Efficient affinity maturation of antibody variable domains requires co-selection of compensatory mutations to maintain thermodynamic stability

GMMA Can Stabilize Proteins Across Different Functional Constraints

Stabilizing proteins without otherwise hampering their function is a central task in protein engineering and design. PYR1 is a plant hormone receptor that has been engineered to bind diverse small molecule ligands. We sought a set of generalized mutations that would provide stability without affecting functionality for PYR1 variants with diverse ligand-binding capabilities. To do this we used a global multi-mutant analysis (GMMA) approach, which can identify substitutions that have stabilizing effects and do not lower function. GMMA has the added benefit of finding substitutions that are stabilizing in different sequence contexts and we hypothesized that applying GMMA to PYR1 with different functionalities would identify this set of generalized mutations. Indeed, conducting FACS and deep sequencing of libraries for PYR1 variants with two different functionalities and applying a GMMA analysis identified 5 substitutions that, when inserted into four PYR1 variants that each bind a unique ligand, provided an increase of 2-6 °C in thermal inactivation temperature and no decrease in functionality.

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.

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.

Arginine cluster introduction on framework region in anti-lysozyme antibody improved association rate constant by changing conformational diversity of CDR loops

Antibodies are used for many therapeutic and biotechnological purposes. Because the affinity of an antibody to the antigen is critical for clinical efficacy of pharmaceuticals, many affinity maturation strategies have been developed. Although we previously reported an affinity maturation strategy in which the association rate of the antibody toward its antigen is improved by introducing a cluster of arginine residues into the framework region of the antibody, the detailed molecular mechanism responsible for this improvement has been unknown. In this study, we introduced five arginine residues into an anti-hen egg white lysozyme antibody (HyHEL10) Fab fragment to create the R5-mutant and comprehensively characterized the interaction between antibody and antigen using thermodynamic analysis, X-ray crystallography, and molecular dynamics (MD) simulations. Our results indicate that introduction of charged residues strongly enhanced the association rate, as previously reported, and the antibody-antigen complex structure was almost the same for the R5-mutant and wild-type Fabs. The MD simulations indicate that the mutation increased conformational diversity in complementarity-determining region loops and thereby enhanced the association rate. These observations provide the molecular basis of affinity maturation by R5 mutation.

Broadening a SARS-CoV-1-neutralizing antibody for potent SARS-CoV-2 neutralization through directed evolution

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) underscores the need for strategies to rapidly develop neutralizing monoclonal antibodies that can function as prophylactic and therapeutic agents and to help guide vaccine design. Here, we demonstrate that engineering approaches can be used to refocus an existing antibody that neutralizes one virus but not a related virus. Through a rapid affinity maturation strategy, we engineered CR3022, a SARS-CoV-1-neutralizing antibody, to bind to the receptor binding domain of SARS-CoV-2 with >1000-fold increased affinity. The engineered CR3022 neutralized SARS-CoV-2 and provided prophylactic protection from viral challenge in a small animal model of SARS-CoV-2 infection. Deep sequencing throughout the engineering process paired with crystallographic analysis of engineered CR3022 elucidated the molecular mechanisms by which the antibody can accommodate sequence differences in the epitopes between SARS-CoV-1 and SARS-CoV-2. This workflow provides a blueprint for the rapid broadening of neutralization of an antibody from one virus to closely related but resistant viruses.

Specific attributes of the VL domain influence both the structure and structural variability of CDR-H3 through steric effects

Antibodies, through their ability to target virtually any epitope, play a key role in driving the adaptive immune response in jawed vertebrates. The binding domains of standard antibodies are their variable light (VL) and heavy (VH) domains, both of which present analogous complementarity-determining region (CDR) loops. It has long been known that the VH CDRs contribute more heavily to the antigen-binding surface (paratope), with the CDR-H3 loop providing a major modality for the generation of diverse paratopes. Here, we provide evidence for an additional role of the VL domain as a modulator of CDR-H3 structure, using a diverse set of antibody crystal structures and a large set of molecular dynamics simulations. We show that specific attributes of the VL domain such as subtypes, CDR canonical forms and genes can influence the structural diversity of the CDR-H3 loop, and provide a physical model for how this effect occurs through inter-loop contacts and packing of CDRs against each other. Our results indicate that the rigid minor loops fine-tune the structure of CDR-H3, thereby contributing to the generation of surfaces complementary to the vast number of possible epitope topologies, and provide insights into the interdependent nature of CDR conformations, an understanding of which is important for the rational antibody design process.

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.

Chemiluminescence Biosensor for the Determination of Cardiac Troponin I (cTnI)

Cardiac vascular diseases, especially acute myocardial infarction (AMI), are one of the leading causes of death worldwide. Therefore cardio-specific biomarkers such as cardiac troponin I (cTnI) play an essential role in the field of diagnostics. In order to enable rapid and accurate measurement of cTnI with the potential of online measurements, a chemiluminescence-based immunosensor is presented as a proof of concept. A flow cell was designed and combined with a sensitive CMOS camera allowing sensitive optical readout. In addition, a microfluidic setup was established, which achieved selective and quasi-online cTnI determination within ten minutes. The sensor was tested with recombinant cTnI in phosphate buffer and demonstrated cTnI measurements in the concentration range of 2-25 µg/L. With the optimized system, a limit of detection (LoD) of 0.6 µg/L (23 pmol/L) was achieved. Furthermore, the selectivity of the immunosensor was investigated with other recombinant proteins, such as cTnT, and cTnC, at a level of 16 µg/L. No cross-reactivity could be observed. Measurements with diluted blood plasma and serum resulted in an LoD of 60 µg/L (2.4 nmol/L) and 70 µg/L (2.9 nmol/L), respectively.

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

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

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.

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.

