Deep Sequencing-guided Design of a High Affinity Dual Specificity Antibody to Target Two Angiogenic Factors in Neovascular Age-related Macular Degeneration

Leveraging neonatal Fc receptor (FcRn) to enhance antibody transport across the blood brain barrier

The blood-brain barrier (BBB) restricts efficient penetration of systemically delivered therapeutic antibodies into the brain, limiting the development of this class of drugs to treat neurodegenerative diseases. Here we demonstrate that the neonatal Fc receptor (FcRn), which is highly expressed at the BBB, can be used to facilitate IgG transport to the brain. Engineering of the antibody Fc region to promote binding to FcRn at neutral pH enhances antibody transcytosis in a cellular model. In vivo, these modifications improve brain penetration, as well as brain target engagement and activity, of systemically administered antibodies in both mice and non-human primates. This engineering approach can be broadly implemented to enhance central nervous system (CNS) exposure of antibody- and Fc-based drugs, improving the clinical potential of biotherapeutics for the treatment of human brain diseases.

Amino acids characterization based on frequency and interaction analysis in human antigen-antibody complexes from Thera-SAbDab

BackgroundAntibodies are composed of light and heavy chains, both of which have constant and variable regions. The diversity, specific binding ability and therapeutic potential of antibodies are determined by hypervariable loops called complementarity-determining regions (CDRs), with the other regions being the framework regions.ObjectiveTo investigate the key amino acid patterns in various antibody regions in the human therapeutic antigen-antibody (Ag-Ab) complexes collected from the Thera-SAbDab database.MethodThe study focuses on identifying the amino acid frequency, diversity index in CDRs, paratope-epitope amino acid interactions, amino acid bond formation frequency, and bond types among selected therapeutic Ag-Ab complexes.ResultsThe results revealed that Ser is highly distributed in the overall light chain CDRs while Gly is highly distributed in the heavy chain CDRs. CDR profiling analysis indicated that the average amino acid diversity in heavy chain CDRs is 60% to 70%, while in the light chain, it is 50% to 60%. Aromatic residues such as Tyr, Trp and Phe are the top contributors to these paratope-epitope interactions in the light and heavy chains. Moreover, we examined the frequency of amino acids in light and heavy chains of Ag-Ab complexes. Importantly, the outcome of this study leverages the in depth analysis on single residues, dipeptides, and tripeptides for the therapeutic Ag-Ab complexes.ConclusionWe conclude that the amino acid frequency and interaction analysis centered on therapeutic Ag-Ab complexes will benefit antibody engineering parameters such as antibody design, optimization, affinity maturation, and overall antibody development.

Epitope mapping via in vitro deep mutational scanning methods and its applications

Epitope mapping is a technique employed to define the region of an antigen that elicits an immune response, providing crucial insight into the structural architecture of the antigen as well as epitope-paratope interactions. With this breadth of knowledge, immunotherapies, diagnostics, and vaccines are being developed with a rational and data-supported design. Traditional epitope mapping methods are laborious, time-intensive, and often lack the ability to screen proteins in a high-throughput manner or provide high resolution. Deep mutational scanning (DMS), however, is revolutionizing the field as it can screen all possible single amino acid mutations and provide an efficient and high-throughput way to infer the structures of both linear and three-dimensional epitopes with high resolution. Currently, more than 50 publications take this approach to efficiently identify enhancing or escaping mutations, with many then employing this information to rapidly develop broadly neutralizing antibodies, T-cell immunotherapies, vaccine platforms, or diagnostics. We provide a comprehensive review of the approaches to accomplish epitope mapping while also providing a summation of the development of DMS technology and its impactful applications.

Structural Insights into Helix-Loop-Helix Peptides for "Ligand-Targeting" Intracellular Drug Delivery via VEGF Receptor-Mediated Endocytosis

As a new alternative to antibody-drug conjugates, we developed "ligand-targeting" peptide-drug conjugates (PDCs), in which conformationally constrained helix-loop-helix (HLH) peptide M49 targeting human vascular endothelial growth factor-A (VEGF) was used as a drug carrier. The biochemical study showed that HLH peptide M49 made a complex with VEGF in the extracellular environment, and then the M49/VEGF complex interacts with the receptor on the cell surface to induce cellular internalization via the endocytic pathway. Here, we present an X-ray crystal structure of the M49/VEGF complex at 1.5 Å resolution using a protein crystal grown in microgravity. The structure illustrated the "ligand-targeting" cellular uptake mechanism for intracellular drug delivery and the molecular basis on the peptide-VEGF binding mode with tight binding and high target specificity. In addition, mutational studies and thermodynamic analysis provided information on the driving forces of the complex formation. This work would contribute to the design of mid-size molecular-targeting peptides as well as HLH peptides, advancing the research in drug discovery and chemical biology.

