Nonspecificity in a nonimmune human scFv repertoire

Synthetic development of a broadly neutralizing antibody against snake venom long-chain α-neurotoxins

Snakebite envenoming is a major global public health concern for which improved therapies are urgently needed. The antigenic diversity present in snake venom toxins from various species presents a considerable challenge to the development of a universal antivenom. Here, we used a synthetic human antibody library to find and develop an antibody that neutralizes long-chain three-finger α-neurotoxins produced by numerous medically relevant snakes. Our antibody bound diverse toxin variants with high affinity, blocked toxin binding to the nicotinic acetylcholine receptor in vitro, and protected mice from lethal venom challenge. Structural analysis of the antibody-toxin complex revealed a binding mode that mimics the receptor-toxin interaction. The overall workflow presented is generalizable for the development of antibodies that target conserved epitopes among antigenically diverse targets, and it offers a promising framework for the creation of a monoclonal antibody-based universal antivenom to treat snakebite envenoming.

Discovery of antibodies targeting multipass transmembrane proteins using a suspension cell-based evolutionary approach

Due to their critical functions in cell sensing and signal processing, membrane proteins are highly preferred as pharmacological targets, and antibody drugs constitute the fastest growing category of therapeutic agents on the pharmaceutical market. However, major limitations exist in developing antibodies that recognize complex, multipass transmembrane proteins, such as G-protein-coupled receptors (GPCRs). These challenges, largely due to difficulties with recombinant expression of multipass transmembrane proteins, can be overcome using whole-cell screening techniques, which enable presentation of the functional antigen in its native conformation. Here, we developed suspension cell-based whole-cell panning methodologies to screen for specific binders against GPCRs within a naive yeast-displayed antibody library. We implemented our strategy to discover high-affinity antibodies against four distinct GPCR target proteins, demonstrating the potential for our cell-based screening workflow to advance the discovery of antibody therapeutics targeting membrane proteins.

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.

An in silico method to assess antibody fragment polyreactivity

Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. Here, we design a set of experiments using a diverse naïve synthetic camelid antibody fragment (nanobody) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC > 0.8). Moreover, our models provide quantitative scoring metrics that predict the effect of amino acid substitutions on polyreactivity. We experimentally test our models' performance on three independent nanobody scaffolds, where over 90% of predicted substitutions successfully reduced polyreactivity. Importantly, the models allow us to diminish the polyreactivity of an angiotensin II type I receptor antagonist nanobody, without compromising its functional properties. We provide a companion web-server that offers a straightforward means of predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence.

Prediction of polyreactive and nonspecific single-chain fragment variables through structural biochemical features and protein language-based descriptors

BACKGROUND

Monoclonal antibodies (mAbs) have been used as therapeutic agents, which must overcome many developability issues after the discovery from in vitro display libraries. Especially, polyreactive mAbs can strongly bind to a specific target and weakly bind to off-target proteins, which leads to poor antibody pharmacokinetics in clinical development. Although early assessment of polyreactive mAbs is important in the early discovery stage, experimental assessments are usually time-consuming and expensive. Therefore, computational approaches for predicting the polyreactivity of single-chain fragment variables (scFvs) in the early discovery stage would be promising for reducing experimental efforts.

RESULTS

Here, we made prediction models for the polyreactivity of scFvs with the known polyreactive antibody features and natural language model descriptors. We predicted 19,426 protein structures of scFvs with trRosetta to calculate the polyreactive antibody features and investigated the classifying performance of each factor for polyreactivity. In the known polyreactive features, the net charge of the CDR2 loop, the tryptophan and glycine residues in CDR-H3, and the lengths of the CDR1 and CDR2 loops, importantly contributed to the performance of the models. Additionally, the hydrodynamic features, such as partial specific volume, gyration radius, and isoelectric points of CDR loops and scFvs, were newly added to improve model performance. Finally, we made the prediction model with a robust performance ([Formula: see text]) with an ensemble learning of the top 3 best models.

CONCLUSION

The prediction models for polyreactivity would help assess polyreactive scFvs in the early discovery stage and our approaches would be promising to develop machine learning models with quantitative data from high throughput assays for antibody screening.

