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

Optimization of synthetic human VH affinity and solubility through in vitro affinity maturation and minimal camelization

An attractive feature of human VHs over camelid VHHs as immunotherapeutics is their perceived lower risk of immunogenicity. While human VHs can readily be obtained from synthetic phage display libraries, they often suffer from low affinity and poor solubility compared to VHHs derived from immune libraries. Using SARS-CoV-2 spike protein as a model antigen, we screened a synthetic human VH phage display library and identified a diverse set of antigen-specific VHs. However, the VHs exhibited low affinity, and many had low solubility; that is, they were prone to aggregation. To explore the feasibility of improving the affinity, we subjected a representative VH to in vitro affinity maturation. We created a yeast surface display library of VH variants employing a site-saturated mutagenesis approach targeting complementarity-determining regions and selected against the target antigen. Next-generation sequencing of the selected variants, combined with structural modeling, identified a set of VHs as potentially improved candidates. Characterization of these candidates revealed several VHs with improved affinities of up to 100-fold (KDs as low as 3 nM) and potent neutralization capabilities; however, they still showed significant aggregation. By introducing as few as two camelid residues into the framework region 2 of a high-affinity VH (a process referred to as camelization), we were able to completely solubilize the VH without compromising its affinity and other important attributes, including thermostability and protein A binding. This study demonstrates the feasibility of generating high-affinity, -solubility, and -stability human VHs from synthetic libraries through a combination of in vitro affinity maturation and minimal camelization.

Antibody-Nanoparticle Conjugates in Therapy: Combining the Best of Two Worlds

Monoclonal antibodies (mAbs) and antibody fragments have revolutionized medicine as highly specific binding agents and inhibitors. At the same time, several types of nanomaterials, including liposomes, lipid nanoparticles (NPs), polymersomes, metal and metal oxide NPs, and protein nanostructures, are increasingly utilized and explored for therapeutic potential due to their versatility, chemical and physical properties, and tunability. However, nanomaterials alone often lack specificity, leading to relatively low efficacy and/or high toxicity. To address this problem, a rapidly emerging area is antibody-nanomaterial conjugates (ANCs), which combine the precise targeting specificity of antibodies with the effector functionality of the nanomaterial. In this review, we give a brief introduction to mAbs and major conjugation techniques, describe major classes of nanomaterials being studied for therapeutic potential, and review the literature on ANCs of each class. Special focus is given to emerging applications including ANCs addressing the blood-brain barrier, ANCs delivering nucleic acids, and light-activated ANCs. While many disease targets are related to cancer, ANCs are also under development to address autoimmune, neurological, and infectious diseases. While important challenges remain, ANCs are poised to become a next-generation therapeutic technology.

Pareto-optimal sampling for multi-objective protein sequence design

Supervised machine learning (ML) has significantly advanced sequence-based protein property prediction. However, its inverse application, designing protein sequences with desired properties, remains under-explored. The challenges in sequence design stem from the vast search space and the rugged protein fitness landscape. In this work, we present MosPro, an efficient ML algorithm for property-guided protein sequence design. We frame sequence design as a discrete sampling problem. Utilizing a pre-trained differentiable ML model that predicts properties of sequences, MosPro shapes a distribution that assigns high probability mass to regions for high-property sequences. To generate designs, MosPro efficiently samples sequences from this constructed distribution. We further develop a Pareto optimization algorithm to propose sequences that are simultaneously optimized for multiple properties. Evaluations on experimental fitness landscapes demonstrated that MosPro generates sequences that optimally trade off multiple desiderata. Our results suggested an unparalleled potential of generative ML for efficient and controllable design for functional proteins.

AntiFold: improved structure-based antibody design using inverse folding

Summary

The design and optimization of antibodies requires an intricate balance across multiple properties. Protein inverse folding models, capable of generating diverse sequences folding into the same structure, are promising tools for maintaining structural integrity during antibody design. Here, we present AntiFold, an antibody-specific inverse folding model, fine-tuned from ESM-IF1 on solved and predicted antibody structures. AntiFold outperforms existing inverse folding tools on sequence recovery across complementarity-determining regions, with designed sequences showing high structural similarity to their solved counterpart. It additionally achieves stronger correlations when predicting antibody-antigen binding affinity in a zero-shot manner. AntiFold assigns low probabilities to mutations that disrupt antigen binding, synergizing with protein language model residue probabilities, and demonstrates promise for guiding antibody optimization while retaining structure-related properties.

Availability and implementation

AntiFold is freely available under the BSD 3-Clause as a web server (https://opig.stats.ox.ac.uk/webapps/antifold/) and pip-installable package (https://github.com/oxpig/AntiFold).

Potential risk factors of protein aggregation in syringe handling during antibody drug dilution for intravenous administration

Protein aggregation, a major concern in biopharmaceutical quality control, can be accelerated by various stresses during clinical handling. This study investigated potential aggregation risk factors during dilution process with syringe handling for intravenous administration. Using γ-globulin and IgG solutions as surrogate models of antibody therapeutics, we examined the effects of high sliding speeds and piston operations of the syringe on protein aggregation during saline dilution. Our results revealed that elevated sliding speeds promoted proteinaceous subvisible and/or visible particle formation, which was further enhanced by piston operations. The proteinaceous particle formation was presumed to be caused by fine air bubbles generated due to rapid pressure changes arising from shear stress during needle passage. While polysorbate 20 effectively suppressed the particle formation induced by the syringe handling at sufficient concentrations, its protective effect became inadequate under high dilution conditions, as exemplified by those encountered in low-body-weight patient protocols. Different proteins exhibited varying susceptibility to the syringe-induced aggregation. These findings demonstrate that the combination of syringe handling and dilution conditions could significantly impact protein stability during clinical handling, particularly for less stable biopharmaceuticals. A deeper understanding of these factors is crucial for developing more robust formulations and establishing safer handling practices for biopharmaceuticals.

Differential roles of human CD4+ and CD8+ regulatory T cells in controlling self-reactive immune responses

Here we analyzed the relative contributions of CD4+ regulatory T cells expressing Forkhead box protein P3 (FOXP3) and CD8+ regulatory T cells expressing killer cell immunoglobulin-like receptors to the control of autoreactive T and B lymphocytes in human tonsil-derived immune organoids. FOXP3 and GZMB respectively encode proteins FOXP3 and granzyme B, which are critical to the suppressive functions of CD4+ and CD8+ regulatory T cells. Using CRISPR-Cas9 gene editing, we were able to achieve a reduction of ~90-95% in the expression of these genes. FOXP3 knockout in tonsil T cells led to production of antibodies against a variety of autoantigens and increased the affinity of influenza-specific antibodies. By contrast, GZMB knockout resulted in an increase in follicular helper T cells, consistent with the ablation of CD8+ regulatory T cells observed in mouse models, and a marked expansion of autoreactive CD8+ and CD4+ T cells. These findings highlight the distinct yet complementary roles of CD8+ and CD4+ regulatory T cells in regulating cellular and humoral responses to prevent autoimmunity.