Disulfi de constrained Fabs overcome target size limitation for high-resolution single-particle cryo-EM

High-resolution structures of proteins are critical to understanding molecular mechanisms of biological processes and in the discovery of therapeutic molecules. Cryo-EM has revolutionized structure determination of large proteins and their complexes1, but a vast majority of proteins that underlie human diseases are small (< 50 kDa) and usually beyond its reach due to low signal-to-noise images and difficulties in particle alignment2. Current strategies to overcome this problem increase the overall size of small protein targets using scaffold proteins that bind to the target, but are limited by inherent flexibility and not being bound to their targets in a rigid manner, resulting in the target being poorly resolved compared to the scaffolds3-11. Here we present an iteratively engineered molecular design for transforming Fabs (antibody fragments), into conformationally rigid scaffolds (Rigid-Fabs) that, when bound to small proteins (~20 kDa), can enable high-resolution structure determination using cryo-EM. This design introduces multiple disulfide bonds at strategic locations, generates a well-folded Fab constrained into a rigid conformation and can be applied to Fabs from various species, isotypes and chimeric Fabs. We present examples of the Rigid Fab design enabling high-resolution (2.3-2.5 Å) structures of small proteins, Ang2 (26 kDa) and KRAS (21 kDa) by cryo-EM. The strategies for designing disulfide constrained Rigid Fabs in our work thus establish a general approach to overcome the target size limitation of single particle cryo-EM.

Meta learning addresses noisy and under-labeled data in machine learning-guided antibody engineering

Machine learning-guided protein engineering is rapidly progressing; however, collecting high-quality, large datasets remains a bottleneck. Directed evolution and protein engineering studies often require extensive experimental processes to eliminate noise and label protein sequence-function data. Meta learning has proven effective in other fields in learning from noisy data via bi-level optimization given the availability of a small dataset with trusted labels. Here, we leverage meta learning approaches to overcome noisy and under-labeled data and expedite workflows in antibody engineering. We generate yeast display antibody mutagenesis libraries and screen them for target antigen binding followed by deep sequencing. We then create representative learning tasks, including learning from noisy training data, positive and unlabeled learning, and learning out of distribution properties. We demonstrate that meta learning has the potential to reduce experimental screening time and improve the robustness of machine learning models by training with noisy and under-labeled training data.

CD200R1 immune checkpoint blockade by the first-in-human anti-CD200R1 antibody 23ME-00610: molecular mechanism and engineering of a surrogate antibody

Human CD200R1 (hCD200R1), an immune inhibitory receptor expressed predominantly on T cells and myeloid cells, was identified as a promising immuno-oncology target by the 23andMe database. Blockade of CD200R1-dependent signaling enhances T cell-mediated antitumor activity in vitro and in vivo. 23ME-00610 is a potential first-in-class, humanized IgG1 investigational antibody that binds hCD200R1 with high affinity. We have previously shown that 23ME-00610 inhibits the hCD200R1 immune checkpoint function. Herein, we dissect the molecular mechanism of 23ME-00610 blockade of hCD200R1 by solving the crystal structure of 23ME-00610 Fab in complex with hCD200R1 and performing mutational studies, which show 23ME-00610 blocks the interaction between hCD200 and hCD200R1 through steric hindrance. However, 23ME-00610 does not bind CD200R1 of preclinical species such as cynomolgus monkey MfCD200R1. To enable preclinical toxicology studies of CD200R1 blockade in a pharmacologically relevant non-clinical species, we engineered a surrogate antibody with high affinity toward MfCD200R1. We used phage display libraries of 23ME-00610 variants with individual CDR residues randomized to all 20 amino acids, from which we identified mutations that switched on MfCD200R1 binding. Structural analysis suggests how the surrogate, named 23ME-00611, acquires the ortholog binding ability at the equivalent epitope of 23ME-00610. This engineering approach does not require a priori knowledge of structural and functional mapping of antibody-antigen interaction and thus is generally applicable for therapeutic antibody development when desired ortholog binding is lacking. These findings provide foundational insights as 23ME-00610 advances in clinical studies to gain understanding of the hCD200R1 immune checkpoint as a target in immuno-oncology.

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.

Protein engineering via sequence-performance mapping

Discovery and evolution of new and improved proteins has empowered molecular therapeutics, diagnostics, and industrial biotechnology. Discovery and evolution both require efficient screens and effective libraries, although they differ in their challenges because of the absence or presence, respectively, of an initial protein variant with the desired function. A host of high-throughput technologies-experimental and computational-enable efficient screens to identify performant protein variants. In partnership, an informed search of sequence space is needed to overcome the immensity, sparsity, and complexity of the sequence-performance landscape. Early in the historical trajectory of protein engineering, these elements aligned with distinct approaches to identify the most performant sequence: selection from large, randomized combinatorial libraries versus rational computational design. Substantial advances have now emerged from the synergy of these perspectives. Rational design of combinatorial libraries aids the experimental search of sequence space, and high-throughput, high-integrity experimental data inform computational design. At the core of the collaborative interface, efficient protein characterization (rather than mere selection of optimal variants) maps sequence-performance landscapes. Such quantitative maps elucidate the complex relationships between protein sequence and performance-e.g., binding, catalytic efficiency, biological activity, and developability-thereby advancing fundamental protein science and facilitating protein discovery and evolution.

