Antibody humanization by structure-based computational protein design

Prospects for the computational humanization of antibodies and nanobodies

To be viable therapeutics, antibodies must be tolerated by the human immune system. Rational approaches to reduce the risk of unwanted immunogenicity involve maximizing the 'humanness' of the candidate drug. However, despite the emergence of new discovery technologies, many of which start from entirely human gene fragments, most antibody therapeutics continue to be derived from non-human sources with concomitant humanization to increase their human compatibility. Early experimental humanization strategies that focus on CDR loop grafting onto human frameworks have been critical to the dominance of this discovery route but do not consider the context of each antibody sequence, impacting their success rate. Other challenges include the simultaneous optimization of other drug-like properties alongside humanness and the humanization of fundamentally non-human modalities such as nanobodies. Significant efforts have been made to develop in silico methodologies able to address these issues, most recently incorporating machine learning techniques. Here, we outline these recent advancements in antibody and nanobody humanization, focusing on computational strategies that make use of the increasing volume of sequence and structural data available and the validation of these tools. We highlight that structural distinctions between antibodies and nanobodies make the application of antibody-focused in silico tools to nanobody humanization non-trivial. Furthermore, we discuss the effects of humanizing mutations on other essential drug-like properties such as binding affinity and developability, and methods that aim to tackle this multi-parameter optimization problem.

The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design

Synthetic antibodies (Abs) represent a category of artificial proteins capable of closely emulating the functions of natural Abs. Their in vitro production eliminates the need for an immunological response, streamlining the process of Ab discovery, engineering, and development. These artificially engineered Abs offer novel approaches to antigen recognition, paratope site manipulation, and biochemical/biophysical enhancements. As a result, synthetic Abs are fundamentally reshaping conventional methods of Ab production. This mirrors the revolution observed in molecular biology and genomics as a result of deep sequencing, which allows for the swift and cost-effective sequencing of DNA and RNA molecules at scale. Within this framework, deep sequencing has enabled the exploration of whole genomes and transcriptomes, including particular gene segments of interest. Notably, the fusion of synthetic Ab discovery with advanced deep sequencing technologies is redefining the current approaches to Ab design and development. Such combination offers opportunity to exhaustively explore Ab repertoires, fast-tracking the Ab discovery process, and enhancing synthetic Ab engineering. Moreover, advanced computational algorithms have the capacity to effectively mine big data, helping to identify Ab sequence patterns/features hidden within deep sequencing Ab datasets. In this context, these methods can be utilized to predict novel sequence features thereby enabling the successful generation of de novo Ab molecules. Hence, the merging of synthetic Ab design, deep sequencing technologies, and advanced computational models heralds a new chapter in Ab discovery, broadening our comprehension of immunology and streamlining the advancement of biological therapeutics.

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

Computational optimization of antibody humanness and stability by systematic energy-based ranking

Conventional methods for humanizing animal-derived antibodies involve grafting their complementarity-determining regions onto homologous human framework regions. However, this process can substantially lower antibody stability and antigen-binding affinity, and requires iterative mutational fine-tuning to recover the original antibody properties. Here we report a computational method for the systematic grafting of animal complementarity-determining regions onto thousands of human frameworks. The method, which we named CUMAb (for computational human antibody design; available at http://CUMAb.weizmann.ac.il ), starts from an experimental or model antibody structure and uses Rosetta atomistic simulations to select designs by energy and structural integrity. CUMAb-designed humanized versions of five antibodies exhibited similar affinities to those of the parental animal antibodies, with some designs showing marked improvement in stability. We also show that (1) non-homologous frameworks are often preferred to highest-homology frameworks, and (2) several CUMAb designs that differ by dozens of mutations and that use different human frameworks are functionally equivalent.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

Differences between Omicron SARS-CoV-2 RBD and other variants in their ability to interact with cell receptors and monoclonal antibodies

SARS-CoV-2 remains a health threat with the continuous emergence of new variants. This work aims to expand the knowledge about the SARS-CoV-2 receptor-binding domain (RBD) interactions with cell receptors and monoclonal antibodies (mAbs). By using constant-pH Monte Carlo simulations, the free energy of interactions between the RBD from different variants and several partners (Angiotensin-Converting Enzyme-2 (ACE2) polymorphisms and various mAbs) were predicted. Computed RBD-ACE2-binding affinities were higher for two ACE2 polymorphisms (rs142984500 and rs4646116) typically found in Europeans which indicates a genetic susceptibility. This is amplified for Omicron (BA.1) and its sublineages BA.2 and BA.3. The antibody landscape was computationally investigated with the largest set of mAbs so far in the literature. From the 32 studied binders, groups of mAbs were identified from weak to strong binding affinities (e.g. S2K146). These mAbs with strong binding capacity and especially their combination are amenable to experimentation and clinical trials because of their high predicted binding affinities and possible neutralization potential for current known virus mutations and a universal coronavirus.Communicated by Ramaswamy H. Sarma.

