusion culture or concentrated fed-batch, have been explored for higher yields and lower manufacturing costs. Although these new approaches produced promising results, but their wide-spread use in the industry is still limited. In this study, a dialysis rolled scaffold bioreactor was presented for long-term production of monoclonal antibodies with reduced media consumption. Media dialysis can selectively remove cellular bio-wastes without losing cells or produced recombinant proteins. The dialysis process was streamlined to significantly improve its efficiency. Then, extended culture of recombinant CHO cells for 41 days was successfully demonstrated with consistent production rate and minimal media consumption. The unique configuration of the developed bioreactor allows efficient dialysis for media management, as well as rapid media exchange to harvest produced recombinant proteins before they degrade. Taken together, it was envisioned that the developed bioreactor will enable cost-effective and long-term large-scale culture of various cells for biopharmaceutical production.

Antibody engineering to generate anti-tumor-associated glycoprotein 72 mouse recombinant CC49 IgG with improved solubility, purity, and thermal stability

Tumor-associated glycoprotein 72 (TAG-72) is a mucin that is overexpressed heterogeneously on the surface of cancer cells, and is a potential target for immunotherapies for various cancer types. As a tumor marker, TAG-72 is measured with the cancer antigen (CA) 72-4 immunoassay. The murine monoclonal antibody (mAb) CC49 is a second-generation IgG that targets an antigen on TAG-72; however, CC49 has an unfavorable propensity to aggregate, which results in antibody impurity, instability, and low solubility and thus low potency and efficacy for therapeutic and diagnostic applications. Sequence analysis of CC49 revealed aggregation-prone motifs in the variable domain of the light chain. Using antibody engineering approaches, we developed three aggregation-resistant CC49 mIgG2a mutants (CC49M1, CC49M2, and CC49M3). The engineered CC49 mIgG2a mutants retained compatible binding performance with a significantly higher thermal stability. The CC49 mIgG2a mutants also demonstrated an almost 15-fold improvement in solubility, with 97% purity vs 70% purity of the parent molecule at 0.3 mg/mL. The enhanced stability and improved solubility of engineered CC49 could have significant advantages for diagnostic and therapeutic applications.

A3D Model Organism Database (A3D-MODB): a database for proteome aggregation predictions in model organisms

Protein aggregation has been associated with aging and different pathologies and represents a bottleneck in the industrial production of biotherapeutics. Numerous past studies performed in Escherichia coli and other model organisms have allowed to dissect the biophysical principles underlying this process. This knowledge fuelled the development of computational tools, such as Aggrescan 3D (A3D) to forecast and re-design protein aggregation. Here, we present the A3D Model Organism Database (A3D-MODB) http://biocomp.chem.uw.edu.pl/A3D2/MODB, a comprehensive resource for the study of structural protein aggregation in the proteomes of 12 key model species spanning distant biological clades. In addition to A3D predictions, this resource incorporates information useful for contextualizing protein aggregation, including membrane protein topology and structural model confidence, as an indirect reporter of protein disorder. The database is openly accessible without any need for registration. We foresee A3D-MOBD evolving into a central hub for conducting comprehensive, multi-species analyses of protein aggregation, fostering the development of protein-based solutions for medical, biotechnological, agricultural and industrial applications.

Multidimensional Protein Solubility Optimization with an Ultrahigh-Throughput Microfluidic Platform

Protein-based biologics are highly suitable for drug development as they exhibit low toxicity and high specificity for their targets. However, for therapeutic applications, biologics must often be formulated to elevated concentrations, making insufficient solubility a critical bottleneck in the drug development pipeline. Here, we report an ultrahigh-throughput microfluidic platform for protein solubility screening. In comparison with previous methods, this microfluidic platform can make, incubate, and measure samples in a few minutes, uses just 20 μg of protein (>10-fold improvement), and yields 10,000 data points (1000-fold improvement). This allows quantitative comparison of formulation excipients, such as sodium chloride, polysorbate, histidine, arginine, and sucrose. Additionally, we can measure how solubility is affected by the combinatorial effect of multiple additives, find a suitable pH for the formulation, and measure the impact of mutations on solubility, thus enabling the screening of large libraries. By reducing material and time costs, this approach makes detailed multidimensional solubility optimization experiments possible, streamlining drug development and increasing our understanding of biotherapeutic solubility and the effects of excipients.

The immunolocalization of cluster of differentiation 31, phalloidin and alpha smooth muscle actin on vascular network of normal and ischemic rat brain

Cluster of differentiation 31 (CD31), phalloidin and alpha smooth muscle actin (α-SMA) have been widely applied to label the cerebral blood vessels in the past years. Although CD31 is mainly used as endothelial marker in determining the cerebral capillaries, it seems likely that its labeling efficiency is closely correlated with the antibodies from the polyclonal or monoclonal one, as well as the conditions of blood vessels. In order to test this phenomenon, we compared the labeling characteristics of goat polyclonal anti-CD31 (gP-CD31) and mouse monoclonal anti-CD31 (mM-CD31) with those of phalloidin and α-SMA on the rat brain in health and ischemia/reperfusion (I/R) with the middle cerebral artery occlusion. By multiple immunofluorescence staining, it was found that gP-CD31 labeling expressed extensively on the cerebral capillaries forming the vascular networks on the normal and ischemic regions, but mM-CD31 labeling mainly presented on the capillaries in the ischemic region. In contrast to the vascular labeling with gP-CD31, phalloidin and α-SMA were mainly expressed on the wall of cortical penetrating arteries, and less on that of capillaries. By three-dimensional reconstruction analysis, it was clearly shown that gP-CD31 labeling was mainly located on the lumen side of vascular wall and was surrounded by phalloidin labeling and α-SMA labeling. These results indicate that gP-CD31 is more sensitive than mM-CD31 for labeling the cerebral vasculature, and is highly compatible with phalloidin and α-SMA for evaluating the cerebral vascular networks under the physiological and pathological conditions.