Design of nanobody-based bispecific constructs by in silico affinity maturation and umbrella sampling simulations

Random mutagenesis is the natural opportunity for proteins to evolve and biotechnologically it has been exploited to create diversity and identify variants with improved characteristics in the mutant pools. Rational mutagenesis based on biophysical assumptions and supported by computational power has been proposed as a faster and more predictable strategy to reach the same aim. In this work we confirm that substantial improvements in terms of both affinity and stability of nanobodies can be obtained by using combinations of algorithms, even for binders with already high affinity and elevated thermal stability. Furthermore, in silico approaches allowed the development of an optimized bispecific construct able to bind simultaneously the two clinically relevant antigens TNF-α and IL-23 and, by means of its enhanced avidity, to inhibit effectively the apoptosis of TNF-α-sensitive L929 cells. The results revealed that salt bridges, hydrogen bonds, aromatic-aromatic and cation-pi interactions had a critical role in increasing affinity. We provided a platform for the construction of high-affinity bispecific constructs based on nanobodies that can have relevant applications for the control of all those biological mechanisms in which more than a single antigen must be targeted to increase the treatment effectiveness and avoid resistance mechanisms.

Non-specificity as the sticky problem in therapeutic antibody development

Antibodies are highly potent therapeutic scaffolds with more than a hundred different products approved on the market. Successful development of antibody-based drugs requires a trade-off between high target specificity and target binding affinity. In order to better understand this problem, we here review non-specific interactions and explore their fundamental physicochemical origins. We discuss the role of surface patches - clusters of surface-exposed amino acid residues with similar physicochemical properties - as inducers of non-specific interactions. These patches collectively drive interactions including dipole-dipole, π-stacking and hydrophobic interactions to complementary moieties. We elucidate links between these supramolecular assembly processes and macroscopic development issues, such as decreased physical stability and poor in vivo half-life. Finally, we highlight challenges and opportunities for optimizing protein binding specificity and minimizing non-specificity for future generations of therapeutics.

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

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

Engineering Strategies to Overcome the Stability-Function Trade-Off in Proteins

In addition to its biological function, the stability of a protein is a major determinant for its applicability. Unfortunately, engineering proteins for improved functionality usually results in destabilization of the protein. This so-called stability–function trade-off can be explained by the simple fact that the generation of a novel protein function—or the improvement of an existing one—necessitates the insertion of mutations, i.e., deviations from the evolutionarily optimized wild-type sequence. In fact, it was demonstrated that gain-of-function mutations are not more destabilizing than other random mutations. The stability–function trade-off is a universal phenomenon during protein evolution that has been observed with completely different types of proteins, including enzymes, antibodies, and engineered binding scaffolds. In this review, we discuss three types of strategies that have been successfully deployed to overcome this omnipresent obstacle in protein engineering approaches: (i) using highly stable parental proteins, (ii) minimizing the extent of destabilization during functional engineering (by library optimization and/or coselection for stability and function), and (iii) repairing damaged mutants through stability engineering. The implementation of these strategies in protein engineering campaigns will facilitate the efficient generation of protein variants that are not only functional but also stable and therefore better-suited for subsequent applications.

Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods

Although antibodies have become the fastest-growing class of therapeutics on the market, it is still challenging to develop them for therapeutic applications, which often require these molecules to withstand stresses that are not present in vivo. We define developability as the likelihood of an antibody candidate with suitable functionality to be developed into a manufacturable, stable, safe, and effective drug that can be formulated to high concentrations while retaining a long shelf life. The implementation of reliable developability assessments from the early stages of antibody discovery enables flagging and deselection of potentially problematic candidates, while focussing available resources on the development of the most promising ones. Currently, however, thorough developability assessment requires multiple in vitro assays, which makes it labor intensive and time consuming to implement at early stages. Furthermore, accurate in vitro analysis at the early stage is compromised by the high number of potential candidates that are often prepared at low quantities and purity. Recent improvements in the performance of computational predictors of developability potential are beginning to change this scenario. Many computational methods only require the knowledge of the amino acid sequences and can be used to identify possible developability issues or to rank available candidates according to a range of biophysical properties. Here, we describe how the implementation of in silico tools into antibody discovery pipelines is increasingly offering time- and cost-effective alternatives to in vitro experimental screening, thus streamlining the drug development process. We discuss in particular the biophysical and biochemical properties that underpin developability potential and their trade-offs, review various in vitro assays to measure such properties or parameters that are predictive of developability, and give an overview of the growing number of in silico tools available to predict properties important for antibody development, including the CamSol method developed in our laboratory.

Reduction of therapeutic antibody self-association using yeast-display selections and machine learning

Self-association governs the viscosity and solubility of therapeutic antibodies in high-concentration formulations used for subcutaneous delivery, yet it is difficult to reliably identify candidates with low self-association during antibody discovery and early-stage optimization. Here, we report a high-throughput protein engineering method for rapidly identifying antibody candidates with both low self-association and high affinity. We find that conjugating quantum dots to IgGs that strongly self-associate (pH 7.4, PBS), such as lenzilumab and bococizumab, results in immunoconjugates that are highly sensitive for detecting other high self-association antibodies. Moreover, these conjugates can be used to rapidly enrich yeast-displayed bococizumab sub-libraries for variants with low levels of immunoconjugate binding. Deep sequencing and machine learning analysis of the enriched bococizumab libraries, along with similar library analysis for antibody affinity, enabled identification of extremely rare variants with co-optimized levels of low self-association and high affinity. This analysis revealed that co-optimizing bococizumab is difficult because most high-affinity variants possess positively charged variable domains and most low self-association variants possess negatively charged variable domains. Moreover, negatively charged mutations in the heavy chain CDR2 of bococizumab, adjacent to its paratope, were effective at reducing self-association without reducing affinity. Interestingly, most of the bococizumab variants with reduced self-association also displayed improved folding stability and reduced nonspecific binding, revealing that this approach may be particularly useful for identifying antibody candidates with attractive combinations of drug-like properties.Abbreviations: AC-SINS: affinity-capture self-interaction nanoparticle spectroscopy; CDR: complementarity-determining region; CS-SINS: charge-stabilized self-interaction nanoparticle spectroscopy; FACS: fluorescence-activated cell sorting; Fab: fragment antigen binding; Fv: fragment variable; IgG: immunoglobulin; QD: quantum dot; PBS: phosphate-buffered saline; V_H: variable heavy; V_L: variable light.