The Emergence of Oligonucleotide Building Blocks in the Multispecific Proximity-Inducing Drug Toolbox of Destruction

Oligonucleotides are a rapidly emerging class of therapeutics. Their most well-known examples are informational drugs that modify gene expression by binding mRNA. Despite inducing proximity between biological machinery and mRNA when applied to modulating gene expression, oligonucleotides are not typically labeled as "proximity-inducing" in literature. Yet, they have recently been explored as building blocks for multispecific proximity-inducing drugs (MPIDs). MPIDs are unique because they can direct endogenous biological machinery to destroy targeted molecules and cells, in contrast to traditional drugs that inhibit only their functions. The unique mechanism of action of MPIDs has enabled the targeting of previously "undruggable" molecular entities that cannot be effectively inhibited. However, the development of MPIDs must ensure that these molecules will selectively direct a potent, destruction-based mechanism of action toward intended targets over healthy tissues to avoid causing life-threatening toxicities. Oligonucleotides have emerged as promising building blocks for the design of MPIDs because they are sequence-controlled molecules that can be rationally designed to program multispecific binding interactions. In this Review, we examine the emergence of oligonucleotide-containing MPIDs in the proximity induction space, which has been dominated by antibody and small molecule MPID modalities. Moreover, examples of oligonucleotides developed as MPID candidates in immunotherapy and protein degradation are discussed to demonstrate the utility of oligonucleotides in expanding the scope and selectivity of the MPID toolbox. Finally, we discuss the utility of programming "AND" gates into oligonucleotide scaffolds to encode conditional responses that have the potential to be incorporated into MPIDs, which can further enhance their selectivity, thus increasing the scope of this drug category.

A Strategy for Simultaneous Engineering of Interspecies Cross-Reactivity, Thermostability, and Expression of a Bispecific 5T4 x CD3 DART® Molecule for Treatment of Solid Tumors

Background: Bispecific antibodies represent a promising class of biologics for cancer treatment. However, their dual specificity and complex structure pose challenges in the engineering process, often resulting in molecules with good functional but poor physicochemical properties. Method: To overcome limitations in the properties of an anti-5T4 x anti-CD3 (α5T4 x αCD3) DART molecule, a phage-display method was developed, which succeeded in simultaneously engineering cross-reactivity to the cynomolgus 5T4 ortholog, improving thermostability and the elevating expression level. Results: This approach generated multiple DART molecules that exhibited significant improvements in all three properties. The lead DART molecule demonstrated potent in vitro and in vivo anti-tumor activity. Although its clearance in human FcRn-transgenic mice was comparable to that of the parental molecule, faster clearance was observed in cynomolgus monkeys. The lead α5T4 x αCD3 DART molecule displayed no evidence of off-target binding or polyspecificity, suggesting that the increased affinity for the target may account for its accelerated clearance in cynomolgus monkeys. Conclusions: This may reflect target-mediated drug disposition (TMDD), a potential limitation of targeting 5T4, despite its limited expression in healthy tissues.

Use of Affimer technology for inhibition of α2-antiplasmin and enhancement of fibrinolysis

ABSTRACT

Hypofibrinolysis is a documented abnormality in conditions with high risk of vascular occlusion. A key inhibitor of fibrinolysis is α2-antiplasmin (α2AP), and we hypothesize that the Affimer technology, comprising small conformational proteins with 2 9-amino-acid variable regions, can be used to modulate α2AP activity and facilitate fibrinolysis. Using a phage display system, a library of Affimers was screened against α2AP. A total of 28 α2AP-specific Affimers were isolated, of which 1, termed Affimer A11, inhibited protein function and enhanced fibrinolysis. Affimer A11 displayed a monomeric form and consistently reduced the lysis time of clots made from plasma samples of individuals with type 2 diabetes mellitus (n = 15; from 150.8 ± 100.9 to 109.8 ± 104.8 minutes) and those with cardiovascular disease (n = 15; 117.6 ± 40.6 to 79.7 ± 33.3 minutes; P < .01 for both groups). The effects of A11 on fibrinolysis were maintained when clots were made from whole blood samples. Mechanistic studies demonstrated that A11 did not affect clot structure or interfere with the incorporation of α2AP into fibrin networks but significantly enhanced plasmin activity and accelerated plasmin generation. Affimer A11 reduced α2AP binding to plasmin(ogen), whereas molecular modeling demonstrated interactions with α2AP in an area responsible for binding to plasminogen, explaining the effects on both plasmin activity and generation. Affimer A11, at 0.15 to 0.60 mg/mL, had the ability to bind 70% to 90% of plasma α2AP. In conclusion, we demonstrate that Affimers are viable tools for inhibiting α2AP function and facilitating fibrinolysis, making them potential future therapeutic agents to reduce thrombosis risk.

Recent Advances in Mass Spectrometry-Based Protein Interactome Studies

The foundation of all biological processes is the network of diverse and dynamic protein interactions with other molecules in cells known as the interactome. Understanding the interactome is crucial for elucidating molecular mechanisms but has been a longstanding challenge. Recent developments in mass spectrometry (MS)-based techniques, including affinity purification, proximity labeling, cross-linking, and co-fractionation mass spectrometry (MS), have significantly enhanced our abilities to study the interactome. They do so by identifying and quantifying protein interactions yielding profound insights into protein organizations and functions. This review summarizes recent advances in MS-based interactomics, focusing on the development of techniques that capture protein-protein, protein-metabolite, and protein-nucleic acid interactions. Additionally, we discuss how integrated MS-based approaches have been applied to diverse biological samples, focusing on significant discoveries that have leveraged our understanding of cellular functions. Finally, we highlight state-of-the-art bioinformatic approaches for predictions of interactome and complex modeling, as well as strategies for combining experimental interactome data with computation methods, thereby enhancing the ability of MS-based techniques to identify protein interactomes. Indeed, advances in MS technologies and their integrations with computational biology provide new directions and avenues for interactome research, leveraging new insights into mechanisms that govern the molecular architecture of living cells and, thereby, our comprehension of biological processes.