Optimization for Simultaneous Removal of Product/Process-Related Impurities of Peptide Fc-Fusion Protein Using Cation Exchange Chromatography

Fc fusion proteins are used as therapeutic agents with unique structures by combining the Fc domain of an antibody with other active proteins, cytokines, and enzymes. Peptide Fc-fusion proteins are complex fusion molecules that possess a structure different from that of monoclonal antibodies (mAbs) and are difficult to express, thereby affecting their quality. Many product/process-related impurities generated during the production of peptide Fc-fusion proteins pose a risk to the robustness of pre-existing three-column platforms for the purification of mAbs. Thus, we first evaluated the effect of pH, conductivity, and dynamic binding capacity (DBC; g of product per liter of resin) on the separation of host cell protein (HCP) and high molecular weight (HMW) and low molecular weight (LMW) proteins in strong cation exchange chromatography and then established an operating range using the design of experiments (DoE). Based on our studies, the optimal removal rates of HCP and HMW were achieved under the following conditions: 8 CV of wash buffer, 20–23 g/L of resin DBC, and an elution buffer conductivity of 63–66 mS/cm. The conductivity of the wash buffer used to remove the LMW was 50 mS/cm. In addition, reproducibility was confirmed by scaling up two batches using the Fractogel® EMD SO3− (M) resin. As a result of confirming with a validated test method in all batches, >55% yield, >98.2% purity, and >27% HCP reduction rate were satisfied. The cation exchanger exhibited an acceptable step yield and effectively reduced product/process-related impurities within the established range.

Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space

Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies.

Measuring Self-Association of Antibody Lead Candidates with Dynamic Light Scattering

In this method chapter, we provide a brief overview of the key methods available to measure self-association of monoclonal antibodies (mAbs) and explain for which experimental throughputs they are usually applied. We then focus on dynamic light scattering (DLS) and describe experimental details on how to measure the diffusion interaction parameter (k_D) which is occasionally referred to as the gold standard for measuring self-association of proteins. The k_D is a well-established parameter to predict solution viscosity, which is one of the most critical developability parameters of mAbs. Finally, we present a pH and excipient screen that is designed to measure self-association with DLS under conditions that are relevant for bioprocessing and formulation of mAbs. The presented light scattering methods are well suited for lead candidate selections where it is essential to select mAbs with high developability potential for progression toward first human dose.

An open-source automated PEG precipitation assay to measure the relative solubility of proteins with low material requirement

The solubility of proteins correlates with a variety of their properties, including function, production yield, pharmacokinetics, and formulation at high concentrations. High solubility is therefore a key requirement for the development of protein-based reagents for applications in life sciences, biotechnology, diagnostics, and therapeutics. Accurate solubility measurements, however, remain challenging and resource intensive, which limits their throughput and hence their applicability at the early stages of development pipelines, when long-lists of candidates are typically available in minute amounts. Here, we present an automated method based on the titration of a crowding agent (polyethylene glycol, PEG) to quantitatively assess relative solubility of proteins using about 200 µg of purified material. Our results demonstrate that this method is accurate and economical in material requirement and costs of reagents, which makes it suitable for high-throughput screening. This approach is freely-shared and based on a low cost, open-source liquid-handling robot. We anticipate that this method will facilitate the assessment of the developability of proteins and make it substantially more accessible.

Systematic Engineering of Optimized Autonomous Heavy-Chain Variable Domains

Autonomous heavy-chain variable (V_H) domains are the smallest functional antibody fragments, and they possess unique features, including small size and convex paratopes, which provide enhanced targeting of concave epitopes that are difficult to access with larger conventional antibodies. However, human V_H domains have evolved to fold and function with a light chain partner, and alone, they typically suffer from low stability and high aggregation propensity. Development of autonomous human V_H domains, in which aggregation propensity is reduced without compromising antigen recognition, has proven challenging. Here, we used an autonomous human V_H domain as a scaffold to construct phage-displayed synthetic libraries in which aspartate was systematically incorporated at different paratope positions. In selections, the library yielded many anti-EphA1 receptor V_H domains, which were characterized in detail. Structural analyses of a parental anti-EphA1 V_H domain and an improved variant provided insights into the effects of aspartate and other substitutions on preventing aggregation while retaining function. Our naïve libraries and in vitro selection procedures offer a systematic approach to generating highly functional autonomous human V_H domains that resist aggregation and could be used for basic research and biomedical applications.

Detection of Isopeptide Bonds in Monoclonal Antibody Aggregates

Purpose

A major difficulty in monoclonal antibody (mAb) therapeutic development is product aggregation. In this study, intermolecular isopeptide bonds in mAb aggregates were characterized for the first time. We aim to propose a mechanism of covalent aggregation in a model antibody using stressed studies at raised temperatures to aid in the understanding of mAb aggregation pathways.

Methods

Aggregate fractions were generated using raised temperature and were purified using size-exclusion chromatography (SEC). The fractions were tryptically digested and characterized using liquid chromatography hyphenated to tandem mass-spectrometry (LC–MS/MS).

Results

An increased amount of clipping between aspartic acid and proline in a solvent accessible loop in the constant heavy 2 (CH2) domain of the mAb was observed under these conditions. Detailed peptide mapping revealed 14 isopeptide bonds between aspartic acid at that cleavage site and lysine residues on adjacent antibodies. Two additional isopeptide bonds were identified between the mAb HC N-terminal glutamic acid or a separate aspartic acid to lysine residues on adjacent antibodies.

Conclusions

Inter-protein isopeptide bonds between the side chains of acidic amino acids (aspartate and glutamate) and lysine were characterized for the first time in mAb aggregates. A chemical mechanism was presented whereby spontaneous isopeptide bond formation could be facilitated via either the aspartic acid side chain or C-terminus.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11095-021-03103-y.