Affinity and Stability Analysis of Yeast Displayed Proteins

Yeast surface display is a powerful protein engineering technology that is extensively used to improve various properties of proteins, including affinity, specificity, and stability or even to add novel functions (usually ligand binding). Apart from its robustness and versatility as an engineering tool, yeast display offers a further critical advantage: Once the selection campaign is finished, usually resulting in an oligoclonal pool, these enriched protein variants can be analyzed individually on the surface of yeast without the need for any sub-cloning, soluble expression, and purification. Here, we provide detailed protocols for determining both the affinity and the thermal stability of yeast displayed proteins. In addition, we discuss the advantages, challenges, and potential pitfalls associated with affinity and stability analysis using yeast surface display.

Simultaneous affinity maturation and developability enhancement using natural liability-free CDRs

Affinity maturation is often a necessary step for the development of potent therapeutic molecules. Many different diversification strategies have been used for antibody affinity maturation, including error-prone PCR, chain shuffling, and targeted complementary-determining region (CDR) mutation. Although effective, they can negatively impact antibody stability or alter epitope recognition. Moreover, they do not address the presence of sequence liabilities, such as glycosylation, asparagine deamidation, aspartate isomerization, aggregation motifs, and others. Such liabilities, if present or inadvertently introduced, can potentially create the need for new rounds of engineering, or even abolish the value of the antibody as a therapeutic molecule. Here, we demonstrate a sequence agnostic method to improve antibody affinities, while simultaneously eliminating sequence liabilities and retaining the same epitope binding as the parental antibody. This was carried out using a defined collection of natural CDRs as the diversity source, purged of sequence liabilities, and matched to the antibody germline gene family. These CDRs were inserted into the lead molecule in one or two sites at a time (LCDR1-2, LCDR3, HCDR1-2) while retaining the HCDR3 and framework regions unchanged. The final analysis of 92 clones revealed 81 unique variants, with each of 24 tested variants having the same epitope specificity as the parental molecule. Of these, the average affinity improved by over 100 times (to 96 pM), and the best affinity improvement was 231-fold (to 32 pM).

Delicate balance among thermal stability, binding affinity, and conformational space explored by single-domain VHH antibodies

The high binding affinities and specificities of antibodies have led to their use as drugs and biosensors. Single-domain V_HH antibodies exhibit high specificity and affinity but have higher stability and solubility than conventional antibodies as they are single-domain proteins. In this work, based on physicochemical measurements and molecular dynamics (MD) simulations, we have gained insight that will facilitate rational design of single-chain V_HH antibodies. We first assessed two homologous V_HH antibodies by differential scanning calorimetry (DSC); one had a high (64.8 °C) and the other a low (58.6 °C) melting temperature. We then generated a series of the variants of the low stability antibody and analyzed their thermal stabilities by DSC and characterized their structures through MD simulations. We found that a single mutation that resulted in 8.2 °C improvement in melting temperature resulted in binding affinity an order of magnitude lower than the parent antibody, likely due to a shift of conformational space explored by the single-chain V_HH antibody. These results suggest that the delicate balance among conformational stability, binding capability, and conformational space explored by antibodies must be considered in design of fully functional single-chain V_HH antibodies.

Regulatory Approved Monoclonal Antibodies Contain Framework Mutations Predicted From Human Antibody Repertoires

Monoclonal antibodies (mAbs) are an important class of therapeutics used to treat cancer, inflammation, and infectious diseases. Identifying highly developable mAb sequences in silico could greatly reduce the time and cost required for therapeutic mAb development. Here, we present position-specific scoring matrices (PSSMs) for antibody framework mutations developed using baseline human antibody repertoire sequences. Our analysis shows that human antibody repertoire-based PSSMs are consistent across individuals and demonstrate high correlations between related germlines. We show that mutations in existing therapeutic antibodies can be accurately predicted solely from baseline human antibody sequence data. We find that mAbs developed using humanized mice had more human-like FR mutations than mAbs originally developed by hybridoma technology. A quantitative assessment of entire framework regions of therapeutic antibodies revealed that there may be potential for improving the properties of existing therapeutic antibodies by incorporating additional mutations of high frequency in baseline human antibody repertoires. In addition, high frequency mutations in baseline human antibody repertoires were predicted in silico to reduce immunogenicity in therapeutic mAbs due to the removal of T cell epitopes. Several therapeutic mAbs were identified to have common, universally high-scoring framework mutations, and molecular dynamics simulations revealed the mechanistic basis for the evolutionary selection of these mutations. Our results suggest that baseline human antibody repertoires may be useful as predictive tools to guide mAb development in the future.

Human Transbodies to Reverse Transcriptase Connection Subdomain of HIV-1 Gag-Pol Polyprotein Reduce Infectiousness of the Virus Progeny

HIV-1 progeny are released from infected cells as immature particles that are unable to infect new cells. Gag-Pol polyprotein dimerization via the reverse transcriptase connection domain (RTCDs) is pivotal for proper activation of the virus protease (PR protein) in an early event of the progeny virus maturation process. Thus, the RTCD is a potential therapeutic target for a broadly effective anti-HIV agent through impediment of virus maturation. In this study, human single-chain antibodies (HuscFvs) that bound to HIV-1 RTCD were generated using phage display technology. Computerized simulation guided the selection of the transformed Escherichia coli-derived HuscFvs that bound to the RTCD dimer interface. The selected HuscFvs were linked molecularly to human-derived-cell-penetrating peptide (CPP) to make them cell-penetrable (i.e., become transbodies). The CPP-HuscFvs/transbodies produced by a selected transformed E. coli clone were tested for anti-HIV-1 activity. CPP-HuscFvs of transformed E. coli clone 11 (CPP-HuscFv11) that presumptively bound at the RTCD dimer interface effectively reduced reverse transcriptase activity in the newly released virus progeny. Infectiousness of the progeny viruses obtained from CPP-HuscFv11-treated cells were reduced by a similar magnitude to those obtained from protease/reverse transcriptase inhibitor-treated cells, indicating anti-HIV-1 activity of the transbodies. The CPP-HuscFv11/transbodies to HIV-1 RTCD could be an alternative, anti-retroviral agent for long-term HIV-1 treatment.