Computational counterselection identifies nonspecific therapeutic biologic candidates

Effective biologics require high specificity and limited off-target binding, but these properties are not guaranteed by current affinity-selection-based discovery methods. Molecular counterselection against off targets is a technique for identifying nonspecific sequences but is experimentally costly and can fail to eliminate a large fraction of nonspecific sequences. Here, we introduce computational counterselection, a framework for removing nonspecific sequences from pools of candidate biologics using machine learning models. We demonstrate the method using sequencing data from single-target affinity selection of antibodies, bypassing combinatorial experiments. We show that computational counterselection outperforms molecular counterselection by performing cross-target selection and individual binding assays to determine the performance of each method at retaining on-target, specific antibodies and identifying and eliminating off-target, nonspecific antibodies. Further, we show that one can identify generally polyspecific antibody sequences using a general model trained on affinity data from unrelated targets with potential affinity for a broad range of sequences.

Learning what not to select for in antibody drug discovery

Identifying antibodies with high affinity and target specificity is crucial for drug discovery and development; however, filtering out antibody candidates with nonspecific or polyspecific binding profiles is also important. In this issue of Cell Reports Methods, Saksena et al. report a computational counterselection method combining deep sequencing and machine learning for identifying nonspecific antibody candidates and demonstrate that it has advantages over more established molecular counterselection methods.

A Hybrid Adherent/Suspension Cell-Based Selection Strategy for Discovery of Antibodies Targeting Membrane Proteins

Membrane proteins are favored drug targets and antibody therapeutics represent the fastest-growing category of pharmaceuticals. However, there remains a need for rapid and effective approaches for the discovery of antibodies that recognize membrane proteins to develop a robust clinical pipeline for targeted therapeutics. The challenges associated with recombinant expression of membrane proteins make whole cell screening techniques desirable, as these strategies allow presentation of the target membrane proteins in their native conformations. Here, we describe a workflow that employs both adherent cell-based and suspension cell-based whole cell panning methodologies to enrich for specific binders within a yeast-displayed antibody library. The first round of selection consists of an adherent cell-based approach, wherein a diverse library is panned over target-expressing mammalian cell monolayers in order to debulk the naïve library. Subsequent rounds involve the use of suspension cell-based approaches, facilitated with magnetic-activated cell sorting (MACS) or fluorescence-activated cell sorting (FACS), to achieve further library enrichment. Finally, we describe a high-throughput approach to screen target binding and specificity of individual clones isolated from selection campaigns.

From Genetics to Epigenetics: Top 4 Aspects for Improved SARS-CoV-2 Vaccine Designs as Paradigmatic Examples

This literature review described the genetic and biochemical factors that may have been overlooked in the formulation of vaccines and that most likely underlie possible issues with mass vaccination.

Protein design and variant prediction using autoregressive generative models

The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for important applications where multiple sequence alignments are not robust. Such applications include the prediction of variant effects of indels, disordered proteins, and the design of proteins such as antibodies due to the highly variable complementarity determining regions. We introduce a deep generative model adapted from natural language processing for prediction and design of diverse functional sequences without the need for alignments. The model performs state-of-art prediction of missense and indel effects and we successfully design and test a diverse 105-nanobody library that shows better expression than a 1000-fold larger synthetic library. Our results demonstrate the power of the alignment-free autoregressive model in generalizing to regions of sequence space traditionally considered beyond the reach of prediction and design.

Interaction of clinical-stage antibodies with heme predicts their physiochemical and binding qualities

Immunoglobulin repertoires contain a fraction of antibodies that recognize low molecular weight compounds, including some enzymes' cofactors, such as heme. Here, by using a set of 113 samples with variable region sequences matching clinical-stage antibodies, we demonstrated that a considerable number of these antibodies interact with heme. Antibodies that interact with heme possess specific sequence traits of their antigen-binding regions. Moreover they manifest particular physicochemical and functional qualities i.e. increased hydrophobicity, higher propensity of self-binding, higher intrinsic polyreactivity and reduced expression yields. Thus, interaction with heme is a strong predictor of different molecular and functional qualities of antibodies. Notably, these qualities are of high importance for therapeutic antibodies, as their presence was associated with failure of drug candidates to reach clinic. Our study reveled an important facet of information about relationship sequence-function in antibodies. It also offers a convenient tool for detection of liabilities of therapeutic antibodies.