Reduction of Chemoresistance by Claudin-14-Targeting Peptide in Human Colorectal Cancer Cells

The expression of claudins (CLDNs), major components of tight junctions (TJs), is abnormal in various solid tumors. CLDN14 is highly expressed in human colorectal cancer (CRC) tissues and confers chemoresistance. CLDN14 may become a novel therapeutic target for CRC, but CLDN14-targeting drugs have not been developed. Here, we searched for a CLDN14-targeting peptide, which can suppress CLDN14 expression and chemoresistance using human CRC-derived DLD-1 and LoVo cells. Among some short peptides which mimic the second extracellular loop structure of CLDN14, PSGMK most strongly suppressed the protein expression of CLDN14. The mRNA expression of other endogenous TJ components was unchanged by PSGMK. The PSGMK-induced reduction of CLDN14 protein was inhibited by chloroquine, a lysosome inhibitor, and monodansylcadaverine, a clathrin-dependent endocytosis inhibitor, indicating that PSGMK may enhance endocytosis and lysosomal degradation of CLDN14. In a three-dimensional culture model, the oxidative stress was significantly reduced by PSGMK, whereas hypoxia stress was not. Furthermore, the expression levels of nuclear factor erythroid 2-related factor 2, an oxidative stress response factor, and its target genes were decreased by PSGMK. These results suggest that PSGMK relieves stress conditions in spheroids. The cell viability of spheroids was decreased by anticancer drugs such as doxorubicin and oxaliplatin, which was exaggerated by the cotreatment with PSGMK. Our data indicate that CLDN14-targeting peptide, PSGMK has an anti-chemoresistance effect in CRC cells.

Novel Strategy of Antibody Affinity Maturation and Enhancement of Nucleolin-Mediated Antibody-Dependent Cellular Cytotoxicity Against Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a clinically aggressive subtype of breast cancer that remains an unmet medical need. Because TNBC cells do not express the most common markers of breast cancers, there is an active search for novel molecular targets in triple-negative tumors. Additionally, this subtype of breast cancer presents strong immunogenic characteristics which have been encouraging the development of immunotherapeutic approaches against the disease. In this context, nucleolin arises as a promising target for immunotherapy against TNBC. Our group has previously developed an anti-nucleolin VHH-Fc antibody capable of eliciting antibody-dependent cellular cytotoxicity (ADCC). Moreover, we constructed and characterized an antibody library, that was screened against nucleolin-overexpressing cells, originating an enriched anti-nucleolin antibody pool. In this work, a strategy to select individual clones from the pool was designed, combining NGS data with 3D modeling. Two antibodies demonstrated a significant 4.4- and 6.1-fold increase in binding to nucleolin-overexpressing and TNBC cells, and an improvement in affinity to the sub-micromolar range (0.19 µM and 83.69 nM). Additionally, an increment in 4.6- and 3.1-fold in ADCC activity against respective cell lines was observed for the M2 antibody clone. Herein, the affinity maturation strategy developed was validated and corroborated a positive, but not proportional, correlation between antibody binding, affinity, and ADCC.

Bioorthogonal Chemistry: Enzyme Immune and Protein Capture for Enhanced LC-MS Bioanalysis

Immunocapture liquid chromatography-mass spectrometry (IC-LC-MS) bioanalysis has become an indispensable technique across various scientific disciplines, ranging from drug discovery to clinical diagnostics. While traditional immunocapture techniques have proven to be effective, they often encounter limitations in sensitivity, specificity, and compatibility with MS analysis. Chemoenzymatic immunocapture and protein capture (IPC) offers a promising solution, combining the high specificity of antibodies or proteins with the versatility of enzymatic and chemical modifications. This Review explores the foundational principles of chemoenzymatic IPC and examines various modification strategies including bioorthogonal click-chemistry, enzymatic-tagging, and HaloTag/CLIP-tag. Recent advancements in chemoenzymatic IPC techniques have significantly expanded their applicability to a diverse range of biomolecules including small molecules, peptides, RNAs, and proteins. This Review focuses on improvements in analytical performance achieved through these innovative approaches. Moreover, we discuss the broad applications of chemoenzymatic immunocapture in drug discovery, clinical diagnostics, and environmental analysis and explore its potential for future advancements in bioanalysis. We propose a novel solid-phase chemoenzymatic IPC assay (SCEIA) that effectively utilizes bioorthogonal click chemistry and chemoenzymatic approaches for efficient IPC and target analyte release. In summary, chemoenzymatic IPC represents a transformative paradigm shift in IC-LC-MS bioanalysis. By overcoming the limitations of traditional IPC techniques, this approach paves the way for more robust, sensitive, and versatile analytical workflows.

Mass Spectrometry-Based Proteomics for Assessing Epitranscriptomic Regulations

Epitranscriptomics is a rapidly evolving field that explores chemical modifications in RNA and how they contribute to dynamic and reversible regulations of gene expression. These modifications, for example, N6-methyladenosine (m6A), are crucial in various RNA metabolic processes, including splicing, stability, subcellular localization, and translation efficiency of mRNAs. Mass spectrometry-based proteomics has become an indispensable tool in unraveling the complexities of epitranscriptomics, offering high-throughput, precise protein identification, and accurate quantification of differential protein expression. Over the past two decades, advances in mass spectrometry, including the improvement of high-resolution mass spectrometers and innovative sample preparation methods, have allowed researchers to perform in-depth analyses of epitranscriptomic regulations. This review focuses on the applications of bottom-up proteomics in the field of epitranscriptomics, particularly in identifying and quantifying epitranscriptomic reader, writer, and eraser (RWE) proteins and in characterizing their functions, posttranslational modifications, and interactions with other proteins. Together, by leveraging modern proteomics, researchers can gain deep insights into the intricate regulatory networks of RNA modifications, advancing fundamental biology, and fostering potential therapeutic applications.

Production Technologies for Recombinant Antibodies: Insights into Eukaryotic, Prokaryotic, and Transgenic Expression Systems

Recombinant antibodies, a prominent class of recombinant proteins, are witnessing substantial growth in research and diagnostics. Recombinant antibodies are being produced employing diverse hosts ranging from highly complex eukaryotes, for instance, mammalian cell lines (and insects, fungi, yeast, etc.) to unicellular prokaryotic models like gram-positive and gram-negative bacteria. This review delves into these production methods, highlighting approaches like antibody phage display that employs bacteriophages for gene library creation. Recent studies emphasize monoclonal antibody generation through hybridoma technology, utilizing hybridoma cells from myeloma and B-lymphocytes. Transgenic plants and animals have emerged as sources for polyclonal and monoclonal antibodies, with transgenic animals preferred due to their human-like post-translational modifications and reduced immunogenicity risk. Chloroplast expression offers environmental safety by preventing transgene contamination in pollen. Diverse production technologies, such as stable cell pools and clonal cell lines, are available, followed by purification via techniques like affinity chromatography. The burgeoning applications of recombinant antibodies in medicine have led to their large-scale industrial production.