Deimmunization of flagellin adjuvant for clinical application

Flagellin is the cognate ligand for host pattern recognition receptors, toll-like receptor 5 (TLR5) in the cell surface, and NAIP5/NLRC4 inflammasome in the cytosol. TLR5-binding domain is located in D1 domain, where crucial amino acid sequences are conserved among diverse bacteria. The highly conserved C-terminal 35 amino acids of flagellin were proved to be responsible for the inflammasome activation by binding to NAIP5. D2/D3 domains, located in the central region and exposed to the outside surface of flagellar filament, are heterogeneous across bacterial species and highly immunogenic. Taking advantage of TLR5- and NLRC4-stimulating activities, flagellin has been actively developed as a vaccine adjuvant and immunotherapeutic. Because of its immunogenicity, there exist worries concerning diminished efficacy and possible reactogenicity after repeated administration. Deimmunization of flagellin derivatives while preserving the TLR5/NLRC4-mediated immunomodulatory activity should be the most reasonable option for clinical application. This review describes strategies and current achievements in flagellin deimmunization.

Application of germline antibody features to vaccine development, antibody discovery, antibody optimization and disease diagnosis

Although the efficacy and commercial success of vaccines and therapeutic antibodies have been tremendous, designing and discovering new drug candidates remains a labor-, time- and cost-intensive endeavor with high risks. The main challenges of vaccine development are inducing a strong immune response in broad populations and providing effective prevention against a group of highly variable pathogens. Meanwhile, antibody discovery faces several great obstacles, especially the blindness in antibody screening and the unpredictability of the developability and druggability of antibody drugs. These challenges are largely due to poorly understanding of germline antibodies and the antibody responses to pathogen invasions. Thanks to the recent developments in high-throughput sequencing and structural biology, we have gained insight into the germline immunoglobulin (Ig) genes and germline antibodies and then the germline antibody features associated with antigens and disease manifestation. In this review, we firstly outline the broad associations between germline antibodies and antigens. Moreover, we comprehensively review the recent applications of antigen-specific germline antibody features, physicochemical properties-associated germline antibody features, and disease manifestation-associated germline antibody features on vaccine development, antibody discovery, antibody optimization, and disease diagnosis. Lastly, we discuss the bottlenecks and perspectives of current and potential applications of germline antibody features in the biotechnology field.

Analytical Method for Experimental Validation of Computer-Designed Antibody

In the computational design of antibodies, the interaction analysis between target antigen and antibody is an essential process to obtain feedback for validation and optimization of the design. Kinetic and thermodynamic parameters as well as binding affinity (K_D) allow for a more detailed evaluation and understanding of the molecular recognition. In this chapter, we summarize the conventional experimental methods which can calculate K_D value (ELISA, FP), analyze a binding activity to actual cells (FCM), and evaluate the kinetic and thermodynamic parameters (ITC, SPR, BLI), including high-throughput analysis and a recently developed experimental technique.

Changes in antibody binding and functionality after humanizing a murine scFv anti-IFN-α2: From in silico studies to experimental analysis

The structural and dynamic changes introduced during antibody humanization continue to be a topic open to new contributions. For this reason, the study of structural and functional changes of a murine scFv (mu.scFv) anti-rhIFN-α2b after humanization was carried out. As it was shown by long molecular dynamics simulations and circular dichroism analysis, changes in primary sequence affected the tertiary structure of the humanized scFv (hz.scFv): the position of the variable domain of light chain (VL) respective to the variable domain of heavy chain (VH) in each scFv molecule was different. This change mainly impacted on conformation and dynamics of the complementarity-determining region 3 of VH (CDR-H3) which led to changes in the specificity and affinity of humanized scFv (hz.scFv). These observations agree with experimental results that showed a decrease in the antigen-binding strength of hz.scFv, and different capacities of these molecules to neutralize the in vitro rhIFN-α2b biological activity. Besides, experimental studies to characterize antigen-antibody binding showed that mu.scFv and hz.scFv bind to the same antigen area and recognize a conformational epitope, which is evidence of docking results. Finally, the differences between these molecules to neutralize the in vitro rhIFN-α2b biological activity were described as a consequence of the blockade of certain functionally relevant amino acids of the cytokine, after scFv binding. All these observations confirmed that humanization affected the affinity and specificity of hz.scFv and pointed out that two specific changes in the frameworks would be responsible.