Affinity maturation of antibodies by combinatorial codon mutagenesis versus error-prone PCR

IN VITRO

affinity maturation of therapeutic monoclonal antibodies is commonly applied to achieve desired properties, such as improved binding kinetics and affinity. Currently there are no universally accepted protocols for generation of variegated antibody libraries or selection thereof. Here, we performed affinity maturation using a yeast-based single-chain variable fragment (scFv) expression system to compare two mutagenesis methods: random mutagenesis across the entire V(D)J region by error-prone PCR, and a novel combinatorial mutagenesis process limited to the complementarity-determining regions (CDRs). We applied both methods of mutagenesis to four human antibodies against well-known immuno-oncology target proteins. Detailed sequence analysis showed an even mutational distribution across the entire length of the scFv for the error-prone PCR method and an almost exclusive targeting of the CDRs for the combinatorial method. Though there were distinct mutagenesis profiles for each target antibody and mutagenesis method, we found that both methods improved scFv affinity with similar efficiency. When a subset of the affinity-matured antibodies was expressed as full-length immunoglobulin, the measured affinity constants were mostly comparable to those of the respective scFv, but the full-length antibodies were inferior to their scFv counterparts for one of the targets. Furthermore, we found that improved affinity for the full-length antibody did not always translate into enhanced binding to cell-surface expressed antigen or improved immune checkpoint blocking ability, suggesting that screening with full-length antibody or antigen-binding fragment formats might be advantageous and the subject of a future study.

Directed evolution of potent neutralizing nanobodies against SARS-CoV-2 using CDR-swapping mutagenesis

There is widespread interest in facile methods for generating potent neutralizing antibodies, nanobodies, and other affinity proteins against SARS-CoV-2 and related viruses to address current and future pandemics. While isolating antibodies from animals and humans are proven approaches, these methods are limited to the affinities, specificities, and functional activities of antibodies generated by the immune system. Here we report a surprisingly simple directed evolution method for generating nanobodies with high affinities and neutralization activities against SARS-CoV-2. We demonstrate that complementarity-determining region swapping between low-affinity lead nanobodies, which we discovered unintentionally but find is simple to implement systematically, results in matured nanobodies with unusually large increases in affinity. Importantly, the matured nanobodies potently neutralize both SARS-CoV-2 pseudovirus and live virus, and possess drug-like biophysical properties. We expect that our methods will improve in vitro nanobody discovery and accelerate the generation of potent neutralizing nanobodies against diverse coronaviruses.

Design of a Trispecific Checkpoint Inhibitor and Natural Killer Cell Engager Based on a 2 + 1 Common Light Chain Antibody Architecture

Natural killer cell engagers gained enormous interest in recent years due to their potent anti-tumor activity and favorable safety profile. Simultaneously, chicken-derived antibodies entered clinical studies paving the way for avian-derived therapeutics. In this study, we describe the affinity maturation of a common light chain (cLC)-based, chicken-derived antibody targeting EGFR, followed by utilization of the same light chain for the isolation of CD16a- and PD-L1-specific monoclonal antibodies. The resulting binders target their respective antigen with single-digit nanomolar affinity while blocking the ligand binding of all three respective receptors. Following library-based humanization, bispecific and trispecific variants in a standard 1 + 1 or a 2 + 1 common light chain format were generated, simultaneously targeting EGFR, CD16a, and PD-L1. The trispecific antibody mediated an elevated antibody-dependent cellular cytotoxicity (ADCC) in comparison to the EGFR×CD16a bispecific variant by effectively bridging EGFR/PD-L1 double-positive cancer cells with CD16a-positive effector cells. These findings represent, to our knowledge, the first detailed report on the generation of a trispecific 2 + 1 antibodies exhibiting a common light chain and illustrate synergistic effects of trispecific antigen binding. Overall, this generic procedure paves the way for the engineering of tri- and oligospecific therapeutic antibodies derived from avian immunizations.

IsAb: a computational protocol for antibody design

The design of therapeutic antibodies has attracted a large amount of attention over the years. Antibodies are widely used to treat many diseases due to their high efficiency and low risk of adverse events. However, the experimental methods of antibody design are time-consuming and expensive. Although computational antibody design techniques have had significant advances in the past years, there are still some challenges that need to be solved, such as the flexibility of antigen structure, the lack of antibody structural data and the absence of standard antibody design protocol. In the present work, we elaborated on an in silico antibody design protocol for users to easily perform computer-aided antibody design. First, the Rosetta web server will be applied to generate the 3D structure of query antibodies if there is no structural information available. Then, two-step docking will be used to identify the binding pose of an antibody-antigen complex when the binding information is unknown. ClusPro is the first method to be used to conduct the global docking, and SnugDock is applied for the local docking. Sequentially, based on the predicted binding poses, in silico alanine scanning will be used to predict the potential hotspots (or key residues). Finally, computational affinity maturation protocol will be used to modify the structure of antibodies to theoretically increase their affinity and stability, which will be further validated by the bioassays in the future. As a proof of concept, we redesigned antibody D44.1 and compared it with previously reported data in order to validate IsAb protocol. To further illustrate our proposed protocol, we used cemiplimab antibody, a PD-1 checkpoint inhibitor, as an example to showcase a step-by-step tutorial.