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.

Accurate profiling of full-length Fv in highly homologous antibody libraries using UMI tagged short reads

Deep parallel sequencing (NGS) is a viable tool for monitoring scFv and Fab library dynamics in many antibody engineering high-throughput screening efforts. Although very useful, the commonly used Illumina NGS platform cannot handle the entire sequence of scFv or Fab in a single read, usually focusing on specific CDRs or resorting to sequencing VH and VL variable domains separately, thus limiting its utility in comprehensive monitoring of selection dynamics. Here we present a simple and robust method for deep sequencing repertoires of full length scFv, Fab and Fv antibody sequences. This process utilizes standard molecular procedures and unique molecular identifiers (UMI) to pair separately sequenced VH and VL. We show that UMI assisted VH-VL matching allows for a comprehensive and highly accurate mapping of full length Fv clonal dynamics in large highly homologous antibody libraries, as well as identification of rare variants. In addition to its utility in synthetic antibody discovery processes, our method can be instrumental in generating large datasets for machine learning (ML) applications, which in the field of antibody engineering has been hampered by conspicuous paucity of large scale full length Fv data.

An optimized thermodynamics integration protocol for identifying beneficial mutations in antibody design

Accurate identification of beneficial mutations is central to antibody design. Many knowledge-based (KB) computational approaches have been developed to predict beneficial mutations, but their accuracy leaves room for improvement. Thermodynamic integration (TI) is an alchemical free energy algorithm that offers an alternative technique for identifying beneficial mutations, but its performance has not been evaluated. In this study, we developed an efficient TI protocol with high accuracy for predicting binding free energy changes of antibody mutations. The improved TI method outperforms KB methods at identifying both beneficial and deleterious mutations. We observed that KB methods have higher accuracies in predicting deleterious mutations than beneficial mutations. A pipeline using KB methods to efficiently exclude deleterious mutations and TI to accurately identify beneficial mutations was developed for high-throughput mutation scanning. The pipeline was applied to optimize the binding affinity of a broadly sarbecovirus neutralizing antibody 10-40 against the circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant. Three identified beneficial mutations show strong synergy and improve both binding affinity and neutralization potency of antibody 10-40. Molecular dynamics simulation revealed that the three mutations improve the binding affinity of antibody 10-40 through the stabilization of an altered binding mode with increased polar and hydrophobic interactions. Above all, this study presents an accurate and efficient TI-based approach for optimizing antibodies and other biomolecules.

Understanding and Modulating Antibody Fine Specificity: Lessons from Combinatorial Biology

Combinatorial biology methods such as phage and yeast display, suitable for the generation and screening of huge numbers of protein fragments and mutated variants, have been useful when dissecting the molecular details of the interactions between antibodies and their target antigens (mainly those of protein nature). The relevance of these studies goes far beyond the mere description of binding interfaces, as the information obtained has implications for the understanding of the chemistry of antibody–antigen binding reactions and the biological effects of antibodies. Further modification of the interactions through combinatorial methods to manipulate the key properties of antibodies (affinity and fine specificity) can result in the emergence of novel research tools and optimized therapeutics.

A Generic Strategy to Generate Bifunctional Two-in-One Antibodies by Chicken Immunization

Various formats of bispecific antibodies exist, among them Two-in-One antibodies in which each Fab arm can bind to two different antigens. Their IgG-like architecture accounts for low immunogenicity and also circumvents laborious engineering and purification steps to facilitate correct chain pairing. Here we report for the first time the identification of a Two‐in‐One antibody by yeast surface display (YSD) screening of chicken-derived immune libraries. The resulting antibody simultaneously targets the epidermal growth factor receptor (EGFR) and programmed death‐ligand 1 (PD-L1) at the same Fv fragment with two non-overlapping paratopes. The dual action Fab is capable of inhibiting EGFR signaling by binding to dimerization domain II as well as blocking the PD-1/PD-L1 interaction. Furthermore, the Two-in-One antibody demonstrates specific cellular binding properties on EGFR/PD-L1 double positive tumor cells. The presented strategy relies solely on screening of combinational immune-libraries and obviates the need for any additional CDR engineering as described in previous reports. Therefore, this study paves the way for further development of therapeutic antibodies derived from avian immunization with novel and tailor-made binding properties.

Deep mutational scanning for therapeutic antibody engineering

The biophysical and functional properties of monoclonal antibody (mAb) drug candidates are often improved by protein engineering methods to increase the probability of clinical efficacy. One emerging method is deep mutational scanning (DMS) which combines the power of exhaustive protein mutagenesis and functional screening with deep sequencing and bioinformatics. The application of DMS has yielded significant improvements to the affinity, specificity, and stability of several preclinical antibodies alongside novel applications such as introducing multi-specific binding properties. DMS has also been applied directly on target antigens to precisely map antibody-binding epitopes and notably to profile the mutational escape potential of viral targets (e.g., SARS-CoV-2 variants). Finally, DMS combined with machine learning is enabling advances in the computational screening and engineering of therapeutic antibodies.