A novel sustainable immunoassay for sensitive detection of atrazine based on the anti-idiotypic nanobody and recombinant full-length antibody

Immunoassays have been widely used to determine small-molecule compounds in food and the environment, meeting the challenge of obtaining false positive or negative results because of the variance in the batches of antibodies and antigens. To resolve this problem, atrazine (ATR) was used as a target, and anti-idiotypic nanobodies for ATR (AI-Nbs) and a recombinant full-length antibody against ATR (ATR-rAb) were prepared for the development of a sustainable enzyme-linked immunosorbent assay (ELISA). AI-Nb-7, AI-Nb-58, and AI-Nb-66 were selected from an immune phage display library. ATR-rAb was produced in mammalian HEK293 (F) cells. Among the four detection methods explored, the assay using AI-Nb-66 as a coating antigen and ATR-rAb as a detection reagent yielded a half maximal inhibitory concentration (IC50) of 1.66 ng mL-1 for ATR and a linear range of 0.35-8.73 ng mL-1. The cross-reactivity of the assay to ametryn was 64.24%, whereas that to terbutylazine was 38.20%. Surface plasmon resonance (SPR) analysis illustrated that these cross-reactive triazine compounds can bind to ATR-rAb to varying degrees at high concentrations; however, the binding/dissociation kinetic curves and the response values at the same concentration are different, which results in differences in cross-reactivity. Homology modeling and molecular docking revealed that the triazine ring is vital in recognizing triazine compounds. The proposed immunoassay exhibited acceptable recoveries of 84.40-105.36% for detecting fruit, vegetables, and black tea. In conclusion, this study highlights a new strategy for developing sustainable immunoassays for detecting trace pesticide contaminants.

Native ion mobility-mass spectrometry reveals the binding mechanisms of anti-amyloid therapeutic antibodies

One of the most important attributes of anti-amyloid antibodies is their selective binding to oligomeric and amyloid aggregates. However, current methods of examining the binding specificities of anti-amyloid β (Aβ) antibodies have limited ability to differentiate between complexes that form between antibodies and monomeric or oligomeric Aβ species during the dynamic Aβ aggregation process. Here, we present a high-resolution native ion-mobility mass spectrometry (nIM-MS) method to investigate complexes formed between a variety of Aβ oligomers and three Aβ-specific IgGs, namely two antibodies with relatively high conformational specificity (aducanumab and A34) and one antibody with low conformational specificity (crenezumab). We found that crenezumab primarily binds Aβ monomers, while aducanumab preferentially binds Aβ monomers and dimers and A34 preferentially binds Aβ dimers, trimers, and tetrameters. Through collision induced unfolding (CIU) analysis, our data indicate that antibody stability is increased upon Aβ binding and, surprisingly, this stabilization involves the Fc region. Together, we conclude that nIM-MS and CIU enable the identification of Aβ antibody binding stoichiometries and provide important details regarding antibody binding mechanisms.

Specific features of a scaffolding antibody light chain

The antigen-binding sites in conventional antibodies are formed by hypervariable complementarity-determining regions (CDRs) from both heavy chains (HCs) and light chains (LCs). A deviation from this paradigm is found in a subset of bovine antibodies that bind antigens via an ultra-long CDR. The HCs bearing ultra-long CDRs pair with a restricted set of highly conserved LCs that convey stability to the antibody. Despite the importance of these LCs, their specific features remained unknown. Here, we show that the conserved bovine LC found in antibodies with ultra-long CDRs exhibits a distinct combination of favorable physicochemical properties such as good secretion from mammalian cells, strong dimerization, high stability, and resistance to aggregation. These physicochemical traits of the LCs arise from a combination of the specific sequences in the germline CDRs and a lambda LC framework. In addition to understanding the molecular architecture of antibodies with ultra-long CDRs, our findings reveal fundamental insights into LC characteristics that can guide the design of antibodies with improved properties.

Generation and characterization of nanobodies targeting human pepsinogens

Human pepsinogens (mainly pepsinogen I and pepsinogen II) are the major inactive precursor forms of the digestive enzyme pepsin which play a crucial role in protein digestion. The levels and ratios of human pepsinogens have demonstrated potential as diagnostic biomarkers for gastrointestinal diseases, particularly gastric cancer. Nanobodies are promising tools for the treatment and diagnosis of diseases, owing to their unique recognition properties. In this study, recombinant human pepsinogens proteins were expressed and purified as immunized antigens. We constructed a VHH phage library and identified several nanobodies via phage display bio-panning. We determined the binding potency and cross-reactivity of these nanobodies. Our study provides technical support for developing immunodiagnostic reagents targeting human pepsinogens.

Unveiling the affinity-stability relationship in anti-measles virus antibodies: a computational approach for hotspots prediction

Recent years have seen an uptick in the use of computational applications in antibody engineering. These tools have enhanced our ability to predict interactions with antigens and immunogenicity, facilitate humanization, and serve other critical functions. However, several studies highlight the concern of potential trade-offs between antibody affinity and stability in antibody engineering. In this study, we analyzed anti-measles virus antibodies as a case study, to examine the relationship between binding affinity and stability, upon identifying the binding hotspots. We leverage in silico tools like Rosetta and FoldX, along with molecular dynamics (MD) simulations, offering a cost-effective alternative to traditional in vitro mutagenesis. We introduced a pattern in identifying key residues in pairs, shedding light on hotspots identification. Experimental physicochemical analysis validated the predicted key residues by confirming significant decrease in binding affinity for the high-affinity antibodies to measles virus hemagglutinin. Through the nature of the identified pairs, which represented the relative hydropathy of amino acid side chain, a connection was proposed between affinity and stability. The findings of the study enhance our understanding of the interactions between antibody and measles virus hemagglutinin. Moreover, the implications of the observed correlation between binding affinity and stability extend beyond the field of anti-measles virus antibodies, thereby opening doors for advancements in antibody research.