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.

BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning

Despite recent advances in transgenic animal models and display technologies, humanization of mouse sequences remains one of the main routes for therapeutic antibody development. Traditionally, humanization is manual, laborious, and requires expert knowledge. Although automation efforts are advancing, existing methods are either demonstrated on a small scale or are entirely proprietary. To predict the immunogenicity risk, the human-likeness of sequences can be evaluated using existing humanness scores, but these lack diversity, granularity or interpretability. Meanwhile, immune repertoire sequencing has generated rich antibody libraries such as the Observed Antibody Space (OAS) that offer augmented diversity not yet exploited for antibody engineering. Here we present BioPhi, an open-source platform featuring novel methods for humanization (Sapiens) and humanness evaluation (OASis). Sapiens is a deep learning humanization method trained on the OAS using language modeling. Based on an in silico humanization benchmark of 177 antibodies, Sapiens produced sequences at scale while achieving results comparable to that of human experts. OASis is a granular, interpretable and diverse humanness score based on 9-mer peptide search in the OAS. OASis separated human and non-human sequences with high accuracy, and correlated with clinical immunogenicity. BioPhi thus offers an antibody design interface with automated methods that capture the richness of natural antibody repertoires to produce therapeutics with desired properties and accelerate antibody discovery campaigns. The BioPhi platform is accessible at https://biophi.dichlab.org and https://github.com/Merck/BioPhi.

A Novel Humanized Anti-Abrin A Chain Antibody Inhibits Abrin Toxicity In Vitro and In Vivo

Abrin, a type-II ribosome inactivating protein from the seed of Abrus precatorius, is classified as a Category B bioterrorism warfare agent. Due to its high toxicity, ingestion by animals or humans will lead to death from multiple organ failure. Currently, no effective agents have been reported to treat abrin poisoning. In this study, a novel anti-abrin neutralizing antibody (S008) was humanized using computer-aided design, which possessed lower immunogenicity. Similar to the parent antibody, a mouse anti-abrin monoclonal antibody, S008 possessed high affinity and showed a protective effect against abrin both in vitro and in vivo, and protected mice that S008 was administered 6 hours after abrin. S008 was found that it did not inhibit entry of abrin into cells, suggesting an intracellular blockade capacity against the toxin. In conclusion, this work demonstrates that S008 is a high affinity anti-abrin antibody with both a neutralizing and protective effect and may be an excellent candidate for clinical treatment of abrin poisoning.

Engineering a natural ligand-based CAR: directed evolution of the stress-receptor NKp30

B7H6, a stress-induced ligand which binds to the NK cell receptor NKp30, has recently emerged as a promising candidate for immunotherapy due to its tumor-specific expression on a broad array of human tumors. NKp30 can function as a chimeric antigen receptor (CAR) extracellular domain but exhibits weak binding with a fast on and off rate to B7H6 compared to the TZ47 anti-B7H6 single-chain variable fragment (scFv). Here, directed evolution using yeast display was employed to isolate novel NKp30 variants that bind to B7H6 with higher affinity compared to the native receptor but retain its fast association and dissociation profile. Two variants, CC3 and CC5, were selected for further characterization and were expressed as soluble Fc-fusion proteins and CARs containing CD28 and CD3ς intracellular domains. We observed that Fc-fusion protein forms of NKp30 and its variants were better able to bind tumor cells expressing low levels of B7H6 than TZ47, and that the novel variants generally exhibited improved in vitro tumor cell killing relative to NKp30. Interestingly, CAR T cells expressing the engineered variants produced unique cytokine signatures in response to multiple tumor types expressing B7H6 compared to both NKp30 and TZ47. These findings suggest that natural CAR receptors can be fine-tuned to produce more desirable signaling outputs while maintaining evolutionary advantages in ligand recognition relative to scFvs.