Secondary Structure and Conformational Stability of the Antigen Residues Making Contact with Antibodies

Antibodies are crucial biomolecules that bring high therapeutic efficacy in medicine and accurate molecular detection in diagnosis. Many studies have been devoted to analyzing the antigen-antibody interaction from the importance of understanding the antibody recognition mechanism. However, most of the previous studies examined the characteristic of the antibody for interaction. It is also informative to clarify the significant antigen residues contributing to the binding. To characterize the molecular interaction of antigens, we computationally analyzed 350 antigen-antibody complex structures by molecular mechanics (MM) calculations and molecular dynamics (MD) simulations. Based on the MM calculations, the antigen residues contributing to the binding were extracted from all the 350 complexes. The extracted residues are located at the antigen-antibody interface and are responsible for making contact with the antibody. The appearances of the charged polar residues, Asp, Glu, Arg, and Lys, were noticeably large. In contrast, the populations of the hydrophobic residues, Leu, Val, and Ala, were relatively low. The appearance frequencies of the other amino acid residues were almost close to the abundance of general proteins of eukaryotes. The binding score indicated that the hydrophilic interaction was dominant at the antigen-antibody contact instead of the hydrophobic one. The positively charged residues, Arg and Lys, remarkably contributed to the binding compared to the negatively charged ones, Asp and Glu. Considerable contributions were also observed for the noncharged polar residues, Asn and Gln. The analysis of the secondary structures of the extracted antigen residues suggested that there was no marked difference in recognition by antibodies among helix, sheet, turn, and coil. A long helix of the antigen sometimes made contact with antibody complementarity-determining regions, and a large sheet also frequently covered the antibody heavy and light chains. The turn structure was the most popularly observed at the contact with antibody among 350 complexes. Three typical complexes were picked up for each of the four secondary structures. MD simulations were performed to examine the stability of the interfacial structures of the antigens for these 12 complex models. The alterations of secondary structures were monitored through the simulations. The structural fluctuations of the contact residues were low compared with the other domains of antigen molecules. No drastic conversion was observed for every model during the 100 ns simulation. The motions of the interfacial antigen residues were small compared to the other residues on the protein surface. Therefore, diverse molecular conformations are possible for antibody recognition as long as the target areas are polar, nonflexible, and protruding on the protein surface.

Analysis of Binding Modes of Antigen-Antibody Complexes by Molecular Mechanics Calculation

Antibodies are one of the most important protein molecules in biopharmaceutics. Due to the recent advance in technology for producing monoclonal antibodies, many structural data are available on the antigen-antibody complexes. To characterize the molecular interaction in antigen-antibody recognition, we computationally analyzed 500 complex structures by molecular mechanics calculations. The presence of Ser and Tyr is markedly large in the complementarity-determining regions (CDRs). Although Ser is abundant in CDRs, its contribution to the binding score is not large. Instead, Tyr, Asp, Glu, and Arg significantly contribute to the molecular interaction from the viewpoint of the binding score. The decomposition of the binding score suggests that the hydrophilic interaction is predominant in all CDRs compared with the hydrophobic one. The contribution of the heavy chain is larger than that of the light chain. In particular, H2 and H3 largely contribute to the binding interaction. Tyr is a main contributing residue both in H2 and H3. The positively charged residue Arg also significantly contributes to the binding score in H3, while the contribution of Lys is small. The appearance of Ser is remarkable in H2, and Asp is abundant in H3. The non-charged polar residues, Thr, Asn, and Gln, appear much in H2, compared to appearing in H3. The negatively charged residues Asp and Glu significantly contribute to the binding score in H3. The contributions of Phe and Trp are not large in spite that the aromatic residues are capable of making the π-π or CH-π interaction. Gly is commonly abundant both in H2 and H3. The average distance of the shortest direct hydrogen bond between the antigen and antibody is longer than that of the hydrogen bonds observed in the complexes between compounds and their target proteins. Therefore, the antigen-antibody interface is not so tight as the compound-target protein interface. The calculation of shape complementarity is consistent with the result of the hydrogen bonds in that the fitness of the antigen-antibody contact is not so high as that of the compound-target protein contact. There exist many water molecules at the antigen-antibody interface. These findings suggest that Tyr, Asp, Glu, and Arg are rich in H3 and work as major contributors for the interaction with the antigen. Ser, Thr, Asn, and Gln are rich in H2 and support the interaction with enhancing molecular fitness. Gly is helpful in increasing flexibility and geometrical diversity. Because the antigen-antibody binding is fundamentally hydrophilic-driven, the non-polar residues are unfavorable for mediating the contact even for the aromatic residues such as Phe and Trp.