Toward Drug-Like Multispecific Antibodies by Design

The success of antibody therapeutics is strongly influenced by their multifunctional nature that couples antigen recognition mediated by their variable regions with effector functions and half-life extension mediated by a subset of their constant regions. Nevertheless, the monospecific IgG format is not optimal for many therapeutic applications, and this has led to the design of a vast number of unique multispecific antibody formats that enable targeting of multiple antigens or multiple epitopes on the same antigen. Despite the diversity of these formats, a common challenge in generating multispecific antibodies is that they display suboptimal physical and chemical properties relative to conventional IgGs and are more difficult to develop into therapeutics. Here we review advances in the design and engineering of multispecific antibodies with drug-like properties, including favorable stability, solubility, viscosity, specificity and pharmacokinetic properties. We also highlight emerging experimental and computational methods for improving the next generation of multispecific antibodies, as well as their constituent antibody fragments, with natural IgG-like properties. Finally, we identify several outstanding challenges that need to be addressed to increase the success of multispecific antibodies in the clinic.

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.

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.

Nature-inspired design and evolution of anti-amyloid antibodies

Antibodies that recognize amyloidogenic aggregates with high conformational and sequence specificity are important for detecting and potentially treating a wide range of neurodegenerative disorders, including Alzheimer's and Parkinson's diseases. However, these types of antibodies are challenging to generate because of the large size, hydrophobicity, and heterogeneity of protein aggregates. To address this challenge, we developed a method for generating antibodies specific for amyloid aggregates. First, we grafted amyloidogenic peptide segments from the target polypeptide [Alzheimer's amyloid-β (Aβ) peptide] into the complementarity-determining regions (CDRs) of a stable antibody scaffold. Next, we diversified the grafted and neighboring CDR sites using focused mutagenesis to sample each WT or grafted residue, as well as one to five of the most commonly occurring amino acids at each site in human antibodies. Finally, we displayed these antibody libraries on the surface of yeast cells and selected antibodies that strongly recognize Aβ-amyloid fibrils and only weakly recognize soluble Aβ. We found that this approach enables the generation of monovalent and bivalent antibodies with nanomolar affinity for Aβ fibrils. These antibodies display high conformational and sequence specificity as well as low levels of nonspecific binding and recognize a conformational epitope at the extreme N terminus of human Aβ. We expect that this systematic approach will be useful for generating antibodies with conformational and sequence specificity against a wide range of peptide and protein aggregates associated with neurodegenerative disorders.

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.

A Suicide Switch Directly Eliminates Intracellular scFv Oligomers in the Cytoplasm of Mammalian Cells

As intracellular antibodies (intrabodies) are highly promising tools for drug discovery, an innovative antibody screening platform in mammalian cells was previously developed by a single-chain Fv (scFv)-c-kit growth sensor, which successfully selected rabies nucleoprotein and phosphoprotein-specific intrabodies from a synthetic scFv library. Since the scFv-c-kit growth sensor releases a growth signal after forming oligomers due to binding to an oligomeric antigen, it is critical to use a library which does not contain self-oligomeric scFvs to avoid the off-target signal of the growth sensor. Here, a novel method to eliminate self-oligomeric scFvs directly in the cytoplasm of mammalian cells is presented. A suicide switch by fusing an scFv with a cell-death signaling domain to eliminate scFv oligomers is developed. It is found that among four cell-death signaling domains, a suicide switch by fusing scFv with Fas-associated death domain (FADD) can selectively reduce oligomeric scFvs. Furthermore, the library after eliminating scFv oligomers results in higher efficiency in the intrabody selection platform with a growth sensor. Collectively, the scFv-FADD suicide switch can be applied to eliminate oligomeric scFvs from a library, which can consequently improve the quality of intracellular scFv libraries and accelerate the discovery of intrabodies in the future.

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.