Contribution of the Commensal Microflora to the Immunological Homeostasis and the Importance of Immune-Related Drug Development for Clinical Applications

Not long ago, self-reactive immune activity was considered as pathological trait. A paradigm shift has now led to the recognition of autoimmune processes as part of natural maintenance of molecular homeostasis. The immune system is assigned further roles beneath the defense against pathogenic organisms. Regarding the humoral immune system, the investigation of natural autoantibodies that are frequently found in healthy individuals has led to further hypotheses involving natural autoimmunity in other processes as the clearing of cellular debris or decrease in inflammatory processes. However, their role and origin have not been entirely clarified, but accumulating evidence links their formation to immune reactions against the gut microbiome. Antibodies targeting highly conserved proteins of the commensal microflora are suggested to show self-reactive properties, following the paradigm of the molecular mimicry. Here, we discuss recent findings, which demonstrate potential links of the commensal microflora to the immunological homeostasis and highlight the possible implications for various diseases. Furthermore, specific components of the immune system, especially antibodies, have become a focus of attention for the medical management of various diseases and provide attractive treatment options in the future. Nevertheless, the development and optimization of such macromolecules still represents a very time-consuming task, shifting the need to more medical agents with simple structural properties and low manufacturing costs. Synthesizing only the biologically active sites of antibodies has become of great interest for the pharmaceutical industry and offers a wide range of therapeutic application areas as it will be discussed in the present review article.

Bispecific Antibodies in Prostate Cancer Therapy: Current Status and Perspectives

Prostate carcinoma (PC) is the second most common cancer in men. When the disease becomes unresponsive to androgen deprivation therapy, the remaining treatment options are of limited benefit. Despite intense efforts, none of the T cell-based immunotherapeutic strategies that meanwhile have become a cornerstone for treatment of other malignancies is established in PC. This refers to immune checkpoint inhibition (CI), which generally reinforces T cell immunity as well as chimeric antigen receptor T (CAR-T) cells and bispecific antibodies (bsAbs) that stimulate the T cell receptor/CD3-complex and mobilize T cells in a targeted manner. In general, compared to CAR-T cells, bsAb would have the advantage of being an "off the shelf" reagent associated with less preparative effort, but at present, despite enormous efforts, neither CAR-T cells nor bsAbs are successful in solid tumors. Here, we focus on the various bispecific constructs that are presently in development for treatment of PC, and discuss underlying concepts and the state of clinical evaluation as well as future perspectives.

An engineered human IgG1 CH2 domain with decreased aggregation and nonspecific binding

The immunoglobulin (Ig) CH2 domain is a promising scaffold for the development of candidate therapeutics. We have previously shown that the stability of isolated CH2 could be increased by the introduction of an additional disulfide bond and removal of seven N-terminal residues (m01s). However, both isolated CH2 and m01s aggregate, likely due to the existence of aggregation-prone regions (APRs) that we identified by using computational methods. This knowledge was used to generate a phage display library of mutants. The library was incubated at high temperature to remove aggregating CH2 domains, and then panned against a mouse anti-human CH2 monoclonal antibody targeting a conformational epitope to remove misfolded CH2s. After two rounds of panning, one clone, m01s5, with smaller APRs, was identified. After additional mutagenesis one clone, m01s5.4, which aggregated much less than m01s as measured by a turbidity assay and dynamic light scattering, was identified. m01s5.4 also exhibited much lower nonspecific binding than m01s. Engineering of a previously identified m01s-based tumor antigen-specific binder led to a dramatic reduction of its aggregation without affecting its binding. In summary, we describe a new approach for reducing aggregation based on a combination of computational and phage display methodologies, and show that aggregation of CH2-based scaffolds can be significantly reduced by the newly identified mutants, which can improve the developability of potential CH2-based therapeutics.

Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods

There is intense and widespread interest in developing monoclonal antibodies as therapeutic agents to treat diverse human disorders. During early-stage antibody discovery, hundreds to thousands of lead candidates are identified, and those that lack optimal physical and chemical properties must be deselected as early as possible to avoid problems later in drug development. It is particularly challenging to characterize such properties for large numbers of candidates with the low antibody quantities, concentrations, and purities that are available at the discovery stage, and to predict concentrated antibody properties (e.g., solubility, viscosity) required for efficient formulation, delivery, and efficacy. Here we review key recent advances in developing and implementing high-throughput methods for identifying antibodies with desirable in vitro and in vivo properties, including favorable antibody stability, specificity, solubility, pharmacokinetics, and immunogenicity profiles, that together encompass overall drug developability. In particular, we highlight impressive recent progress in developing computational methods for improving rational antibody design and prediction of drug-like behaviors that hold great promise for reducing the amount of required experimentation. We also discuss outstanding challenges that will need to be addressed in the future to fully realize the great potential of using such analysis for minimizing development times and improving the success rate of antibody candidates in the clinic.

Directed evolution of conformation-specific antibodies for sensitive detection of polypeptide aggregates in therapeutic drug formulations

Biologics such as peptides and proteins possess a number of attractive attributes that make them particularly valuable as therapeutics, including their high activity, high specificity, and low toxicity. However, one of the key challenges associated with this class of drugs is their propensity to aggregate. Given the safety and immunogenicity concerns related to polypeptide aggregates, it is particularly important to sensitively detect aggregates in therapeutic drug formulations as part of the quality control process. Here, we report the development of conformation-specific antibodies that recognize polypeptide aggregates composed of a GLP-1 receptor agonist (liraglutide) and their integration into a sensitive immunoassay for detecting liraglutide amyloid fibrils. We sorted single-chain antibody libraries against liraglutide fibrils using yeast surface display and magnetic-activated cell sorting, and identified several antibodies with high conformational specificity. Interestingly, these antibodies cross-react with amyloid fibrils formed by several other polypeptides, revealing that they recognize molecular features common to different types of fibrils. Moreover, we find that our immunoassay using these antibodies is >50-fold more sensitive than the conventional method for detecting liraglutide aggregation (Thioflavin T fluorescence). We expect that our systematic approach for generating a sensitive, aggregate-specific immunoassay can be readily extended to other biologics to improve the quality and safety of formulated drug products.

SOLart: a structure-based method to predict protein solubility and aggregation

MOTIVATION

The solubility of a protein is often decisive for its proper functioning. Lack of solubility is a major bottleneck in high-throughput structural genomic studies and in high-concentration protein production, and the formation of protein aggregates causes a wide variety of diseases. Since solubility measurements are time-consuming and expensive, there is a strong need for solubility prediction tools.