Antibody design using LSTM based deep generative model from phage display library for affinity maturation

Molecular evolution is an important step in the development of therapeutic antibodies. However, the current method of affinity maturation is overly costly and labor-intensive because of the repetitive mutation experiments needed to adequately explore sequence space. Here, we employed a long short term memory network (LSTM)-a widely used deep generative model-based sequence generation and prioritization procedure to efficiently discover antibody sequences with higher affinity. We applied our method to the affinity maturation of antibodies against kynurenine, which is a metabolite related to the niacin synthesis pathway. Kynurenine binding sequences were enriched through phage display panning using a kynurenine-binding oriented human synthetic Fab library. We defined binding antibodies using a sequence repertoire from the NGS data to train the LSTM model. We confirmed that likelihood of generated sequences from a trained LSTM correlated well with binding affinity. The affinity of generated sequences are over 1800-fold higher than that of the parental clone. Moreover, compared to frequency based screening using the same dataset, our machine learning approach generated sequences with greater affinity.

Learning the heterogeneous hypermutation landscape of immunoglobulins from high-throughput repertoire data

Somatic hypermutations of immunoglobulin (Ig) genes occurring during affinity maturation drive B-cell receptors’ ability to evolve strong binding to their antigenic targets. The landscape of these mutations is highly heterogeneous, with certain regions of the Ig gene being preferentially targeted. However, a rigorous quantification of this bias has been difficult because of phylogenetic correlations between sequences and the interference of selective forces. Here, we present an approach that corrects for these issues, and use it to learn a model of hypermutation preferences from a recently published large IgH repertoire dataset. The obtained model predicts mutation profiles accurately and in a reproducible way, including in the previously uncharacterized Complementarity Determining Region 3, revealing that both the sequence context of the mutation and its absolute position along the gene are important. In addition, we show that hypermutations occurring concomittantly along B-cell lineages tend to co-localize, suggesting a possible mechanism for accelerating affinity maturation.

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.

Conformational changes in a Vernier zone region: Implications for antibody dual specificity

Understanding the determinants of antibody specificity is one of the challenging tasks in antibody development. Monospecific antibodies are still dominant in approved antibody therapeutics but there is a significant body of work to show that multispecific antibodies can increase the overall therapeutic effect. Dual-specific or "Two-in-One" antibodies can bind to two different antigens separately with the same antigen-binding site as opposed to bispecifics, which simultaneously bind to two different antigens through separate antigen-binding units. These nonstandard dual-specific antibodies were recently shown to be promising for new antibody-based therapeutics. Here, we physicochemically and structurally analyzed six different antibodies of which two are monospecific and four are dual-specific antibodies derived from monospecific templates to gain insight about dual-specificity determinants. These dual-specific antibodies can target both human epidermal growth factor receptor 2 and vascular endothelial growth factor at different binding affinities. We showed that a particular region of clustered Vernier zone residues might play key roles in gaining dual specificity. While there are minimal intramolecular interactions between a certain Vernier zone region, namely LV4 and LCDR1 of monospecific template, there is a significant structural change and consequently close contact formation between LV4-LCDR1 loops of derived dual-specific antibodies. Although Vernier zone residues were previously shown to be important for humanization applications, they are mostly underestimated in the literature. Here, we also aim to resurrect Vernier zone residues for antibody engineering efforts.

Quantitative mapping of binding specificity landscapes for homologous targets by using a high-throughput method

To facilitate investigations of protein-protein interactions (PPIs), we developed a novel platform for quantitative mapping of protein binding specificity landscapes, which combines the multi-target screening of a mutagenesis library into high- and low-affinity populations with sophisticated next-generation sequencing analysis. Importantly, this method generates accurate models to predict affinity and specificity values for any mutation within a protein complex, and requires only a few experimental binding affinity measurements using purified proteins for calibration. We demonstrated the utility of the approach by mapping quantitative landscapes for interactions between the N-terminal domain of the tissue inhibitor of metalloproteinase 2 (N-TIMP2) and three matrix metalloproteinases (MMPs) having homologous structures but different affinities (MMP-1, MMP-3, and MMP-14). The binding landscapes for N-TIMP2/MMP-1 and N-TIMP2/MMP-3 showed the PPIs to be almost fully optimized, with most single mutations giving a loss of affinity. In contrast, the non-optimized PPI for N-TIMP2/MMP-14 was reflected in a wide range of binding affinities, where single mutations exhibited a far more attenuated effect on the PPI. Our new platform reliably and comprehensively identified not only hot- and cold-spot residues, but also specificity-switch mutations that shape target affinity and specificity. Thus, our approach provides a methodology giving an unprecedentedly rich quantitative analysis of the binding specificity landscape, which will broaden the understanding of the mechanisms and evolutionary origins of specific PPIs and facilitate the rational design of specific inhibitors for structurally similar target proteins.