A bioengineered anti-VEGF protein with high affinity and high concentration for intravitreal treatment of wet age-related macular degeneration

Intravitreal (IVT) injection of anti-vascular endothelial growth factor (anti-VEGF) has greatly improved the treatment of many retinal disorders, including wet age-related macular degeneration (wAMD), which is the third leading cause of blindness. However, frequent injections can be difficult for patients and may lead to various risks such as elevated intraocular pressure, infection, and retinal detachment. To address this issue, researchers have found that IVT injection of anti-VEGF proteins at their maximally viable concentration and dose can be an effective strategy. However, the intrinsic protein structure can limit the maximum concentration due to stability and solution viscosity. To overcome this challenge, we developed a novel anti-VEGF protein called nanoFc by fusing anti-VEGF nanobodies with a crystallizable fragment (Fc). NanoFc has demonstrated high binding affinity to VEGF165 through multivalency and potent bioactivity in various bioassays. Furthermore, nanoFc maintains satisfactory chemical and physical stability at 4°C over 1 month and is easily injectable at concentrations up to 200 mg/mL due to its unique architecture that yields a smaller shape factor. The design of nanoFc offers a bioengineering strategy to ensure both strong anti-VEGF binding affinity and high protein concentration, with the goal of reducing the frequency of IV injections.

Re-Engineering Therapeutic Anti-Aβ Monoclonal Antibody to Target Amyloid Light Chain

Amyloidosis involves the deposition of misfolded proteins. Even though it is caused by different pathogenic mechanisms, in aggregate, it shares similar features. Here, we tested and confirmed a hypothesis that an amyloid antibody can be engineered by a few mutations to target a different species. Amyloid light chain (AL) and β-amyloid peptide (Aβ) are two therapeutic targets that are implicated in amyloid light chain amyloidosis and Alzheimer's disease, respectively. Though crenezumab, an anti-Aβ antibody, is currently unsuccessful, we chose it as a model to computationally design and prepare crenezumab variants, aiming to discover a novel antibody with high affinity to AL fibrils and to establish a technology platform for repurposing amyloid monoclonal antibodies. We successfully re-engineered crenezumab to bind both Aβ42 oligomers and AL fibrils with high binding affinities. It is capable of reversing Aβ42-oligomers-induced cytotoxicity, decreasing the formation of AL fibrils, and alleviating AL-fibrils-induced cytotoxicity in vitro. Our research demonstrated that an amyloid antibody could be engineered by a few mutations to bind new amyloid sequences, providing an efficient way to reposition a therapeutic antibody to target different amyloid diseases.

Matrixed CDR grafting: A neoclassical framework for antibody humanization and developability

Discovery and optimization of a biotherapeutic monoclonal antibody requires a careful balance of target engagement and physicochemical developability properties. To take full advantage of the sequence diversity provided by different antibody discovery platforms, a rapid and reliable process for humanization of antibodies from nonhuman sources is required. Canonically, maximizing homology of the human variable region (V-region) to the original germline was believed to result in preservation of binding, often without much consideration for inherent molecular properties. We expand on this approach by grafting the complementary determining regions (CDRs) of a mouse anti-LAG3 antibody into an extensive matrix of human variable heavy chain (VH) and variable light chain (VL) framework regions with substantially broader sequence homology to assess the impact on complementary determining region-framework compatibility through progressive evaluation of expression, affinity, biophysical developability, and function. Specific VH and VL framework sequences were associated with major expression and purification phenotypes. Greater VL sequence conservation was correlated with retained or improved affinity. Analysis of grafts that bound the target demonstrated that initial developability criteria were significantly impacted by VH, but not VL. In contrast, cell binding and functional characteristics were significantly impacted by VL, but not VH. Principal component analysis of all factors identified multiple grafts that exhibited more favorable antibody properties, notably with nonoptimal sequence conservation. Overall, this study demonstrates that modern throughput systems enable a more thorough, customizable, and systematic analysis of graft-framework combinations, resulting in humanized antibodies with improved global properties that may progress through development more quickly and with a greater probability of success.

Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning

Antibody development, delivery, and efficacy are influenced by antibody-antigen affinity interactions, off-target interactions that reduce antibody bioavailability and pharmacokinetics, and repulsive self-interactions that increase the stability of concentrated antibody formulations and reduce their corresponding viscosity. Yet identifying antibody variants with optimal combinations of these three types of interactions is challenging. Here we show that interpretable machine-learning classifiers, leveraging antibody structural features descriptive of their variable regions and trained on experimental data for a panel of 80 clinical-stage monoclonal antibodies, can identify antibodies with optimal combinations of low off-target binding in a common physiological-solution condition and low self-association in a common antibody-formulation condition. For three clinical-stage antibodies with suboptimal combinations of off-target binding and self-association, the classifiers predicted variable-region mutations that optimized non-affinity interactions while maintaining high-affinity antibody-antigen interactions. Interpretable machine-learning models may facilitate the optimization of antibody candidates for therapeutic applications.

Comparison of the Protective Effect of Polysorbates, Poloxamer and Brij on Antibody Stability Against Different Interfaces

Therapeutic proteins and antibodies are exposed to a variety of interfaces during their lifecycle, which can compromise their stability. Formulations, including surfactants, must be carefully optimized to improve interfacial stability against all types of surfaces. Here we apply a nanoparticle-based approach to evaluate the instability of four antibody drugs against different solid-liquid interfaces characterized by different degrees of hydrophobicity. We considered a model hydrophobic material as well as cycloolefin-copolymer (COC) and cellulose, which represent some of the common solid-liquid interfaces encountered during drug production, storage, and delivery. We assess the protective effect of polysorbate 20, polysorbate 80, Poloxamer 188 and Brij 35 in our assay and in a traditional agitation study. While all nonionic surfactants stabilize antibodies against the air-water interface, none of them can protect against hydrophilic charged cellulose. Polysorbates and Brij increase antibody stability in the presence of COC and the model hydrophobic interface, although to a lesser extent compared to the air-water interface, while Poloxamer 188 has a negligible stabilizing effect against these interfaces. These results highlight the challenge of fully protecting antibodies against all types of solid-liquid interfaces with traditional surfactants. In this context, our high-throughput nanoparticle-based approach can complement traditional shaking assays and assist in formulation design to ensure protein stability not only at air-water interfaces, but also at relevant solid-liquid interfaces encountered during the product lifecycle.

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.

Understanding and controlling the molecular mechanisms of protein aggregation in mAb therapeutics

In antibody development and manufacturing, protein aggregation is a common challenge that can lead to serious efficacy and safety issues. To mitigate this problem, it is important to investigate its molecular origins. This review discusses (1) our current molecular understanding and theoretical models of antibody aggregation, (2) how various stress conditions related to antibody upstream and downstream bioprocesses can trigger aggregation, and (3) current mitigation strategies employed towards inhibiting aggregation. We discuss the relevance of the aggregation phenomenon in the context of novel antibody modalities and highlight how in silico approaches can be exploited to mitigate it.