Redesigning an antibody H3 loop by virtual screening of a small library of human germline-derived sequences

The design of superior biologic therapeutics, including antibodies and engineered proteins, involves optimizing their specific ability to bind to disease-related molecular targets. Previously, we developed and applied the Assisted Design of Antibody and Protein Therapeutics (ADAPT) platform for virtual affinity maturation of antibodies (Vivcharuk et al. in PLoS One 12(7):e0181490, 10.1371/journal.pone.0181490, 2017). However, ADAPT is limited to point mutations of hot-spot residues in existing CDR loops. In this study, we explore the possibility of wholesale replacement of the entire H3 loop with no restriction to maintain the parental loop length. This complements other currently published studies that sample replacements for the CDR loops L1, L2, L3, H1 and H2. Given the immense sequence space theoretically available to H3, we focused on the virtual grafting of over 5000 human germline-derived H3 sequences from the IGMT/LIGM database increasing the diversity of the sequence space when compared to using crystalized H3 loop sequences. H3 loop conformations are generated and scored to identify optimized H3 sequences. Experimental testing of high-ranking H3 sequences grafted into the framework of the bH1 antibody against human VEGF-A led to the discovery of multiple hits, some of which had similar or better affinities relative to the parental antibody. In over 75% of the tested designs, the re-designed H3 loop contributed favorably to overall binding affinity. The hits also demonstrated good developability attributes such as high thermal stability and no aggregation. Crystal structures of select re-designed H3 variants were solved and indicated that although some deviations from predicted structures were seen in the more solvent accessible regions of the H3 loop, they did not significantly affect predicted affinity scores.

Humanization of antibodies using a machine learning approach on large-scale repertoire data

MOTIVATION

Monoclonal antibody therapeutics are often produced from non-human sources (typically murine), and can therefore generate immunogenic responses in humans. Humanization procedures aim to produce antibody therapeutics that do not elicit an immune response and are safe for human use, without impacting efficacy. Humanization is normally carried out in a largely trial-and-error experimental process. We have built machine learning classifiers that can discriminate between human and non-human antibody variable domain sequences using the large amount of repertoire data now available.

RESULTS

Our classifiers consistently outperform the current best-in-class model for distinguishing human from murine sequences, and our output scores exhibit a negative relationship with the experimental immunogenicity of existing antibody therapeutics. We used our classifiers to develop a novel, computational humanization tool, Hu-mAb, that suggests mutations to an input sequence to reduce its immunogenicity. For a set of therapeutic antibodies with known precursor sequences, the mutations suggested by Hu-mAb show significant overlap with those deduced experimentally. Hu-mAb is therefore an effective replacement for trial-and-error humanization experiments, producing similar results in a fraction of the time.

AVAILABILITY

Hu-mAb (humanness scoring and humanization) is freely available to use at opig.stats.ox.ac.uk/webapps/humab.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Human-likeness of antibody biologics determined by back-translation and comparison with large antibody variable gene repertoires

The antibody (Ab) germline gene rearrangement of variable (V), diversity (D), and joining (J) gene segments, as well as somatic hypermutation, give rise to the human Ab variable gene sequence repertoire. It is common to characterize single nucleotide frequencies of the variable region by alignment to species-specific wildtype germline genes. The increasing application of next-generation sequencing to immune repertoire studies has led to the compilation of increasing large adaptive immunome receptor repertoire datasets. We have developed a method that maps the sequence of a target Ab onto an immunome dataset of 326 million human Ab sequences. For this purpose, we created a position- and gene-specific scoring matrix (PGSSM) and its corresponding antibody similarity score. We characterized our PGSSM score and found that it strongly correlated with the phylogenetic distance of 181,355 Ab sequences from GenBank across 20 species. The most likely human nucleotide back-translation was obtained given only PGSSMs and the amino acid sequence of an Ab achieving a nucleotide sequence recovery of 95.9% and 97.2% for human heavy and light chains, respectively. In conclusion, the scoring of our back-translation is a valuable estimate for the similarity of an Ab sequence to the natural human repertoire. As expected, Ab therapeutic molecules developed from a human source showed a higher similarity to the repertoire than engineered Abs. Thus, the PGSSM metric introduced here can be used to engineer human-like Ab therapeutics.

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.

Engineering therapeutic antibodies for patient safety: tackling the immunogenicity problem

Established monoclonal antibodies (mAbs) allow treatment of cancers, autoimmune diseases and other severe illnesses. Side effects either arise due to interaction with the target protein and its biology or result from of the patient's immune system reacting to the foreign protein. This immunogenic reaction against therapeutic antibodies is dependent on various factors. The presence of non-human sequences can trigger immune responses as well as chemical and post-translational modifications of the antibody. However, even fully human antibodies can induce immune response through T cell epitopes or aggregates. In this review, we briefly describe, how therapeutic antibodies can interact with the patient's immune system and summarize recent advancements in protein engineering and in silico methods to reduce immunogenicity of therapeutic monoclonal antibodies.