Adaption of human antibody λ and κ light chain architectures to CDR repertoires

Monoclonal antibodies bind with high specificity to a wide range of diverse antigens, primarily mediated by their hypervariable complementarity determining regions (CDRs). The defined antigen binding loops are supported by the structurally conserved β-sandwich framework of the light chain (LC) and heavy chain (HC) variable regions. The LC genes are encoded by two separate loci, subdividing the entity of antibodies into kappa (LC_κ) and lambda (LC_λ) isotypes that exhibit distinct sequence and conformational preferences. In this work, a diverse set of techniques were employed including machine learning, force field analysis, statistical coupling analysis and mutual information analysis of a non-redundant antibody structure collection. Thereby, it was revealed how subtle changes between the structures of LC_κ and LC_λ isotypes increase the diversity of antibodies, extending the predetermined restrictions of the general antibody fold and expanding the diversity of antigen binding. Interestingly, it was found that the characteristic framework scaffolds of κ and λ are stabilized by diverse amino acid clusters that determine the interplay between the respective fold and the embedded CDR loops. In conclusion, this work reveals how antibodies use the remarkable plasticity of the beta-sandwich Ig fold to incorporate a large diversity of CDR loops.

A bispecific IgG format containing four independent antigen binding sites

Bispecific antibodies come in many different formats, including the particularly interesting two-in-one antibodies, where one conventional IgG binds two different antigens. The IgG format allows these antibodies to mediate Fc-related functionality, and their wild-type structure ensures low immunogenicity and enables standard methods to be used for development. It is however difficult, time-consuming and costly to generate two-in-one antibodies. Herein we demonstrate a new approach to create a similar type of antibody by combining two different variable heavy (VH) domains in each Fab arm of an IgG, a tetra-VH IgG format. The VHs are used as building blocks, where one VH is placed at its usual position, and the second VH replaces the variable light (VL) domain in a conventional IgG. VH domains, binding several different types of antigens, were discovered and could be rearranged in any combination, offering a convenient "plug and play" format. The tetra-VH IgGs were found to be functionally tetravalent, binding two antigens on each arm of the IgG molecule simultaneously. This offers a new strategy to also create monospecific, tetravalent IgGs that, depending on antigen architecture and mode-of-action, may have enhanced efficacy compared to traditional bivalent antibodies.

Next-generation sequencing-guided identification and reconstruction of antibody CDR combinations from phage selection outputs

Next-generation sequencing (NGS) technologies have been employed in several phage display platforms for analyzing natural and synthetic antibody sequences and for identifying and reconstructing single-chain variable fragments (scFv) and antigen-binding fragments (Fab) not found by conventional ELISA screens. In this work, we developed an NGS-assisted antibody discovery platform by integrating phage-displayed, single-framework, synthetic Fab libraries. Due to limitations in attainable read and amplicon lengths, NGS analysis of Fab libraries and selection outputs is usually restricted to either V_H or V_L. Since this information alone is not sufficient for high-throughput reconstruction of Fabs, we developed a rapid and simple method for linking and sequencing all diversified CDRs in phage Fab pools. Our method resulted in a reliable and straightforward platform for converting NGS information into Fab clones. We used our NGS-assisted Fab reconstruction method to recover low-frequency rare clones from phage selection outputs. While previous studies chose rare clones for rescue based on their relative frequencies in sequencing outputs, we chose rare clones for reconstruction from less-frequent CDRH3 lengths. In some cases, reconstructed rare clones (frequency ∼0.1%) showed higher affinity and better specificity than high-frequency top clones identified by Sanger sequencing, highlighting the significance of NGS-based approaches in synthetic antibody discovery.

Circular permutation profiling by deep sequencing libraries created using transposon mutagenesis

Deep mutational scanning has been used to create high-resolution DNA sequence maps that illustrate the functional consequences of large numbers of point mutations. However, this approach has not yet been applied to libraries of genes created by random circular permutation, an engineering strategy that is used to create open reading frames that express proteins with altered contact order. We describe a new method, termed circular permutation profiling with DNA sequencing (CPP-seq), which combines a one-step transposon mutagenesis protocol for creating libraries with a functional selection, deep sequencing and computational analysis to obtain unbiased insight into a protein's tolerance to circular permutation. Application of this method to an adenylate kinase revealed that CPP-seq creates two types of vectors encoding each circularly permuted gene, which differ in their ability to express proteins. Functional selection of this library revealed that >65% of the sampled vectors that express proteins are enriched relative to those that cannot translate proteins. Mapping enriched sequences onto structure revealed that the mobile AMP binding and rigid core domains display greater tolerance to backbone fragmentation than the mobile lid domain, illustrating how CPP-seq can be used to relate a protein's biophysical characteristics to the retention of activity upon permutation.

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).