Multivalent protein-drug conjugates - An emerging strategy for the upgraded precision and efficiency of drug delivery to cancer cells

With almost 20 million new cases per year, cancer constitutes one of the most important challenges for public health systems. Unlike traditional chemotherapy, targeted anti-cancer strategies employ sophisticated therapeutics to precisely identify and attack cancer cells, limiting the impact of drugs on healthy cells and thereby minimizing the unwanted side effects of therapy. Protein drug conjugates (PDCs) are a rapidly growing group of targeted therapeutics, composed of a cancer-recognition factor covalently coupled to a cytotoxic drug. Several PDCs, mainly in the form of antibody-drug conjugates (ADCs) that employ monoclonal antibodies as cancer-recognition molecules, are used in the clinic and many PDCs are currently in clinical trials. Highly selective, strong and stable interaction of the PDC with the tumor marker, combined with efficient, rapid endocytosis of the receptor/PDC complex and its subsequent effective delivery to lysosomes, is critical for the efficacy of targeted cancer therapy with PDCs. However, the bivalent architecture of contemporary clinical PDCs is not optimal for tumor receptor recognition or PDCs internalization. In this review, we focus on multivalent PDCs, which represent a rapidly evolving and highly promising therapeutics that overcome most of the limitations of current bivalent PDCs, enhancing the precision and efficiency of drug delivery to cancer cells. We present an expanding set of protein scaffolds used to generate multivalent PDCs that, in addition to folding into well-defined multivalent molecular structures, enable site-specific conjugation of the cytotoxic drug to ensure PDC homogeneity. We provide an overview of the architectures of multivalent PDCs developed to date, emphasizing their efficacy in the targeted treatment of various cancers.

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.

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data

MOTIVATION

Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity, and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries.

RESULTS

Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns.

AVAILABILITY AND IMPLEMENTATION

All deep sequencing datasets and code to perform the analyses presented within are available via https://github.com/Tessier-Lab-UMich/PSERM_paper.

Non-Immunoglobulin Synthetic Binding Proteins for Oncology

Extensive application of technologies like phage display in screening peptide and protein combinatorial libraries has not only facilitated creation of new recombinant antibodies but has also significantly enriched repertoire of the protein binders that have polypeptide scaffolds without homology to immunoglobulins. These innovative synthetic binding protein (SBP) platforms have grown in number and now encompass monobodies/adnectins, DARPins, lipocalins/anticalins, and a variety of miniproteins such as affibodies and knottins, among others. They serve as versatile modules for developing complex affinity tools that hold promise in both diagnostic and therapeutic settings. An optimal scaffold typically has low molecular weight, minimal immunogenicity, and demonstrates resistance against various challenging conditions, including proteolysis - making it potentially suitable for peroral administration. Retaining functionality under reducing intracellular milieu is also advantageous. However, paramount to its functionality is the scaffold's ability to tolerate mutations across numerous positions, allowing for the formation of a sufficiently large target binding region. This is achieved through the library construction, screening, and subsequent expression in an appropriate system. Scaffolds that exhibit high thermodynamic stability are especially coveted by the developers of new SBPs. These are steadily making their way into clinical settings, notably as antagonists of oncoproteins in signaling pathways. This review surveys the diverse landscape of SBPs, placing particular emphasis on the inhibitors targeting the oncoprotein KRAS, and highlights groundbreaking opportunities for SBPs in oncology.

Development of a V5-tag-directed nanobody and its implementation as an intracellular biosensor of GPCR signaling

Protein-protein interactions (PPIs) form the foundation of any cell signaling network. Considering that PPIs are highly dynamic processes, cellular assays are often essential for their study because they closely mimic the biological complexities of cellular environments. However, incongruity may be observed across different PPI assays when investigating a protein partner of interest; these discrepancies can be partially attributed to the fusion of different large functional moieties, such as fluorescent proteins or enzymes, which can yield disparate perturbations to the protein's stability, subcellular localization, and interaction partners depending on the given cellular assay. Owing to their smaller size, epitope tags may exhibit a diminished susceptibility to instigate such perturbations. However, while they have been widely used for detecting or manipulating proteins in vitro, epitope tags lack the in vivo traceability and functionality needed for intracellular biosensors. Herein, we develop NbV5, an intracellular nanobody binding the V5-tag, which is suitable for use in cellular assays commonly used to study PPIs such as BRET, NanoBiT, and Tango. The NbV5:V5 tag system has been applied to interrogate G protein-coupled receptor signaling, specifically by replacing larger functional moieties attached to the protein interactors, such as fluorescent or luminescent proteins (∼30 kDa), by the significantly smaller V5-tag peptide (1.4 kDa), and for microscopy imaging which is successfully detected by NbV5-based biosensors. Therefore, the NbV5:V5 tag system presents itself as a versatile tool for live-cell imaging and a befitting adaptation to existing cellular assays dedicated to probing PPIs.

Simplifying complex antibody engineering using machine learning

Machine learning is transforming antibody engineering by enabling the generation of drug-like monoclonal antibodies with unprecedented efficiency. Unsupervised algorithms trained on massive and diverse protein sequence datasets facilitate the prediction of panels of antibody variants with native-like intrinsic properties (e.g., high stability), greatly reducing the amount of subsequent experimentation needed to identify specific candidates that also possess desired extrinsic properties (e.g., high affinity). Additionally, supervised algorithms, which are trained on deep sequencing datasets obtained after enrichment of in vitro antibody libraries for one or more specific extrinsic properties, enable the prediction of antibody variants with desired combinations of extrinsic properties without the need for additional screening. Here we review recent advances using both machine learning approaches and how they are impacting the field of antibody engineering as well as key outstanding challenges and opportunities for these paradigm-changing methods.

Quantitative flow cytometric selection of tau conformational nanobodies specific for pathological aggregates

Single-domain antibodies, also known as nanobodies, are broadly important for studying the structure and conformational states of several classes of proteins, including membrane proteins, enzymes, and amyloidogenic proteins. Conformational nanobodies specific for aggregated conformations of amyloidogenic proteins are particularly needed to better target and study aggregates associated with a growing class of associated diseases, especially neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. However, there are few reported nanobodies with both conformational and sequence specificity for amyloid aggregates, especially for large and complex proteins such as the tau protein associated with Alzheimer's disease, due to difficulties in selecting nanobodies that bind to complex aggregated proteins. Here, we report the selection of conformational nanobodies that selectively recognize aggregated (fibrillar) tau relative to soluble (monomeric) tau. Notably, we demonstrate that these nanobodies can be directly isolated from immune libraries using quantitative flow cytometric sorting of yeast-displayed libraries against tau aggregates conjugated to quantum dots, and this process eliminates the need for secondary nanobody screening. The isolated nanobodies demonstrate conformational specificity for tau aggregates in brain samples from both a transgenic mouse model and human tauopathies. We expect that our facile approach will be broadly useful for isolating conformational nanobodies against diverse amyloid aggregates and other complex antigens.