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.

Deimmunization of protein therapeutics - Recent advances in experimental and computational epitope prediction and deletion

Biotherapeutics, and antimicrobial proteins in particular, are of increasing interest for human medicine. An important challenge in the development of such therapeutics is their potential immunogenicity, which can induce production of anti-drug-antibodies, resulting in altered pharmacokinetics, reduced efficacy, and potentially severe anaphylactic or hypersensitivity reactions. For this reason, the development and application of effective deimmunization methods for protein drugs is of utmost importance. Deimmunization may be achieved by unspecific shielding approaches, which include PEGylation, fusion to polypeptides (e.g., XTEN or PAS), reductive methylation, glycosylation, and polysialylation. Alternatively, the identification of epitopes for T cells or B cells and their subsequent deletion through site-directed mutagenesis represent promising deimmunization strategies and can be accomplished through either experimental or computational approaches. This review highlights the most recent advances and current challenges in the deimmunization of protein therapeutics, with a special focus on computational epitope prediction and deletion tools.

Computational approaches to therapeutic antibody design: established methods and emerging trends

Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.

Exploration and Modulation of Antibody Fragment Biophysical Properties by Replacing the Framework Region Sequences

In order to increase the successful development of recombinant antibodies and fragments, it seems fundamental to enhance their expression and/or biophysical properties, such as the thermal, chemical, and pH stabilities. In this study, we employed a method bases on replacing the antibody framework region sequences, in order to promote more particularly single-chain Fragment variable (scFv) product quality. We provide evidence that mutations of the VH- C-C′ loop might significantly improve the prokaryote production of well-folded and functional fragments with a production yield multiplied by 27 times. Additional mutations are accountable for an increase in the thermal (+19.6 °C) and chemical (+1.9 M) stabilities have also been identified. Furthermore, the hereby-produced fragments have shown to remain stable at a pH of 2.0, which avoids molecule functional and structural impairments during the purification process. Lastly, this study provides relevant information to the understanding of the relationship between the antibodies amino acid sequences and their respective biophysical properties.

Structure guided maturation of a novel humanized anti-HBV antibody and its preclinical development

We have reported that E6F6, a mouse monoclonal antibody, is a promising treatment option for patients with chronic hepatitis B (CHB). A humanized E6F6 antibody B11 with affinity loss was obtained by CDR-grafting approach. To address this issue, in silico affinity maturation through scanning mutagenesis using CHARMM force field methods was performed on an predicted immune complex model of the B11:HBsAg. We chose four variants with top increased interaction energy for further characterization. The antibody huE6F6-1 within two point mutations (Heavy Chain: Asp65Val; His66Leu) was identified to restore the parental antibody's high binding affinity, neutralization activity, and potent efficacy of viral suppression in vivo. Crystal structure (1.8 Å resolution) based molecular docking proved more stabilized and compact hydrogen bond interactions formed in huE6F6-1.The smaller and dispersed HBV immune complexes of huE6F6-1 by electron microscopy suggested it will have the same therapeutic efficacy as the parental E6F6 mAb. Preclinical study and pharmacokinetics of huE6F6-1 demonstrated that it is a stable and desirable lead candidate to improve the clinical management of CHB. Notably, our structure guided approach may facilitate the humanization and affinity maturation of other rodent antibody candidates during drug development.

Development of therapeutic antibodies for the treatment of diseases

It has been more than three decades since the first monoclonal antibody was approved by the United States Food and Drug Administration (US FDA) in 1986, and during this time, antibody engineering has dramatically evolved. Current antibody drugs have increasingly fewer adverse effects due to their high specificity. As a result, therapeutic antibodies have become the predominant class of new drugs developed in recent years. Over the past five years, antibodies have become the best-selling drugs in the pharmaceutical market, and in 2018, eight of the top ten bestselling drugs worldwide were biologics. The global therapeutic monoclonal antibody market was valued at approximately US$115.2 billion in 2018 and is expected to generate revenue of $150 billion by the end of 2019 and $300 billion by 2025. Thus, the market for therapeutic antibody drugs has experienced explosive growth as new drugs have been approved for treating various human diseases, including many cancers, autoimmune, metabolic and infectious diseases. As of December 2019, 79 therapeutic mAbs have been approved by the US FDA, but there is still significant growth potential. This review summarizes the latest market trends and outlines the preeminent antibody engineering technologies used in the development of therapeutic antibody drugs, such as humanization of monoclonal antibodies, phage display, the human antibody mouse, single B cell antibody technology, and affinity maturation. Finally, future applications and perspectives are also discussed.