RESULTS

We have recently introduced solubility-dependent distance potentials that are able to unravel the role of residue-residue interactions in promoting or decreasing protein solubility. Here, we extended their construction by defining solubility-dependent potentials based on backbone torsion angles and solvent accessibility, and integrated them, together with other structure- and sequence-based features, into a random forest model trained on a set of Escherichia coli proteins with experimental structures and solubility values. We thus obtained the SOLart protein solubility predictor, whose most informative features turned out to be folding free energy differences computed from our solubility-dependent statistical potentials. SOLart performances are very good, with a Pearson correlation coefficient between experimental and predicted solubility values of almost 0.7 both in cross-validation on the training dataset and in an independent set of Saccharomyces cerevisiae proteins. On test sets of modeled structures, only a limited drop in performance is observed. SOLart can thus be used with both high-resolution and low-resolution structures, and clearly outperforms state-of-art solubility predictors. It is available through a user-friendly webserver, which is easy to use by non-expert scientists.

AVAILABILITY AND IMPLEMENTATION

The SOLart webserver is freely available at http://babylone.ulb.ac.be/SOLART/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Phage Display Derived Monoclonal Antibodies: From Bench to Bedside

Monoclonal antibodies (mAbs) have become one of the most important classes of biopharmaceutical products, and they continue to dominate the universe of biopharmaceutical markets in terms of approval and sales. They are the most profitable single product class, where they represent six of the top ten selling drugs. At the beginning of the 1990s, an in vitro antibody selection technology known as antibody phage display was developed by John McCafferty and Sir. Gregory Winter that enabled the discovery of human antibodies for diverse applications, particularly antibody-based drugs. They created combinatorial antibody libraries on filamentous phage to be utilized for generating antigen specific antibodies in a matter of weeks. Since then, more than 70 phage–derived antibodies entered clinical studies and 14 of them have been approved. These antibodies are indicated for cancer, and non-cancer medical conditions, such as inflammatory, optical, infectious, or immunological diseases. This review will illustrate the utility of phage display as a powerful platform for therapeutic antibodies discovery and describe in detail all the approved mAbs derived from phage display.

Physicochemical Rules for Identifying Monoclonal Antibodies with Drug-like Specificity

The ability of antibodies to recognize their target antigens with high specificity is fundamental to their natural function. Nevertheless, therapeutic antibodies display variable and difficult-to-predict levels of nonspecific and self-interactions that can lead to various drug development challenges, including antibody aggregation, abnormally high viscosity, and rapid antibody clearance. Here we report a method for predicting the overall specificity of antibodies in terms of their relative risk for displaying high levels of nonspecific or self-interactions at physiological conditions. We find that individual and combined sets of chemical rules that limit the maximum and minimum numbers of certain solvent-exposed amino acids in antibody variable regions are strong predictors of specificity for large panels of preclinical and clinical-stage antibodies. We also demonstrate how the chemical rules can be used to identify sites that mediate nonspecific interactions in suboptimal antibodies and guide the design of targeted sublibraries that yield variants with high antibody specificity. These findings can be readily used to improve the selection and engineering of antibodies with drug-like specificity.

Insight into the protein solubility driving forces with neural attention

Protein solubility is a key aspect for many biotechnological, biomedical and industrial processes, such as the production of active proteins and antibodies. In addition, understanding the molecular determinants of the solubility of proteins may be crucial to shed light on the molecular mechanisms of diseases caused by aggregation processes such as amyloidosis. Here we present SKADE, a novel Neural Network protein solubility predictor and we show how it can provide novel insight into the protein solubility mechanisms, thanks to its neural attention architecture. First, we show that SKADE positively compares with state of the art tools while using just the protein sequence as input. Then, thanks to the neural attention mechanism, we use SKADE to investigate the patterns learned during training and we analyse its decision process. We use this peculiarity to show that, while the attention profiles do not correlate with obvious sequence aspects such as biophysical properties of the aminoacids, they suggest that N- and C-termini are the most relevant regions for solubility prediction and are predictive for complex emergent properties such as aggregation-prone regions involved in beta-amyloidosis and contact density. Moreover, SKADE is able to identify mutations that increase or decrease the overall solubility of the protein, allowing it to be used to perform large scale in-silico mutagenesis of proteins in order to maximize their solubility.

The solubility of proteins is a crucial biophysical aspect when it comes to understanding many human diseases and to improve the industrial processes for protein production. Due to its relevance, computational methods have been devised in order to study and possibly optimize the solubility of proteins. In this work we apply a deep-learning technique, called neural attention to predict protein solubility while “opening” the model itself to interpretability, even though Machine Learning models are usually considered black boxes. Thank to the attention mechanism, we show that i) our model implicitly learns complex patterns related to emergent, protein folding-related, aspects such as to recognize β-amyloidosis regions and that ii) the N-and C-termini are the regions with the highes signal fro solubility prediction. When it comes to enhancing the solubility of proteins, we, for the first time, propose to investigate the synergistic effects of tandem mutations instead of “single” mutations, suggesting that this could minimize the number of required proposed mutations.

Computer-based Engineering of Thermostabilized Antibody Fragments

We used the molecular modeling program Rosetta to identify clusters of amino acid substitutions in antibody fragments (scFvs and scAbs) that improve global protein stability and resistance to thermal deactivation. Using this methodology, we increased the melting temperature (T_m) and resistance to heat treatment of an antibody fragment that binds to the Clostridium botulinum hemagglutinin protein (anti-HA33). Two designed antibody fragment variants with two amino acid replacement clusters, designed to stabilize local regions, were shown to have both higher T_m compared to the parental scFv and importantly, to retain full antigen binding activity after 2 hours of incubation at 70 °C. The crystal structure of one thermostabilized scFv variants was solved at 1.6 Å and shown to be in close agreement with the RosettaAntibody model prediction.

Phage Display Derived Monoclonal Antibodies: From Bench to Bedside

Monoclonal antibodies (mAbs) have become one of the most important classes of biopharmaceutical products, and they continue to dominate the universe of biopharmaceutical markets in terms of approval and sales. They are the most profitable single product class, where they represent six of the top ten selling drugs. At the beginning of the 1990s, an in vitro antibody selection technology known as antibody phage display was developed by John McCafferty and Sir. Gregory Winter that enabled the discovery of human antibodies for diverse applications, particularly antibody-based drugs. They created combinatorial antibody libraries on filamentous phage to be utilized for generating antigen specific antibodies in a matter of weeks. Since then, more than 70 phage-derived antibodies entered clinical studies and 14 of them have been approved. These antibodies are indicated for cancer, and non-cancer medical conditions, such as inflammatory, optical, infectious, or immunological diseases. This review will illustrate the utility of phage display as a powerful platform for therapeutic antibodies discovery and describe in detail all the approved mAbs derived from phage display.