In vitro selection of aptamers and their applications

The introduction of the in-vitro evolution method known as SELEX (Systematic Evolution of Ligands by Exponential enrichment) more than 30 years ago led to the conception of versatile synthetic receptors known as aptamers. Offering many benefits such as low cost, high stability and flexibility, aptamers have sparked innovation in molecular diagnostics, enabled advances in synthetic biology and have facilitated new therapeutic approaches. The SELEX method itself is inherently adaptable and offers near limitless possibilities in yielding functional nucleic acid ligands. This Primer serves to provide guidance on experimental design and highlight new growth areas for this impactful technology.

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data

Motivation

Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries.

Results

Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns.

Availability

All deep sequencing datasets and code to do the analyses presented within are available via GitHub.

Contact

Peter Tessier, ptessier@umich.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Engineering of conserved residues near antibody heavy chain complementary determining region 3 (HCDR3) improves both affinity and stability

Affinity and stability are crucial parameters in antibody development and engineering approaches. Although improvement in both metrics is desirable, trade-offs are almost unavoidable. Heavy chain complementarity determining region 3 (HCDR3) is the best-known region for antibody affinity but its impact on stability is often neglected. Here, we present a mutagenesis study of conserved residues near HCDR3 to elicit the role of this region in the affinity-stability trade-off. These key residues are positioned around the conserved salt bridge between VH-K94 and VH-D101 which is crucial for HCDR3 integrity. We show that the additional salt bridge at the stem of HCDR3 (VH-K94:VH-D101:VH-D102) has an extensive impact on this loop's conformation, therefore simultaneous improvement in both affinity and stability. We find that the disruption of π-π stacking near HCDR3 (VH-Y100E:VL-Y49) at the VH-VL interface cause an irrecoverable loss in stability even if it improves the affinity. Molecular simulations of putative rescue mutants exhibit complex and often non-additive effects. We confirm that our experimental measurements agree with the molecular dynamic simulations providing detailed insights for the spatial orientation of HCDR3. VH-V102 right next to HCDR3 salt bridge might be an ideal candidate to overcome affinity-stability trade-off.

The Evolution of Commercial Antibody Formulations

It has been nearly four decades since the first therapeutic monoclonal antibodies were approved and made available for widespread human use. Herein, US and EU approved antibody formulations are reviewed, and their nature and compositions are evaluated over time. From 1986 through Jan 2023, significant formulation trends have occurred and to represent this, 165 commercial antibody therapeutic formulations were binned into 5 different periods of time. Overall, we have observed the following: 1) The average formulation pH has decreased in recent years by over 0.5 units along with a decrease in variability that is largely driven by non-high concentration liquid in vial presentations for IV administration, 2) The use of certain excipients and buffers such as histidine, sucrose, metal chelators, arginine and methionine has become significantly more common, whereas formulations that contain phosphate, salt, no sugar or no surfactant have fallen out of favor, 3) Overall formulation space has increasingly become more homogenous and has converged in terms of formulation pH and excipient preferences regardless of formulation concentration, drug product presentation, and route of administration, 4) The average calculated isoelectric point (pI) has decreased 0.26 units, and 5) Overall, the average formulation pH and calculated pI for all commercial antibodies surveyed was 6.0 and 8.4, respectively. These trends and formulation convergence may be driven by multiple factors such as advancements in high-throughput computational and analytical technologies, the increased emphasis and understanding of certain developability attributes and formulation principles during lead selection and formulation development, and the adoption of low-risk development platform approaches.

AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains

Over 120 FDA-approved antibody-based therapeutics are used to treat a variety of diseases.However, many candidates could fail because of unfavorable physicochemical properties. Light-chain amyloidosis is one form of aggregation that can lead to severe safety risks in clinical development. Therefore, screening candidates with a less amyloidosis risk at the early stage can not only save the time and cost of antibody development but also improve the safety of antibody drugs. In this study, based on the dipeptide composition of 742 amyloidogenic and 712 non-amyloidogenic antibody light chains, a support vector machine-based model, AB-Amy, was trained to predict the light-chain amyloidogenic risk. The AUC of AB-Amy reaches 0.9651. The excellent performance of AB-Amy indicates that it can be a useful tool for the in silico evaluation of the light-chain amyloidogenic risk to ensure the safety of antibody therapeutics under clinical development. A web server is freely available at http://i.uestc.edu.cn/AB-Amy/.

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.

Surface patches induce nonspecific binding and phase separation of antibodies

Nonspecific interactions are a key challenge in the successful development of therapeutic antibodies. The tendency for nonspecific binding of antibodies is often difficult to reduce by rational design, and instead, it is necessary to rely on comprehensive screening campaigns. To address this issue, we performed a systematic analysis of the impact of surface patch properties on antibody nonspecificity using a designer antibody library as a model system and single-stranded DNA as a nonspecificity ligand. Using an in-solution microfluidic approach, we find that the antibodies tested bind to single-stranded DNA with affinities as high as KD = 1 µM. We show that DNA binding is driven primarily by a hydrophobic patch in the complementarity-determining regions. By quantifying the surface patches across the library, the nonspecific binding affinity is shown to correlate with a trade-off between the hydrophobic and total charged patch areas. Moreover, we show that a change in formulation conditions at low ionic strengths leads to DNA-induced antibody phase separation as a manifestation of nonspecific binding at low micromolar antibody concentrations. We highlight that phase separation is driven by a cooperative electrostatic network assembly mechanism of antibodies with DNA, which correlates with a balance between positive and negative charged patches. Importantly, our study demonstrates that both nonspecific binding and phase separation are controlled by the size of the surface patches. Taken together, these findings highlight the importance of surface patches and their role in conferring antibody nonspecificity and its macroscopic manifestation in phase separation.

Automated optimisation of solubility and conformational stability of antibodies and proteins

Biologics, such as antibodies and enzymes, are crucial in research, biotechnology, diagnostics, and therapeutics. Often, biologics with suitable functionality are discovered, but their development is impeded by developability issues. Stability and solubility are key biophysical traits underpinning developability potential, as they determine aggregation, correlate with production yield and poly-specificity, and are essential to access parenteral and oral delivery. While advances for the optimisation of individual traits have been made, the co-optimization of multiple traits remains highly problematic and time-consuming, as mutations that improve one property often negatively impact others. In this work, we introduce a fully automated computational strategy for the simultaneous optimisation of conformational stability and solubility, which we experimentally validate on six antibodies, including two approved therapeutics. Our results on 42 designs demonstrate that the computational procedure is highly effective at improving developability potential, while not affecting antigen-binding. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.uk.