Antibody Structure and Function: The Basis for Engineering Therapeutics

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

Looking for therapeutic antibodies in next-generation sequencing repositories

Recently it has become possible to query the great diversity of natural antibody repertoires using next-generation sequencing (NGS). These methods are capable of producing millions of sequences in a single experiment. Here we compare clinical-stage therapeutic antibodies to the ~1b sequences from 60 independent sequencing studies in the Observed Antibody Space database, which includes antibody sequences from NGS analysis of immunoglobulin gene repertoires. Of 242 post-Phase 1 antibodies, we found 16 with sequence identity matches of 95% or better for both heavy and light chains. There are also 54 perfect matches to therapeutic CDR-H3 regions in the NGS outputs, suggesting a nontrivial amount of convergence between naturally observed sequences and those developed artificially. This has potential implications for both the legal protection of commercial antibodies and the discovery of antibody therapeutics.

DisruPPI: structure-based computational redesign algorithm for protein binding disruption

Motivation

Disruption of protein–protein interactions can mitigate antibody recognition of therapeutic proteins, yield monomeric forms of oligomeric proteins, and elucidate signaling mechanisms, among other applications. While designing affinity-enhancing mutations remains generally quite challenging, both statistically and physically based computational methods can precisely identify affinity-reducing mutations. In order to leverage this ability to design variants of a target protein with disrupted interactions, we developed the DisruPPI protein design method (DISRUpting Protein–Protein Interactions) to optimize combinations of mutations simultaneously for both disruption and stability, so that incorporated disruptive mutations do not inadvertently affect the target protein adversely.

Results

Two existing methods for predicting mutational effects on binding, FoldX and INT5, were demonstrated to be quite precise in selecting disruptive mutations from the SKEMPI and AB-Bind databases of experimentally determined changes in binding free energy. DisruPPI was implemented to use an INT5-based disruption score integrated with an AMBER-based stability assessment and was applied to disrupt protein interactions in a set of different targets representing diverse applications. In retrospective evaluation with three different case studies, comparison of DisruPPI-designed variants to published experimental data showed that DisruPPI was able to identify more diverse interaction-disrupting and stability-preserving variants more efficiently and effectively than previous approaches. In prospective application to an interaction between enhanced green fluorescent protein (EGFP) and a nanobody, DisruPPI was used to design five EGFP variants, all of which were shown to have significantly reduced nanobody binding while maintaining function and thermostability. This demonstrates that DisruPPI may be readily utilized for effective removal of known epitopes of therapeutically relevant proteins.

Availability and implementation

DisruPPI is implemented in the EpiSweep package, freely available under an academic use license.

Supplementary information

Supplementary data are available at Bioinformatics online.

High-Throughput Antibody Structure Modeling and Design Using ABodyBuilder

Antibodies are proteins of the adaptive immune system; they can be designed to bind almost any molecule, and are increasingly being used as biotherapeutics. Experimental antibody design is an expensive and time-consuming process, and computational antibody design methods can now be used to help develop new therapeutics and diagnostics. Within the design pipeline, accurate antibody structure modeling is essential, as it provides the basis for antibody-antigen docking, binding affinity prediction, and estimating thermal stability. Ideally, models should be rapidly generated, allowing the exploration of the breadth of antibody space. This allows methods to replicate the natural processes of antibody diversification (e.g., V(D)J recombination and somatic hypermutation), and cope with large volumes of data that are typical of next-generation sequencing datasets. Here we describe ABodyBuilder and PEARS, algorithms that build and mutate antibody model structures. These methods take ~30 s to generate a model antibody structure.

Systematic testing and specificity mapping of alloantigen-specific chimeric antigen receptors in regulatory T cells

Chimeric antigen receptor (CAR) technology can be used to engineer the antigen specificity of regulatory T cells (Tregs) and improve their potency as an adoptive cell therapy in multiple disease models. As synthetic receptors, CARs carry the risk of immunogenicity, particularly when derived from nonhuman antibodies. Using an HLA-A*02:01-specific CAR (A2-CAR) encoding a single-chain variable fragment (Fv) derived from a mouse antibody, we developed a panel of 20 humanized A2-CARs (hA2-CARs). Systematic testing demonstrated variations in expression, and ability to bind HLA-A*02:01 and stimulate human Treg suppression in vitro. In addition, we developed a new method to comprehensively map the alloantigen specificity of CARs, revealing that humanization reduced HLA-A cross-reactivity. In vivo bioluminescence imaging showed rapid trafficking and persistence of hA2-CAR Tregs in A2-expressing allografts, with eventual migration to draining lymph nodes. Adoptive transfer of hA2-CAR Tregs suppressed HLA-A2+ cell-mediated xenogeneic graft-versus-host disease and diminished rejection of human HLA-A2+ skin allografts. These data provide a platform for systematic development and specificity testing of humanized alloantigen-specific CARs that can be used to engineer specificity and homing of therapeutic Tregs.