Mutational fitness landscapes reveal genetic and structural improvement pathways for a vaccine-elicited HIV-1 broadly neutralizing antibody

Vaccine-based elicitation of broadly neutralizing antibodies holds great promise for preventing HIV-1 transmission. However, the key biophysical markers of improved antibody recognition remain uncertain in the diverse landscape of potential antibody mutation pathways, and a more complete understanding of anti-HIV-1 fusion peptide (FP) antibody development will accelerate rational vaccine designs. Here we survey the mutational landscape of the vaccine-elicited anti-FP antibody, vFP16.02, to determine the genetic, structural, and functional features associated with antibody improvement or fitness. Using site-saturation mutagenesis and yeast display functional screening, we found that 1% of possible single mutations improved HIV-1 envelope trimer (Env) affinity, but generally comprised rare somatic hypermutations that may not arise frequently in vivo. We observed that many single mutations in the vFP16.02 Fab could enhance affinity >1,000-fold against soluble FP, although affinity improvements against the HIV-1 trimer were more measured and rare. The most potent variants enhanced affinity to both soluble FP and Env, had mutations concentrated in antibody framework regions, and achieved up to 37% neutralization breadth compared to 28% neutralization of the template antibody. Altered heavy- and light-chain interface angles and conformational dynamics, as well as reduced Fab thermal stability, were associated with improved HIV-1 neutralization breadth and potency. We also observed parallel sets of mutations that enhanced viral neutralization through similar structural mechanisms. These data provide a quantitative understanding of the mutational landscape for vaccine-elicited FP-directed broadly neutralizing antibody and demonstrate that numerous antigen-distal framework mutations can improve antibody function by enhancing affinity simultaneously toward HIV-1 Env and FP.

Rational affinity maturation of anti-amyloid antibodies with high conformational and sequence specificity

The aggregation of amyloidogenic polypeptides is strongly linked to several neurodegenerative disorders, including Alzheimer's and Parkinson's diseases. Conformational antibodies that selectively recognize protein aggregates are leading therapeutic agents for selectively neutralizing toxic aggregates, diagnostic and imaging agents for detecting disease, and biomedical reagents for elucidating disease mechanisms. Despite their importance, it is challenging to generate high-quality conformational antibodies in a systematic and site-specific manner due to the properties of protein aggregates (hydrophobic, multivalent, and heterogeneous) and limitations of immunization (uncontrolled antigen presentation and immunodominant epitopes). Toward addressing these challenges, we have developed a systematic directed evolution procedure for affinity maturing antibodies against Alzheimer's Aβ fibrils and selecting variants with strict conformational and sequence specificity. We first designed a library based on a lead conformational antibody by sampling combinations of amino acids in the antigen-binding site predicted to mediate high antibody specificity. Next, we displayed this library on the surface of yeast, sorted it against Aβ42 aggregates, and identified promising clones using deep sequencing. The resulting antibodies displayed similar or higher affinities than clinical-stage Aβ antibodies (aducanumab and crenezumab). Moreover, the affinity-matured antibodies retained high conformational specificity for Aβ aggregates, as observed for aducanumab and unlike crenezumab. Notably, the affinity-maturated antibodies displayed extremely low levels of nonspecific interactions, as observed for crenezumab and unlike aducanumab. We expect that our systematic methods for generating antibodies with unique combinations of desirable properties will improve the generation of high-quality conformational antibodies specific for diverse types of aggregated conformers.

Affinity maturation: highlights in the application of in vitro strategies for the directed evolution of antibodies

Affinity maturation is a key technique in protein engineering which is used to improve affinity and binding interactions in vitro, a process often required to fulfil the therapeutic potential of antibodies. There are many available display technologies and maturation methods developed over the years, which have been instrumental in the production of therapeutic antibodies. However, due to the inherent limitations in display capacity of these technologies, accommodation of expansive and complex library builds is still a challenge. In this article, we discuss our recent efforts in the affinity maturation of a difficult antibody lineage using an unbiased approach, which sought to explore a larger sequence space through the application of DNA recombination and shuffling techniques across the entire antibody region and selections using ribosome display. We also highlight the key features of several display technologies and diversification methods, and discuss the strategies devised by different groups in response to different challenges. Particular attention is drawn to examples which are aimed at the expansion of sequence, structural or experimental diversity through different means and approaches. Here, we provide our perspectives on these methodologies and the considerations involved in the design of effective strategies for the directed evolution of antibodies.

Progress toward improved understanding of antibody maturation

Upon encountering an antigen, antibodies mature through various rounds of somatic mutations, resulting in higher affinities and specificities to the particular antigen. We review recent progress in four areas of antibody maturation studies. (1) Next-generation and single-cell sequencing have revolutionized the analysis of antibody repertoires by dramatically increasing the sequences available to study the state and evolution of the immune system. Computational methods, including machine learning tools, have been developed for reconstituting antibody clonal lineages and for general repertoire analysis. (2) The availability of X-ray structures, thermodynamic and kinetic data, and molecular dynamics simulations provide information on the biophysical mechanisms responsible for improved affinity. (3) In addition to improved binding to a specific antigen, providing affinity-independent diversity and self/nonself discrimination are fundamental functions of the immune system. Recent studies, including X-ray structures, yield improved understanding of both mechanisms. (4) Results from in vivo maturation help to develop methods of in vitro maturation to improve antibody properties for therapeutic applications, frequently combining computational and experimental approaches.

Combining random mutagenesis, structure-guided design and next-generation sequencing to mitigate polyreactivity of an anti-IL-21R antibody

Despite substantial technological advances in antibody library and display platform development, the number of approved biotherapeutics from displayed libraries remains limited. In vivo, 20–50% of peripheral B cells undergo a process of receptor editing, which modifies the variable and junctional regions of light chains to delete auto-reactive clones. However, in vitro antibody evolution relies primarily on interaction with antigen, with no in-built checkpoints to ensure that the selected antibodies have not acquired additional specificities or biophysical liabilities during the optimization process. We had previously observed an enrichment of positive charge in the complementarity-determining regions of an anti-IL-21 R antibody during affinity optimization, which correlated with more potent IL-21 neutralization, but poor in vivo pharmacokinetics (PK). There is an emerging body of data that has correlated antibody nonspecificity with poor PK in vivo, and established a series of screening assays that are predictive of this behavior. In this study we revisit the challenge of developing an anti-IL-21 R antibody that can effectively compete with IL-21 for its highly negatively charged paratope while maintaining favorable biophysical properties. In vitro deselection methods that included an excess of negatively charged membrane preparations, or deoxyribonucleic acid, during phage selection of optimization libraries were unsuccessful in avoiding enrichment of highly charged, nonspecific antibody variants. However, a combination of structure-guided rational library design, next-generation sequencing of library outputs and application of linear regression models resulted in the identification of an antibody that maintained high affinity for IL-21 R and exhibited a desirable stability and biophysical profile.