Development of an inhibiting antibody against equine interleukin 5 to treat insect bite hypersensitivity of horses

Insect bite hypersensitivity (IBH) is the most common allergic skin disease of horses. It is caused by insect bites of the Culicoides spp. which mediate a type I/IVb allergy with strong involvement of eosinophil cells. No specific treatment option is available so far. One concept could be the use of a therapeutic antibody targeting equine interleukin 5, the main activator and regulator of eosinophils. Therefore, antibodies were selected by phage display using the naïve human antibody gene libraries HAL9/10, tested in a cellular in vitro inhibition assay and subjected to an in vitro affinity maturation. In total, 28 antibodies were selected by phage display out of which eleven have been found to be inhibiting in the final format as chimeric immunoglobulin G with equine constant domains. The two most promising candidates were further improved by in vitro affinity maturation up to factor 2.5 regarding their binding activity and up to factor 2.0 regarding their inhibition effect. The final antibody named NOL226-2-D10 showed a strong inhibition of the interleukin 5 binding to its receptor (IC₅₀ = 4 nM). Furthermore, a nanomolar binding activity (EC₅₀ = 8.8 nM), stable behavior and satisfactory producibility were demonstrated. This antibody is an excellent candidate for in vivo studies for the treatment of equine IBH.

Surface-Induced Protein Aggregation and Particle Formation in Biologics: Current Understanding of Mechanisms, Detection and Mitigation Strategies

Protein stability against aggregation is a major quality concern for the production of safe and effective biopharmaceuticals. Amongst the different drivers of protein aggregation, increasing evidence indicates that interactions between proteins and interfaces represent a major risk factor for the formation of protein aggregates in aqueous solutions. Potentially harmful surfaces relevant to biologics manufacturing and storage include air-water and silicone oil-water interfaces as well as materials from different processing units, storage containers, and delivery devices. The impact of some of these surfaces, for instance originating from impurities, can be difficult to predict and control. Moreover, aggregate formation may additionally be complicated by the simultaneous presence of interfacial, hydrodynamic and mechanical stresses, whose contributions may be difficult to deconvolute. As a consequence, it remains difficult to identify the key chemical and physical determinants and define appropriate analytical methods to monitor and predict protein instability at these interfaces. In this review, we first discuss the main mechanisms of surface-induced protein aggregation. We then review the types of contact materials identified as potentially harmful or detected as potential triggers of proteinaceous particle formation in formulations and discuss proposed mitigation strategies. Finally, we present current methods to probe surface-induced instabilities, which represent a starting point towards assays that can be implemented in early-stage screening and formulation development of biologics.

SUMO: In Silico Sequence Assessment Using Multiple Optimization Parameters

To select the most promising screening hits from antibody and VHH display campaigns for subsequent in-depth profiling and optimization, it is highly desirable to assess and select sequences on properties beyond only their binding signals from the sorting process. In addition, developability risk criteria, sequence diversity, and the anticipated complexity for sequence optimization are relevant attributes for hit selection and optimization. Here, we describe an approach for the in silico developability assessment of antibody and VHH sequences. This method not only allows for ranking and filtering multiple sequences with regard to their predicted developability properties and diversity, but also visualizes relevant sequence and structural features of potentially problematic regions and thereby provides rationales and starting points for multi-parameter sequence optimization.

Paragraph-antibody paratope prediction using graph neural networks with minimal feature vectors

SUMMARY

The development of new vaccines and antibody therapeutics typically takes several years and requires over $1bn in investment. Accurate knowledge of the paratope (antibody binding site) can speed up and reduce the cost of this process by improving our understanding of antibody-antigen binding. We present Paragraph, a structure-based paratope prediction tool that outperforms current state-of-the-art tools using simpler feature vectors and no antigen information.

AVAILABILITY AND IMPLEMENTATION

Source code is freely available at www.github.com/oxpig/Paragraph.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

Binding affinity prediction for antibody-protein antigen complexes: A machine learning analysis based on interface and surface areas

Specific antibodies can bind to protein antigens with high affinity and specificity, and this property makes them one of the best protein-based therapeutics. Accurate prediction of antibody‒protein antigen binding affinity is crucial for designing effective antibodies. The current predictive methods for protein‒protein binding affinity usually fail to predict the binding affinity of an antibody‒protein antigen complex with a comparable level of accuracy. Here, new models specific for antibody‒antigen binding affinity prediction are developed according to the different types of interface and surface areas present in antibody‒antigen complex. The contacts-based descriptors are also employed to construct or train different models specific for antibody‒protein antigen binding affinity prediction. The results of this study show that (i) the area-based descriptors are slightly better than the contacts-based descriptors in terms of the predictive power; (ii) the new models specific for antibody‒protein antigen binding affinity prediction are superior to the previously-used general models for predicting the protein‒protein binding affinities; (iii) the performances of the best area-based and contacts-based models developed in this work are better than the performances of a recently-developed graph-based model (i.e., CSM-AB) specific for antibody‒protein antigen binding affinity prediction. The new models developed in this work would not only help understand the mechanisms underlying antibody‒protein antigen interactions, but would also be of some applicable utility in the design and virtual screening of antibody-based therapeutics.

A library approach for the de novo high-throughput isolation of humanized VHH domains with favorable developability properties following camelid immunization

In this study, we generated a novel library approach for high throughput de novo identification of humanized single-domain antibodies following camelid immunization. To achieve this, VHH-derived complementarity-determining regions-3 (CDR3s) obtained from an immunized llama (Lama glama) were grafted onto humanized VHH backbones comprising moderately sequence-diversified CDR1 and CDR2 regions similar to natural immunized and naïve antibody repertoires. Importantly, these CDRs were tailored toward favorable in silico developability properties, by considering human-likeness as well as excluding potential sequence liabilities and predicted immunogenic motifs. Target-specific humanized single-domain antibodies (sdAbs) were readily obtained by yeast surface display. We demonstrate that, by exploiting this approach, high affinity sdAbs with an optimized in silico developability profile can be generated. These sdAbs display favorable biophysical, biochemical, and functional attributes and do not require any further sequence optimization. This approach is generally applicable to any antigen upon camelid immunization and has the potential to significantly accelerate candidate selection and reduce risks and attrition rates in sdAb development.