Structural delineation of potent transmission-blocking epitope I on malaria antigen Pfs48/45

Interventions that can block the transmission of malaria-causing Plasmodium falciparum (Pf) between the human host and Anopheles vector have the potential to reduce the incidence of malaria. Pfs48/45 is a gametocyte surface protein critical for parasite development and transmission, and its targeting by monoclonal antibody (mAb) 85RF45.1 leads to the potent reduction of parasite transmission. Here, we reveal how the Pfs48/45 6C domain adopts a (SAG1)-related-sequence (SRS) fold. We structurally delineate potent epitope I and show how mAb 85RF45.1 recognizes an electronegative surface with nanomolar affinity. Analysis of Pfs48/45 sequences reveals that polymorphisms are rare for residues involved at the binding interface. Humanization of rat-derived mAb 85RF45.1 conserved the mode of recognition and activity of the parental antibody, while also improving its thermostability. Our work has implications for the development of transmission-blocking interventions, both through improving vaccine designs and the testing of passive delivery of mAbs in humans.

Malaria protein Pfs48/45 is a promising transmission-blocking antigen targeted by antibodies. Here, the authors determine the structure of its transmission-blocking epitope I, and generate a humanized monoclonal antibody that binds Pfs48/45 with high affinity.

Humanization of Antibodies using a Statistical Inference Approach

Antibody humanization is a key step in the preclinical phase of the development of therapeutic antibodies, originally developed and tested in non-human models (most typically, in mouse). The standard technique of Complementarity-Determining Regions (CDR) grafting into human Framework Regions of germline sequences has some important drawbacks, in that the resulting sequences often need further back-mutations to ensure functionality and/or stability. Here we propose a new method to characterize the statistical distribution of the sequences of the variable regions of human antibodies, that takes into account phenotypical correlations between pairs of residues, both within and between chains. We define a "humanness score" of a sequence, comparing its performance in distinguishing human from murine sequences, with that of some alternative scores in the literature. We also compare the score with the experimental immunogenicity of clinically used antibodies. Finally, we use the humanness score as an optimization function and perform a search in the sequence space, starting from different murine sequences and keeping the CDR regions unchanged. Our results show that our humanness score outperforms other methods in sequence classification, and the optimization protocol is able to generate humanized sequences that are recognized as human by standard homology modelling tools.

Early identification of unusually clustered mutations and root causes in therapeutic antibody development

This study reports findings of an unusual cluster of mutations spanning 22 bp (base pairs) in a monoclonal antibody expression vector. It was identified by two orthogonal methods: mass spectrometry on expressed protein and next-generation sequencing (NGS) on the plasmid DNA. While the initial NGS analysis confirmed the designed sequence modification, intact mass analysis detected an additional mass of the antibody molecule expressed in CHO cells. The extra mass was eventually found to be associated with unmatched nucleotides in a distal region by checking full-length sequence alignment plots. Interestingly, the complementary sequence of the mutated sequence was a reverse sequence of the original sequence and flanked by two 10-bp reverse-complementary sequences, leading to an undesirable DNA recombination. The finding highlights the necessity of rigorous examination of expression vector design and early monitoring of molecule integrity at both DNA and protein levels to prevent clones from having sequence variants during cell line development.

Antibody side chain conformations are position-dependent

Side chain prediction is an integral component of computational antibody design and structure prediction. Current antibody modelling tools use backbone-dependent rotamer libraries with conformations taken from general proteins. Here we present our antibody-specific rotamer library, where rotamers are binned according to their immunogenetics (IMGT) position, rather than their local backbone geometry. We find that for some amino acid types at certain positions, only a restricted number of side chain conformations are ever observed. Using this information, we are able to reduce the breadth of the rotamer sampling space. Based on our rotamer library, we built a side chain predictor, position-dependent antibody rotamer swapper (PEARS). On a blind test set of 95 antibody model structures, PEARS had the highest average χ₁ and χ1+2 accuracy (78.7% and 64.8%) compared to three leading backbone-dependent side chain predictors. Our use of IMGT position, rather than backbone ϕ/ψ, meant that PEARS was more robust to errors in the backbone of the model structure. PEARS also achieved the lowest number of side chain-side chain clashes. PEARS is freely available as a web application at http://opig.stats.ox.ac.uk/webapps/pears.