Systematic Activity Maturation of a Single-Domain Antibody with Non-canonical Amino Acids through Chemical Mutagenesis

Great advances have been made over the last four decades in therapeutic and diagnostic applications of antibodies. The activity maturation of antibody candidates, however, remains a significant challenge. To address this problem, we present a method that enables the systematic enhancement of the activity of a single-domain antibody through the post-translational installation of non-canonical side chains by chemical mutagenesis. We illustrate this approach by performing a structure-activity relationship study beyond the 20 naturally occurring amino acids on a single-domain antibody designed in silico to inhibit the aggregation of the amyloid-β peptide, a process closely linked to Alzheimer's disease. We found that this approach can improve, by five orders of magnitude, the anti-aggregation activity of the starting single-domain antibody, without affecting its stability. These results show that the expansion of the chemical space available to antibodies through chemical mutagenesis can be exploited for the systematic enhancement of the activity of these molecules.

Drug-like antibodies with high affinity, diversity and developability directly from next-generation antibody libraries

Therapeutic antibodies must have "drug-like" properties. These include high affinity and specificity for the intended target, biological activity, and additional characteristics now known as "developability properties": long-term stability and resistance to aggregation when in solution, thermodynamic stability to prevent unfolding, high expression yields to facilitate manufacturing, low self-interaction, among others. Sequence-based liabilities may affect one or more of these characteristics. Improving the stability and developability of a lead antibody is typically achieved by modifying its sequence, a time-consuming process that often results in reduced affinity. Here we present a new antibody library format that yields high-affinity binders with drug-like developability properties directly from initial selections, reducing the need for further engineering or affinity maturation. The innovative semi-synthetic design involves grafting natural complementarity-determining regions (CDRs) from human antibodies into scaffolds based on well-behaved clinical antibodies. HCDR3s were amplified directly from B cells, while the remaining CDRs, from which all sequence liabilities had been purged, were replicated from a large next-generation sequencing dataset. By combining two in vitro display techniques, phage and yeast display, we were able to routinely recover a large number of unique, highly developable antibodies against clinically relevant targets with affinities in the subnanomolar to low nanomolar range. We anticipate that the designs and approaches presented here will accelerate the drug development process by reducing the failure rate of leads due to poor antibody affinities and developability.Abbreviations: AC-SINS: affinity-capture self-interaction nanoparticle spectroscopy; CDR: complementarity-determining region; CQA: critical quality attribute; ELISA: enzyme-linked immunoassay; FACS: fluorescence-activated cell sorting; Fv: fragment variable; GM-CSF: granulocyte-macrophage colony-stimulating factor; HCDR3: heavy chain CDR3; IFN2a: interferon α-2; IL6: interleukin-6; MACS: magnetic-activated cell sorting; NGS: next generation sequencing; PCR: polymerase chain reaction; SEC: size-exclusion chromatography; SPR: surface plasmon resonance; TGFβ-R2: transforming growth factor β-R2; VH: variable heavy; VK: variable kappa; VL: variable light; Vl: variable lambda.

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

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

Structure-based design and discovery of novel anti-tissue factor antibodies with cooperative double-point mutations, using interaction analysis

The generation of a wide range of candidate antibodies is important for the successful development of drugs that simultaneously satisfy multiple requirements. To find cooperative mutations and increase the diversity of mutants, an in silico double-point mutation approach, in which 3D models of all possible double-point mutant/antigen complexes are constructed and evaluated using interaction analysis, was developed. Starting from an antibody with very high affinity, four double-point mutants were designed in silico. Two of the double-point mutants exhibited improved affinity or affinity comparable to that of the starting antibody. The successful identification of two active double-point mutants showed that a cooperative mutation could be found by utilizing information regarding the interactions. The individual single-point mutants of the two active double-point mutants showed decreased affinity or no expression. These results suggested that the two active double-point mutants cannot be obtained through the usual approach i.e. a combination of improved single-point mutants. In addition, a triple-point mutant, which combines the distantly located active double-point mutation and an active single-point mutation collaterally obtained in the process of the double-point mutation strategy, was designed. The triple-point mutant showed improved affinity. This finding suggested that the effects of distantly located mutations are independent and additive. The double-point mutation approach using the interaction analysis of 3D structures expands the design repertoire for mutants, and hopefully paves a way for the identification of cooperative multiple-point mutations.

In vitro evolution of antibody affinity via insertional scanning mutagenesis of an entire antibody variable region

We report a systematic combinatorial exploration of affinity enhancement of antibodies by insertions and deletions (InDels). Transposon-based introduction of InDels via the method TRIAD (transposition-based random insertion and deletion mutagenesis) was used to generate large libraries with random in-frame InDels across the entire single-chain variable fragment gene that were further recombined and screened by ribosome display. Knowledge of potential insertion points from TRIAD libraries formed the basis of exploration of length and sequence diversity of novel insertions by insertional-scanning mutagenesis (InScaM). An overall 256-fold affinity improvement of an anti-IL-13 antibody BAK1 as a result of InDel mutagenesis and combination with known point mutations validates this approach, and suggests that the results of this InDel mutagenesis and conventional exploration of point mutations can synergize to generate antibodies with higher affinity.

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.