Considerations for the Design of Antibody-Based Therapeutics

Antibody-based proteins have become an important class of biologic therapeutics, due in large part to the stability, specificity, and adaptability of the antibody framework. Indeed, antibodies not only have the inherent ability to bind both antigens and endogenous immune receptors but also have proven extremely amenable to protein engineering. Thus, several derivatives of the monoclonal antibody format, including bispecific antibodies, antibody-drug conjugates, and antibody fragments, have demonstrated efficacy for treating human disease, particularly in the fields of immunology and oncology. Reviewed here are considerations for the design of antibody-based therapeutics, including immunological context, therapeutic mechanisms, and engineering strategies. First, characteristics of antibodies are introduced, with emphasis on structural domains, functionally important receptors, isotypic and allotypic differences, and modifications such as glycosylation. Then, aspects of therapeutic antibody design are discussed, including identification of antigen-specific variable regions, choice of expression system, use of multispecific formats, and design of antibody derivatives based on fragmentation, oligomerization, or conjugation to other functional moieties. Finally, strategies to enhance antibody function through protein engineering are reviewed while highlighting the impact of fundamental biophysical properties on protein developability.

Antibody Structure and Function: The Basis for Engineering Therapeutics

Antibodies and antibody-derived macromolecules have established themselves as the mainstay in protein-based therapeutic molecules (biologics). Our knowledge of the structure–function relationships of antibodies provides a platform for protein engineering that has been exploited to generate a wide range of biologics for a host of therapeutic indications. In this review, our basic understanding of the antibody structure is described along with how that knowledge has leveraged the engineering of antibody and antibody-related therapeutics having the appropriate antigen affinity, effector function, and biophysical properties. The platforms examined include the development of antibodies, antibody fragments, bispecific antibody, and antibody fusion products, whose efficacy and manufacturability can be improved via humanization, affinity modulation, and stability enhancement. We also review the design and selection of binding arms, and avidity modulation. Different strategies of preparing bispecific and multispecific molecules for an array of therapeutic applications are included.

Current Progress in CAR-T Cell Therapy for Solid Tumors

Cancer immunotherapy by chimeric antigen receptor-modified T (CAR-T) cells has shown exhilarative clinical efficacy for hematological malignancies. Recently two CAR-T cell based therapeutics, Kymriah (Tisagenlecleucel) and Yescarta (Axicabtagene ciloleucel) approved by US FDA (US Food and Drug Administration) are now used for treatment of B cell acute lymphoblastic leukemia (B-ALL) and diffuse large B-cell lymphoma (DLBCL) respectively in the US. Despite the progresses made in treating hematological malignancies, challenges still remain for use of CAR-T cell therapy to treat solid tumors. In this landscape, most studies have primarily focused on improving CAR-T cells and overcoming the unfavorable effects of tumor microenvironment on solid tumors. To further understand the current status and trend for developing CAR-T cell based therapies for various solid tumors, this review emphasizes on CAR-T techniques, current obstacles, and strategies for application, as well as necessary companion diagnostics for treatment of solid tumors with CAR-T cells.

Optimization of an Antibody Light Chain Framework Enhances Expression, Biophysical Properties and Pharmacokinetics

Efficacy, safety, and manufacturability of therapeutic antibodies are influenced by their biopharmaceutical and biophysical properties. These properties can be optimized by library approaches or rationale protein design. Here, we employed a protein engineering approach to modify the variable domain of the light chain (VL) framework of an oxidized macrophage migration inhibitory factor (oxMIF)-specific antibody. The amendment of the antibody sequence was based on homology to human germline VL genes. Three regions or positions were identified in the VL domain—L1-4, L66, L79—and mutated independently or in combination to match the closest germline V gene. None of the mutations altered oxMIF specificity or affinity, but some variants improved thermal stability, aggregation propensity, and resulted in up to five-fold higher expression. Importantly, the improved biopharmaceutical properties translated into a superior pharmacokinetic profile of the antibody. Thus, optimization of the V domain framework can ameliorate the biophysical qualities of a therapeutic antibody candidate, and as result its manufacturability, and also has the potential to improve pharmacokinetics.

Net charge of antibody complementarity-determining regions is a key predictor of specificity

Specificity is one of the most important and complex properties that is central to both natural antibody function and therapeutic antibody efficacy. However, it has proven extremely challenging to define robust guidelines for predicting antibody specificity. Here we evaluated the physicochemical determinants of antibody specificity for multiple panels of antibodies, including >100 clinical-stage antibodies. Surprisingly, we find that the theoretical net charge of the complementarity-determining regions (CDRs) is a strong predictor of antibody specificity. Antibodies with positively charged CDRs have a much higher risk of low specificity than antibodies with negatively charged CDRs. Moreover, the charge of the entire set of six CDRs is a much better predictor of antibody specificity than the charge of individual CDRs, variable domains (VH or VL) or the entire variable fragment (Fv). The best indicators of antibody specificity in terms of CDR amino acid composition are reduced levels of arginine and lysine and increased levels of aspartic and glutamic acid. Interestingly, clinical-stage antibodies with negatively charged CDRs also have a lower risk for poor biophysical properties in general, including a reduced risk for high levels of self-association. These findings provide powerful guidelines for predicting antibody specificity and for identifying safe and potent antibody therapeutics.

Selecting and engineering monoclonal antibodies with drug-like specificity

Despite the recent explosion in the use of monoclonal antibodies (mAbs) as drugs, it remains a significant challenge to generate antibodies with a combination of physicochemical properties that are optimal for therapeutic applications. We argue that one of the most important and underappreciated drug-like antibody properties is high specificity - defined here as low levels of antibody non-specific and self-interactions - which is linked to low off-target binding and slow antibody clearance in vivo and high solubility and low viscosity in vitro. Here, we review the latest advances in characterizing antibody specificity and elucidating its molecular determinants as well as using these findings to improve the selection and engineering of antibodies with extremely high, drug-like specificity.