Functional assays for transcription mechanisms in high-throughput

Dramatic increases in the scale of programmed synthesis of nucleic acid libraries coupled with deep sequencing have powered advances in understanding nucleic acid and protein biology. Biological systems centering on nucleic acids or encoded proteins greatly benefit from such high-throughput studies, given that large DNA variant pools can be synthesized and DNA, or RNA products of transcription, can be easily analyzed by deep sequencing. Here we review the scope of various high-throughput functional assays for studies of nucleic acids and proteins in general, followed by discussion of how these types of study have yielded insights into the RNA Polymerase II (Pol II) active site as an example. We discuss methodological considerations in the design and execution of these experiments that should be valuable to studies in any system.

Epistasis in a Fitness Landscape Defined by Antibody-Antigen Binding Free Energy

Epistasis is the phenomenon by which the effect of a mutation depends on its genetic background. While it is usually defined in terms of organismal fitness, for single proteins, it must reflect physical interactions among residues. Here, we systematically extract the specific contribution pairwise epistasis makes to the physical affinity of antibody-antigen binding relevant to affinity maturation, a process of accelerated Darwinian evolution. We find that, among competing definitions of affinity, the binding free energy is the most appropriate to describe epistasis. We show that epistasis is pervasive, accounting for 25%-35% of variability, of which a large fraction is beneficial. This work suggests that epistasis both constrains, through negative epistasis, and enlarges, through positive epistasis, the set of possible evolutionary paths that can produce high-affinity sequences during repeated rounds of mutation and selection.

Mapping protein selectivity landscapes using multi-target selective screening and next-generation sequencing of combinatorial libraries

Characterizing the binding selectivity landscape of interacting proteins is crucial both for elucidating the underlying mechanisms of their interaction and for developing selective inhibitors. However, current mapping methods are laborious and cannot provide a sufficiently comprehensive description of the landscape. Here, we introduce a novel and efficient strategy for comprehensively mapping the binding landscape of proteins using a combination of experimental multi-target selective library screening and in silico next-generation sequencing analysis. We map the binding landscape of a non-selective trypsin inhibitor, the amyloid protein precursor inhibitor (APPI), to each of the four human serine proteases (kallikrein-6, mesotrypsin, and anionic and cationic trypsins). We then use this map to dissect and improve the affinity and selectivity of APPI variants toward each of the four proteases. Our strategy can be used as a platform for the development of a new generation of target-selective probes and therapeutic agents based on selective protein-protein interactions.

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.

Barcoded sequencing workflow for high throughput digitization of hybridoma antibody variable domain sequences

Since the invention of Hybridoma technology by Milstein and Köhler in 1975, its application has greatly advanced the antibody discovery process. The technology enables both functional screening and long-term archival of the immortalized monoclonal antibody producing B cells. Despite the dependable cryopreservation technology for hybridoma cells, practicality of long-term storage has been outpaced by recent progress in robotics and automations, which enables routine identification of thousands of antigen specific hybridoma clones. Such throughput increase imposes two nascent challenges in the antibody discovery process, namely limited cryopreservation storage space and limited throughput in conventional antibody sequencing. We herein provide a barcoded sequencing workflow that utilizes next generation sequencing to expand the conventional sequencing capacity. Accompanied with the bioinformatics tools we describe, the barcoded sequencing workflow robustly reports unambiguous antibody sequences as confirmed with Sanger sequencing controls. In complement with the commonly accessible recombinant DNA technology, the barcoded sequencing workflow allows for high throughput digitization of the antibody sequences and provides an effective solution to the limitations imposed by physical storage and sequencing capacity.

Hierarchical design of artificial proteins and complexes toward synthetic structural biology

In multiscale structural biology, synthetic approaches are important to demonstrate biophysical principles and mechanisms underlying the structure, function, and action of bio-nanomachines. A central goal of "synthetic structural biology" is the design and construction of artificial proteins and protein complexes as desired. In this paper, I review recent remarkable progress of an array of approaches for hierarchical design of artificial proteins and complexes that signpost the path forward toward synthetic structural biology as an emerging interdisciplinary field. Topics covered include combinatorial and protein-engineering approaches for directed evolution of artificial binding proteins and membrane proteins, binary code strategy for structural and functional de novo proteins, protein nanobuilding block strategy for constructing nano-architectures, protein-metal-organic frameworks for 3D protein complex crystals, and rational and computational approaches for design/creation of artificial proteins and complexes, novel protein folds, ideal/optimized protein structures, novel binding proteins for targeted therapeutics, and self-assembling nanomaterials. Protein designers and engineers look toward a bright future in synthetic structural biology for the next generation of biophysics and biotechnology.