Deep Mutational Scanning of SARS-CoV-2 Receptor Binding Domain Reveals Constraints on Folding and ACE2 Binding

The receptor binding domain (RBD) of the SARS-CoV-2 spike glycoprotein mediates viral attachment to ACE2 receptor and is a major determinant of host range and a dominant target of neutralizing antibodies. Here, we experimentally measure how all amino acid mutations to the RBD affect expression of folded protein and its affinity for ACE2. Most mutations are deleterious for RBD expression and ACE2 binding, and we identify constrained regions on the RBD's surface that may be desirable targets for vaccines and antibody-based therapeutics. But a substantial number of mutations are well tolerated or even enhance ACE2 binding, including at ACE2 interface residues that vary across SARS-related coronaviruses. However, we find no evidence that these ACE2-affinity-enhancing mutations have been selected in current SARS-CoV-2 pandemic isolates. We present an interactive visualization and open analysis pipeline to facilitate use of our dataset for vaccine design and functional annotation of mutations observed during viral surveillance.

A blocking antibody against canine CSF-1R maturated by limited CDR mutagenesis

CSF-1R is a receptor mostly associated with the mononuclear phagocytic system. However, its expression within tumors has been linked with poor prognosis in both humans and dogs. Accordingly, several reports have demonstrated the beneficial effects of blocking CSF-1R in model systems of cancer. In this study, we generated a monoclonal antibody that could block CSF-1R in dogs as the first step to develop an anticancer drug for this species. Initially, an antibody was raised by the hybridoma methodology against the fragment responsible for receptor dimerization. mAb3.1, one of the resulting hybridoma clones, was able to bind macrophages in fixed tissues and was shown to inhibit cells of the mononuclear phagocytic line. Nevertheless, mAb 3.1 could not bind to some glycoforms of the receptor in its native form, while also demonstrating cross-reactivity with other proteins. To enhance binding properties of the mAb, five amino acids of the complementarity-determining region 2 of the variable heavy chain of mAb3.1 were mutated by PCR, and the variant scFv clones were screened by phage display. The selected scFv clones demonstrated improved binding to the native receptor as well as increased anti-macrophage activity. The resulting scFv antibody fragment presented here has the potential for use in cancer patients and in inflammatory diseases. Furthermore, this work provides insights into the use of such restricted mutations in antibody engineering.

The Effects of Framework Mutations at the Variable Domain Interface on Antibody Affinity Maturation in an HIV-1 Broadly Neutralizing Antibody Lineage

Understanding affinity maturation of antibodies that can target many variants of HIV-1 is important for vaccine development. While the antigen-binding site of antibodies is known to mutate throughout the co-evolution of antibodies and viruses in infected individuals, the roles of the mutations in the antibody framework region are not well understood. Throughout affinity maturation, the CH103 broadly neutralizing antibody lineage, from an individual designated CH505, altered the orientation of one of its antibody variable domains. The change in orientation was a response to insertions in the variable loop 5 (V5) of the HIV envelope. In this study, we generated CH103 lineage antibody variants in which residues in the variable domain interface were mutated, and measured the binding to both autologous and heterologous HIV-1 envelopes. Our data show that very few mutations in an early intermediate antibody of the lineage can improve binding toward both autologous and heterologous HIV-1 envelopes. We also crystallized an antibody mutant to show that framework mutations alone can result in a shift in relative orientations of the variable domains. Taken together, our results demonstrate the functional importance of residues located outside the antigen-binding site in affinity maturation.

Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding

The receptor binding domain (RBD) of the SARS-CoV-2 spike glycoprotein mediates viral attachment to ACE2 receptor, and is a major determinant of host range and a dominant target of neutralizing antibodies. Here we experimentally measure how all amino-acid mutations to the RBD affect expression of folded protein and its affinity for ACE2. Most mutations are deleterious for RBD expression and ACE2 binding, and we identify constrained regions on the RBD's surface that may be desirable targets for vaccines and antibody-based therapeutics. But a substantial number of mutations are well tolerated or even enhance ACE2 binding, including at ACE2 interface residues that vary across SARS-related coronaviruses. However, we find no evidence that these ACE2-affinity enhancing mutations have been selected in current SARS-CoV-2 pandemic isolates. We present an interactive visualization and open analysis pipeline to facilitate use of our dataset for vaccine design and functional annotation of mutations observed during viral surveillance.

An in vivo platform to select and evolve aggregation-resistant proteins

Protein biopharmaceuticals are highly successful, but their utility is compromised by their propensity to aggregate during manufacture and storage. As aggregation can be triggered by non-native states, whose population is not necessarily related to thermodynamic stability, prediction of poorly-behaving biologics is difficult, and searching for sequences with desired properties is labour-intensive and time-consuming. Here we show that an assay in the periplasm of E. coli linking aggregation directly to antibiotic resistance acts as a sensor for the innate (un-accelerated) aggregation of antibody fragments. Using this assay as a directed evolution screen, we demonstrate the generation of aggregation resistant scFv sequences when reformatted as IgGs. This powerful tool can thus screen and evolve ‘manufacturable’ biopharmaceuticals early in industrial development. By comparing the mutational profiles of three different immunoglobulin scaffolds, we show the applicability of this method to investigate protein aggregation mechanisms important to both industrial manufacture and amyloid disease.

Protein aggregation remains a significant challenge for manufacturing of protein biopharmaceuticals. Here, the authors demonstrate the use of directed evolution and an assay for in vivo innate protein aggregation-propensity to generate aggregation-resistant scFv fragments.

Directed evolution methods for overcoming trade-offs between protein activity and stability

Engineered proteins are being widely developed and employed in applications ranging from enzyme catalysts to therapeutic antibodies. Directed evolution, an iterative experimental process composed of mutagenesis and library screening, is a powerful technique for enhancing existing protein activities and generating entirely new ones not observed in nature. However, the process of accumulating mutations for enhanced protein activity requires chemical and structural changes that are often destabilizing, and low protein stability is a significant barrier to achieving large enhancements in activity during multiple rounds of directed evolution. Here we highlight advances in understanding the origins of protein activity/stability trade-offs for two important classes of proteins (enzymes and antibodies) as well as innovative experimental and computational methods for overcoming such trade-offs. These advances hold great potential for improving the generation of highly active and stable proteins that are needed to address key challenges related to human health, energy and the environment.