In vitro and in silico assessment of the developability of a designed monoclonal antibody library

Despite major advances in antibody discovery technologies, the successful development of monoclonal antibodies (mAbs) into effective therapeutic and diagnostic agents can often be impeded by developability liabilities, such as poor expression, low solubility, high viscosity and aggregation. Therefore, strategies to predict at the early phases of antibody development the risk of late-stage failure of antibody candidates are highly valuable. In this work, we employ the in silico solubility predictor CamSol to design a library of 17 variants of a humanized mAb predicted to span a broad range of solubility values, and we examine their developability potential with a battery of commonly used in vitro and in silico assays. Our results demonstrate the ability of CamSol to rationally enhance mAb developability, and provide a quantitative comparison of in vitro developability measurements with each other and with more resource-intensive solubility measurements, as well as with in silico predictors that offer a potentially faster and cheaper alternative. We observed a strong correlation between predicted and experimentally determined solubility values, as well as with measurements obtained using a panel of in vitro developability assays that probe non-specific interactions. These results indicate that computational methods have the potential to reduce or eliminate the need of carrying out laborious in vitro quality controls for large numbers of lead candidates. Overall, our study provides support to the emerging view that the implementation of in silico tools in antibody discovery campaigns can ensure rapid and early selection of antibodies with optimal developability potential.

Third generation antibody discovery methods: in silico rational design

Owing to their outstanding performances in molecular recognition, antibodies are extensively used in research and applications in molecular biology, biotechnology and medicine. Recent advances in experimental and computational methods are making it possible to complement well-established in vivo (first generation) and in vitro (second generation) methods of antibody discovery with novel in silico (third generation) approaches. Here we describe the principles of computational antibody design and review the state of the art in this field. We then present Modular, a method that implements the rational design of antibodies in a modular manner, and describe the opportunities offered by this approach.

Structure, heterogeneity and developability assessment of therapeutic antibodies

Increasing attention has been paid to developability assessment with the understanding that thorough evaluation of monoclonal antibody lead candidates at an early stage can avoid delays during late-stage development. The concept of developability is based on the knowledge gained from the successful development of approximately 80 marketed antibody and Fc-fusion protein drug products and from the lessons learned from many failed development programs over the last three decades. Here, we reviewed antibody quality attributes that are critical to development and traditional and state-of-the-art analytical methods to monitor those attributes. Based on our collective experiences, a practical workflow is proposed as a best practice for developability assessment including in silico evaluation, extended characterization and forced degradation using appropriate analytical methods that allow characterization with limited material consumption and fast turnaround time.

Identification of B cell epitopes enhanced by protein unfolding and aggregation

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Aggregation of an exemplar therapeutic antibody fragment (scFv) enhances immunogenicity in vivo.

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Epitope mapping reveals immunogenicity is directed to a specific epitope in aggregate species.

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Molecular simulation demonstrates biophysical stress enhances epitope presentation.

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Protein aggregates have distinct immunological profiles to their native counterparts.

Aggregation of therapeutic proteins is a key factor in the generation of unwanted immunogenicity, and can result in reduced serum half-life, neutralization of function and adverse health effects. There is currently little information regarding how aggregates interact with B-cell receptors or cognate antibodies at the protein sequence level, or whether non-native, aggregate-induced epitopes predominate in these interactions. Using an antibody fragment (single chain antibody variable fragment; scFv) that forms aggregates readily at low temperature, anti-scFv IgG antibody responses were generated by intraperitoneal injection of BALB/c strain mice with monomer or aggregate preparations. Aggregate-specific immunosignatures were identified by oligo-peptide microarray fine epitope mapping, using overlapping 15mer peptides based on the linear sequence of scFv, printed onto glass slides. IgG antibodies from mice immunized with aggregated scFv preferentially recognized a patch of overlapping peptides. This region mapped to a β-strand located at the interface between the V_H and V_L domains. Molecular dynamics simulations indicated that the V_L domain is less stable than the V_H domain, suggesting the interface region between the two domains becomes exposed during partial unfolding of the scFv during aggregate formation. These data are consistent with the hypothesis that epitopes from partially unfolded states are revealed, or are more fully exposed, in the aggregated state, and that this can augment the IgG antibody response. This observation offers the theoretical possibility that epitopes preferentially associated with aggregates can be identified from the anti-drug antibody serum IgG response which may, in turn, lead to better methods for detection of anti-drug antibody responses, and improved design of therapeutic proteins to control immunogenicity.

Molecular Recognition between Aβ-Specific Single-Domain Antibody and Aβ Misfolded Aggregates

Aβ is the toxic amyloid polypeptide responsible for Alzheimer's disease (AD). Prevention and elimination of the Aβ misfolded aggregates are the promising therapeutic strategies for the AD treatments. Gammabody, the Aβ-Specific Single-domain (VH) antibody, recognizes Aβ aggregates with high affinity and specificity and reduces their toxicities. Employing the molecular dynamics simulations, we studied diverse gammabody-Aβ recognition complexes to get insights into their structural and dynamic properties and gammabody-Aβ recognitions. Among many heterogeneous binding modes, we focused on two gammabody-Aβ recognition scenarios: recognition through Aβ β-sheet backbone and on sidechain surface. We found that the gammabody primarily uses the complementarity-determining region 3 (CDR3) loop with the grafted Aβ sequence to interact with the Aβ fibril, while CDR1/CDR2 loops have very little contact. The gammabody-Aβ complexes with backbone binding mode are more stable, explaining the gammabody's specificity towards the C-terminal Aβ sequence.