A multispecific monoclonal antibody G2 recognizes at least three completely different epitope sequences with high affinity

A monoclonal antibody (mAb) G2 possesses an unusual characteristic of reacting with at least three proteins (ATP6V1C1, SEPT3, and C6H10orf76) other than its original antigen, chicken prion protein (ChPrP). The epitopes on ChPrP and ATP6V1C1 have been identified previously. In this study, we identified the epitope in the third protein, SEPT3. Interestingly, there was no amino acid sequence similarity among the epitopes on the three proteins. These epitopes had high binding affinities to G2 (K_D  = ∼10^-7 M for monovalent binding and K_D  = ∼10^-9 M for divalent binding), as determined using a SPR biosensor. This is the first report on a three-in-one mAb recognizing completely different epitope sequences with high affinity. Additionally, competitive ELISA indicated that the binding sites on G2, specific for the three different epitopes, overlapped, suggesting that the antigen-binding site may be flexible in the free form and capable of adapting to at least three different conformations to enable interactions with three different antigens.

Deep sequencing methods for protein engineering and design

The advent of next-generation sequencing (NGS) has revolutionized protein science, and the development of complementary methods enabling NGS-driven protein engineering have followed. In general, these experiments address the functional consequences of thousands of protein variants in a massively parallel manner using genotype-phenotype linked high-throughput functional screens followed by DNA counting via deep sequencing. We highlight the use of information rich datasets to engineer protein molecular recognition. Examples include the creation of multiple dual-affinity Fabs targeting structurally dissimilar epitopes and engineering of a broad germline-targeted anti-HIV-1 immunogen. Additionally, we highlight the generation of enzyme fitness landscapes for conducting fundamental studies of protein behavior and evolution. We conclude with discussion of technological advances.

Tuning the specificity of a Two-in-One Fab against three angiogenic antigens by fully utilizing the information of deep mutational scanning

Monoclonal antibodies developed for therapeutic or diagnostic purposes need to demonstrate highly defined binding specificity profiles. Engineering of an antibody to enhance or reduce binding to related antigens is often needed to achieve the desired biologic activity without safety concern. Here, we describe a deep sequencing-aided engineering strategy to fine-tune the specificity of an angiopoietin-2 (Ang2)/vascular endothelial growth factor (VEGF) dual action Fab, 5A12.1 for the treatment of age-related macular degeneration. This antibody utilizes overlapping complementarity-determining region (CDR) sites for dual Ang2/VEGF interaction with KD in the sub-nanomolar range. However, it also exhibits significant (KD of 4 nM) binding to angiopoietin-1, which has high sequence identity with Ang2. We generated a large phage-displayed library of 5A12.1 Fab variants with all possible single mutations in the 6 CDRs. By tracking the change of prevalence of each mutation during various selection conditions, we identified 35 mutations predicted to decrease the affinity for Ang1 while maintaining the affinity for Ang2 and VEGF. We confirmed the specificity profiles for 25 of these single mutations as Fab protein. Structural analysis showed that some of the Fab mutations cluster near a potential Ang1/2 epitope residue that differs in the 2 proteins, while others are up to 15 Å away from the antigen-binding site and likely influence the binding interaction remotely. The approach presented here provides a robust and efficient method for specificity engineering that does not require prior knowledge of the antigen antibody interaction and can be broadly applied to antibody specificity engineering projects.

Mutational landscape of antibody variable domains reveals a switch modulating the interdomain conformational dynamics and antigen binding

Somatic mutations within the antibody variable domains are critical to the immense capacity of the immune repertoire. Here, via a deep mutational scan, we dissect how mutations at all positions of the variable domains of a high-affinity anti-VEGF antibody G6.31 impact its antigen-binding function. The resulting mutational landscape demonstrates that large portions of antibody variable domain positions are open to mutation, and that beneficial mutations can be found throughout the variable domains. We determine the role of one antigen-distal light chain position 83, demonstrating that mutation at this site optimizes both antigen affinity and thermostability by modulating the interdomain conformational dynamics of the antigen-binding fragment. Furthermore, by analyzing a large number of human antibody sequences and structures, we demonstrate that somatic mutations occur frequently at position 83, with corresponding domain conformations observed for G6.31. Therefore, the modulation of interdomain dynamics represents an important mechanism during antibody maturation in vivo.

Affinity Maturation of a Cyclic Peptide Handle for Therapeutic Antibodies Using Deep Mutational Scanning

Meditopes are cyclic peptides that bind in a specific pocket in the antigen-binding fragment of a therapeutic antibody such as cetuximab. Provided their moderate affinity can be enhanced, meditope peptides could be used as specific non-covalent and paratope-independent handles in targeted drug delivery, molecular imaging, and therapeutic drug monitoring. Here we show that the affinity of a recently reported meditope for cetuximab can be substantially enhanced using a combination of yeast display and deep mutational scanning. Deep sequencing was used to construct a fitness landscape of this protein-peptide interaction, and four mutations were identified that together improved the affinity for cetuximab 10-fold to 15 nm Importantly, the increased affinity translated into enhanced cetuximab-mediated recruitment to EGF receptor-overexpressing cancer cells. Although in silico Rosetta simulations correctly identified positions that were tolerant to mutation, modeling did not accurately predict the affinity-enhancing mutations. The experimental approach reported here should be generally applicable and could be used to develop meditope peptides with low nanomolar affinity for other therapeutic antibodies.