Physicochemical determinants of antibody-protein interactions

Antibodies are specialized proteins generated by immune system for high specificity and affinity binding to target antigens. Because of their essential roles in immune system, antibodies have been successfully developed and engineered as biopharmaceuticals for treatment of various diseases. Analysis of antibody-protein interactions is always required to get detailed information on effectivity of such antibody-based therapeutics. Although physicochemical rules cannot be generalized for every antibody-protein interaction, there are some features which should be taken into account during antibody development and engineering efforts. In this chapter, physicochemical analysis of antibody paratope-protein epitope interactions will be discussed to highlight important characteristics. First, paratope and non-paratope regions of antibodies will be described and important roles of these regions on binding and biophysical features of antibodies will be discussed. Then, general features of epitope regions of protein antigens will be introduced along with several computational/experimental tools to identify them. Lastly, a rising star of antibody biopharmaceuticals, nanobodies, will be described to show importance of next-generation antibody fragment based biopharmaceuticals in drug development.

Optimizing antibody affinity and stability by the automated design of the variable light-heavy chain interfaces

Antibodies developed for research and clinical applications may exhibit suboptimal stability, expressibility, or affinity. Existing optimization strategies focus on surface mutations, whereas natural affinity maturation also introduces mutations in the antibody core, simultaneously improving stability and affinity. To systematically map the mutational tolerance of an antibody variable fragment (Fv), we performed yeast display and applied deep mutational scanning to an anti-lysozyme antibody and found that many of the affinity-enhancing mutations clustered at the variable light-heavy chain interface, within the antibody core. Rosetta design combined enhancing mutations, yielding a variant with tenfold higher affinity and substantially improved stability. To make this approach broadly accessible, we developed AbLIFT, an automated web server that designs multipoint core mutations to improve contacts between specific Fv light and heavy chains (http://AbLIFT.weizmann.ac.il). We applied AbLIFT to two unrelated antibodies targeting the human antigens VEGF and QSOX1. Strikingly, the designs improved stability, affinity, and expression yields. The results provide proof-of-principle for bypassing laborious cycles of antibody engineering through automated computational affinity and stability design.

Antibodies are highly important in research, biotechnology, and medical applications. Despite their great utility, however, many antibodies exhibit suboptimal stability and affinity, raising production costs and limiting their practical usefulness. To tackle this general limitation, we used deep mutational scanning to characterize the effects of mutations in an antibody variable fragment on its antigen-binding affinity. Surprisingly, many of the affinity-enhancing mutations clustered at the variable light-heavy chain interface. We, therefore, developed an automated method, called AbLIFT (http://AbLIFT.weizmann.ac.il) to optimize this interface through design. Two unrelated antibodies were tested and showed improvements in expression levels, stability, and antigen-binding affinity. Since AbLIFT requires testing of only a few dozen specific designs, it may dramatically accelerate the development of promising antibodies into useful research and clinical tools.

Germinality does not necessarily define mAb expression and thermal stability

The production potential of recombinant monoclonal antibody (mAb) expressing cell lines depends, among other factors, on the intrinsic antibody structure determined by the amino acid sequence. In this study, we investigated the influence of somatic mutations in the V(D)J sequence of four individual, mature model mAbs on the expression potential. Therefore, we defined four couples, each consisting of one naturally occurring mAb (2G12, Ustekinumab, 4B3, and 2F5) and the corresponding germline-derived cognate mAb (353/11, 554/12, 136/63, and 236/14). For all eight mAb variants, recombinant Chinese hamster ovary (CHO) cell lines were developed with mAbs expressed from a defined chromosomal locus. The presented workflow investigates critical parameters including productivity, intra- and extracellular product profile, XBP1 splicing, thermal stability, and in silico hydrophobicity. Significant differences in productivity were even observed between the germline-derived mAbs which did not undergo somatic mutagenesis. Accordingly, back-to-germline mutations of mature mAbs are not necessarily reflecting improved expression and stability but indicate opportunities and limits of mAb engineering. From our studies, we conclude that germinalization represents a potential to improve mAb properties depending on the antibody’s germline family, highlighting the fact that mAbs should be treated individually.

Enzymes for Detoxification of Various Mycotoxins: Origins and Mechanisms of Catalytic Action

Mycotoxins are highly dangerous natural compounds produced by various fungi. Enzymatic transformation seems to be the most promising method for detoxification of mycotoxins. This review summarizes current information on enzymes of different classes to convert various mycotoxins. An in-depth analysis of 11 key enzyme mechanisms towards dozens of major mycotoxins was realized. Additionally, molecular docking of mycotoxins to enzymes’ active centers was carried out to clarify some of these catalytic mechanisms. Analyzing protein homologues from various organisms (plants, animals, fungi, and bacteria), the prevalence and availability of natural sources of active biocatalysts with a high practical potential is discussed. The importance of multifunctional enzyme combinations for detoxification of mycotoxins is posed.

Trends in therapeutic antibody affinity maturation: From in-vitro towards next-generation sequencing approaches

Current advances in antibody engineering driving the strongest growth area in biotherapeutic agents development. Affinity improvement that is mainly important for biological activity and clinical efficacy of therapeutic antibodies, has still remained a challenging task. In the human body, during a course of immune response affinity maturation increase antibody activity by several rounds of somatic hypermutation and clonal selection in the germinal center. The final outputs are antibodies representing higher affinity and specificity against a particular antigen. In the realm of biotechnology, exploring of mutations which improve antibody affinity while preserving its specificity and stability is an extremely time-consuming and laborious process. Recent advances in computational algorithms and DNA sequencing technologies help researchers to redesign antibody structure to achieve desired properties such as improved binding affinity. In this review, we briefly described the principle of affinity maturation and different corresponding in vitro techniques. Also, we recapitulated the most recent advancements in the field of antibody affinity maturation including computational approaches and next-generation sequencing (NGS).