Biophysical characterization and molecular simulation of electrostatically driven self-association of a single-chain antibody

Colloidal protein-protein interactions (PPI) are often expected to impact key behaviors of proteins in solution, such as aggregation rates and mechanisms, aggregate structure, protein solubility, and solution viscosity. PPI of an anti-fluorescein single chain antibody variable fragment (scFv) were characterized experimentally at low to intermediate ionic strength using a combination of static light scattering and sedimentation equilibrium ultracentrifugation. Surprisingly, the results indicated that interactions were strongly net-attractive and electrostatics promoted self-association. Only repulsive interactions were expected based on prior work and calculations based a homology model of a related scFv crystal structure. However, the crystal structure lacks the charged, net-neutral linker sequence. PyRosetta was used to generate a set of scFv structures with different linker conformations, and coarse-grained Monte Carlo simulations were used to evaluate the effect of different linker configurations via second osmotic virial coefficient (B22 ) simulations. The results show that the configuration of the linker has a significant effect on the calculated B22 values, and can result in strong electrostatic attractions between oppositely charged residues on the protein surface. This is particularly relevant for development of non-natural antibody products, where charged linkers and other loop regions may be prevalent. The results also provide a preliminary computational framework to evaluate the effect of unstructured linkers on experimental protein-protein interaction parameters such as B22 .

Understanding and overcoming trade-offs between antibody affinity, specificity, stability and solubility

The widespread use of monoclonal antibodies for therapeutic applications has led to intense interest in optimizing several of their natural properties (affinity, specificity, stability, solubility and effector functions) as well as introducing non-natural activities (bispecificity and cytotoxicity mediated by conjugated drugs). A common challenge during antibody optimization is that improvements in one property (e.g., affinity) can lead to deficits in other properties (e.g., stability). Here we review recent advances in understanding trade-offs between different antibody properties, including affinity, specificity, stability and solubility. We also review new approaches for co-optimizing multiple antibody properties and discuss how these methods can be used to rapidly and systematically generate antibodies for a wide range of applications.

Engineering of Fc Fragments with Optimized Physicochemical Properties Implying Improvement of Clinical Potentials for Fc-Based Therapeutics

Therapeutic monoclonal antibodies and Fc-fusion proteins are successfully used in treatment of various diseases mainly including cancer, immune disease, and viral infection, which belong to the Fc-based therapeutics. In recent years, engineered Fc-derived antibody domains have also shown potential for Fc-based therapeutics. To increase the druggability of Fc-based therapeutic candidates, many efforts have been made in optimizing physicochemical properties and functions mediated by Fc fragment. The desired result is that we can simultaneously obtain Fc variants with increased physicochemical properties in vitro and capacity of mediating appropriate functions in vivo. However, changes of physicochemical properties of Fc may result in alternation of Fc-mediated functions and vice versa, which leads to undesired outcomes for further development of Fc-based therapeutics. Therefore, whether modified Fc fragments are suitable for achievement of expected clinical results or not needs to be seriously considered. Now, this question comes to be noticed and should be figured out to make better translation from the results of laboratory into clinical applications. In this review, we summarize different strategies on engineering physicochemical properties of Fc, and preliminarily elucidate the relationships between modified Fc in vitro and the subsequent therapeutic influence in vivo.

AGGRESCAN3D: Toward the Prediction of the Aggregation Propensities of Protein Structures

Protein aggregation is responsible for the onset and spread of many human diseases, ranging from neurodegenerative disorders to cancer and diabetes. Moreover, it is one of the major bottlenecks for the production of protein-based therapeutics such as antibodies or enzymes. AGGRESCAN3D (A3D) is a web server aimed to identify and evaluate structural aggregation prone regions, overcoming the limitations of sequence-based algorithms in the prediction of the aggregation propensity of globular proteins. A3D allows the redesign of protein solubility by predicting in silico the impact of mutations and protein conformational fluctuations on the aggregation of native polypeptides.

Biophysical and Sequence-Based Methods for Identifying Monovalent and Bivalent Antibodies with High Colloidal Stability

In vitro antibody discovery and/or affinity maturation are often performed using antibody fragments (Fabs), but most monovalent Fabs are reformatted as bivalent IgGs (monoclonal antibodies, mAbs) for therapeutic applications. One problem related to reformatting antibodies is that the bivalency of mAbs can lead to increased antibody self-association and poor biophysical properties (e.g., reduced antibody solubility and increased viscosity). Therefore, it is important to identify monovalent Fabs early in the discovery and/or optimization process that will display favorable biophysical properties when reformatted as bivalent mAbs. Here we demonstrate a facile approach for evaluating Fab self-association in a multivalent assay format that is capable of identifying antibodies with low self-association and favorable colloidal properties when reformatted as bivalent mAbs. Our approach (self-interaction nanoparticle spectroscopy, SINS) involves immobilizing Fabs on gold nanoparticles in a multivalent format (multiple Fabs per nanoparticle) and evaluating their self-association behavior via shifts in the plasmon wavelength or changes in the absorbance values. Importantly, we find that SINS measurements of Fab self-association are correlated with self-interaction measurements of bivalent mAbs and are useful for identifying antibodies with favorable biophysical properties. Moreover, the significant differences in the levels of self-association detected for Fabs and mAbs with similar frameworks can be largely explained by the physicochemical properties of the complementarity-determining regions (CDRs). Comparison of the properties of the CDRs in this study relative to those of approved therapeutic antibodies reveals several key factors (net charge, fraction of charged residues, and presence of self-interaction motifs) that strongly influence antibody self-association behavior. Increased positive charge in the CDRs was observed to correlate with increased risk of high self-association for the mAbs in this study and clinical-stage antibodies. We expect that these findings will be useful for improving the development of therapeutic antibodies that are well suited for high concentration applications.

Charge Shielding Prevents Aggregation of Supercharged GFP Variants at High Protein Concentration

Understanding protein stability is central to combatting protein aggregation diseases and developing new protein therapeutics. At the high concentrations often present in biological systems, purified proteins can exhibit undesirable high solution viscosities and poor solubilities mediated by short-range electrostatic and hydrophobic protein-protein interactions. The interplay between protein amino acid sequence, protein structure, and solvent conditions to minimize protein-protein interactions is key to designing well-behaved pharmaceutical proteins. However, theoretical approaches have yet to yield a general framework to address these problems. Here, we analyzed the high concentration behavior of superfolder GFP (sfGFP) and two supercharged sfGFP variants engineered to have formal charges of -18 or +15. Under low cosolute conditions, sfGFP and the -18 variant formed a gel or phase separated at ∼10 mg/mL. Under conditions that screen surface charges, including formulations with high histidine or high NaCl concentrations, all three variants attained concentrations up to 250 mg/mL with moderate viscosities. Moreover, all three variants exhibited very similar viscosity-concentration profiles over this range. This effect was not mimicked by high sugar concentrations that exert excluded-volume effects without shielding charge. Collectively, these data demonstrate that charge shielding neutralizes not only long-range electrostatic interactions but also, surprisingly, short-range electrostatic effects due to surface charge anisotropy. This work shows that supercharged sfGFP behavior under high ionic strength is largely determined by particle geometry, a conclusion that is supported by colloid models and may be applicable to pharmaceutically relevant proteins.

Facile Affinity Maturation of Antibody Variable Domains Using Natural Diversity Mutagenesis

The identification of mutations that enhance antibody affinity while maintaining high antibody specificity and stability is a time-consuming and laborious process. Here, we report an efficient methodology for systematically and rapidly enhancing the affinity of antibody variable domains while maximizing specificity and stability using novel synthetic antibody libraries. Our approach first uses computational and experimental alanine scanning mutagenesis to identify sites in the complementarity-determining regions (CDRs) that are permissive to mutagenesis while maintaining antigen binding. Next, we mutagenize the most permissive CDR positions using degenerate codons to encode wild-type residues and a small number of the most frequently occurring residues at each CDR position based on natural antibody diversity. This mutagenesis approach results in antibody libraries with variants that have a wide range of numbers of CDR mutations, including antibody domains with single mutations and others with tens of mutations. Finally, we sort the modest size libraries (~10 million variants) displayed on the surface of yeast to identify CDR mutations with the greatest increases in affinity. Importantly, we find that single-domain (V_HH) antibodies specific for the α-synuclein protein (whose aggregation is associated with Parkinson’s disease) with the greatest gains in affinity (>5-fold) have several (four to six) CDR mutations. This finding highlights the importance of sampling combinations of CDR mutations during the first step of affinity maturation to maximize the efficiency of the process. Interestingly, we find that some natural diversity mutations simultaneously enhance all three key antibody properties (affinity, specificity, and stability) while other mutations enhance some of these properties (e.g., increased specificity) and display trade-offs in others (e.g., reduced affinity and/or stability). Computational modeling reveals that improvements in affinity are generally not due to direct interactions involving CDR mutations but rather due to indirect effects that enhance existing interactions and/or promote new interactions between the antigen and wild-type CDR residues. We expect that natural diversity mutagenesis will be useful for efficient affinity maturation of a wide range of antibody fragments and full-length antibodies.

Rapid and accurate in silico solubility screening of a monoclonal antibody library

Antibodies represent essential tools in research and diagnostics and are rapidly growing in importance as therapeutics. Commonly used methods to obtain novel antibodies typically yield several candidates capable of engaging a given target. The development steps that follow, however, are usually performed with only one or few candidates since they can be resource demanding, thereby increasing the risk of failure of the overall antibody discovery program. In particular, insufficient solubility, which may lead to aggregation under typical storage conditions, often hinders the ability of a candidate antibody to be developed and manufactured. Here we show that the selection of soluble lead antibodies from an initial library screening can be greatly facilitated by a fast computational prediction of solubility that requires only the amino acid sequence as input. We quantitatively validate this approach on a panel of nine distinct monoclonal antibodies targeting nerve growth factor (NGF), for which we compare the predicted and measured solubilities finding a very close match, and we further benchmark our predictions with published experimental data on aggregation hotspots and solubility of mutational variants of one of these antibodies.

Monoclonal antibodies: technologies for early discovery and engineering

Antibodies are essential in modern life sciences biotechnology. Their architecture and diversity allow for high specificity and affinity to a wide array of biochemicals. Combining monoclonal antibody (mAb) technology with recombinant DNA and protein expression links antibody genotype with phenotype. Yet, the ability to select and screen for high affinity binders from recombinantly-displayed, combinatorial libraries unleashes the true power of mAbs and a flood of clinical applications. The identification of novel antibodies can be accomplished by a myriad of in vitro display technologies from the proven (e.g. phage) to the emerging (e.g. mammalian cell and cell-free) based on affinity binding as well as function. Lead candidates can be further engineered for increased affinity and half-life, reduced immunogenicity and/or enhanced manufacturing, and storage capabilities. This review begins with antibody biology and how the structure and genetic machinery relate to function, diversity, and in vivo affinity maturation and follows with the general requirements of (therapeutic) antibody discovery and engineering with an emphasis on in vitro display technologies. Throughout, we highlight where antibody biology inspires technology development and where high-throughput, "big data" and in silico strategies are playing an increasing role. Antibodies dominate the growing class of targeted therapeutics, alone or as bioconjugates. However, their versatility extends to research, diagnostics, and beyond.

Tumor-targeting domains for chimeric antigen receptor T cells

Immunotherapy with chimeric antigen receptor (CAR) T cells has been advancing steadily in clinical trials. Since the ability of engineered T cells to recognize intended tumor-associated targets is crucial for the therapeutic success, antigen-binding domains play an important role in shaping T-cell responses. Single-chain antibody and T-cell receptor fragments, natural ligands, repeat proteins, combinations of the above and universal tag-specific domains have all been used in the antigen-binding moiety of chimeric receptors. Here we outline the advantages and disadvantages of different domains, discuss the concepts of affinity and specificity, and highlight the recent progress of each targeting strategy.

Computational study of aggregation mechanism in human lysozyme[D67H]

Aggregation of proteins is an undesired phenomena that affects both human health and bioengineered products such as therapeutic proteins. Finding preventative measures could be facilitated by a molecular-level understanding of dimer formation, which is the first step in aggregation. Here we present a molecular dynamics (MD) study of dimer formation propensity in human lysozyme and its D67H variant. Because the latter protein aggregates while the former does not, they offer an ideal system for testing the feasibility of the proposed MD approach which comprises three stages: i) partially unfolded conformers involved in dimer formation are generated via high-temperature MD simulations, ii) potential dimer structures are searched using docking and refined with MD, iii) free energy calculations are performed to find the most stable dimer structure. Our results provide a detailed explanation for how a single mutation (D67H) turns human lysozyme from non-aggregating to an aggregating protein. Conversely, the proposed method can be used to identify the residues causing aggregation in a protein, which can be mutated to prevent it.