Antibody Design and Humanization via In Silico Modeling

Antibody humanization process converts any nonhuman antibody sequence into humanized antibodies. This can be achieved using different methods of antibody design and engineering. This chapter will primarily focus on antibody design using a homology model followed by framework shuffling of murine to human germline sequence for humanization. Historically, mouse antibodies have been humanized using sequence-based approaches, in which all the murine frameworks are replaced with most homologous human germline sequence or related scaffold. Most often this humanized antibody design, when tested, has a significantly reduced binding or no binding to the cognate antigen. This is due to noncompatibility of mouse CDRs being supported by non-native human framework scaffold. This mismatch between mouse, human structural fold, and elimination of key conformational residues often leads to antibody humanization failures. Recently, there has been advent of homology modelor structure-guided antibody humanization. Instead of humanization based on linear sequence, this approach takes into account the tertiary structure and fold of the mouse antibody. A mouse homology model of the fragment variable is created, and based on sequence alignment with human germline, residues that are different in mouse are replaced with humanized sequence in the model. Energy minimization is applied to this humanized model that also delineates residues which might have steric clashes due to change in the overall tertiary conformation of the humanized antibody. Therefore, a homology model-guided with rational mutations, and reintroduction of key conformational residues from mouse antibody not only eliminates steric clashes but might also restore function in relation to binding affinity to its antigen.

How B-Cell Receptor Repertoire Sequencing Can Be Enriched with Structural Antibody Data

Next-generation sequencing of immunoglobulin gene repertoires (Ig-seq) allows the investigation of large-scale antibody dynamics at a sequence level. However, structural information, a crucial descriptor of antibody binding capability, is not collected in Ig-seq protocols. Developing systematic relationships between the antibody sequence information gathered from Ig-seq and low-throughput techniques such as X-ray crystallography could radically improve our understanding of antibodies. The mapping of Ig-seq datasets to known antibody structures can indicate structurally, and perhaps functionally, uncharted areas. Furthermore, contrasting naïve and antigenically challenged datasets using structural antibody descriptors should provide insights into antibody maturation. As the number of antibody structures steadily increases and more and more Ig-seq datasets become available, the opportunities that arise from combining the two types of information increase as well. Here, we review how these data types enrich one another and show potential for advancing our knowledge of the immune system and improving antibody engineering.

Computationally-driven identification of antibody epitopes

Understanding where antibodies recognize antigens can help define mechanisms of action and provide insights into progression of immune responses. We investigate the extent to which information about binding specificity implicitly encoded in amino acid sequence can be leveraged to identify antibody epitopes. In computationally-driven epitope localization, possible antibody–antigen binding modes are modeled, and targeted panels of antigen variants are designed to experimentally test these hypotheses. Prospective application of this approach to two antibodies enabled epitope localization using five or fewer variants per antibody, or alternatively, a six-variant panel for both simultaneously. Retrospective analysis of a variety of antibodies and antigens demonstrated an almost 90% success rate with an average of three antigen variants, further supporting the observation that the combination of computational modeling and protein design can reveal key determinants of antibody–antigen binding and enable efficient studies of collections of antibodies identified from polyclonal samples or engineered libraries.

Development of unique cytotoxic chimeric antigen receptors based on human scFv targeting B7H6

As a stress-inducible natural killer (NK) cell ligand, B7H6 plays a role in innate tumor immunosurveillance and is a fairly tumor selective marker expressed on a variety of solid and hematologic cancer cells. Here, we describe the isolation and characterization of a new family of single chain fragment variable (scFv) molecules targeting the human B7H6 ligand. Through directed evolution of a yeast surface displayed non-immune human-derived scFv library, eight candidates comprising a single family of clones differing by up to four amino acid mutations and exhibiting nM avidities for soluble B7H6-Ig were isolated. A representative clone re-formatted as an scFv-CH1-Fc molecule demonstrated specific binding to both B7H6-Ig and native membrane-bound B7H6 on tumor cell lines with a binding avidity comparable to the previously characterized B7H6-targeting antibody, TZ47. Furthermore, these clones recognized an epitope distinct from that of TZ47 and the natural NK cell ligand NKp30, and demonstrated specific activity against B7H6-expressing tumor cells when expressed as a chimeric antigen receptor (CAR) in T cells.

Brief introduction of current technologies in isolation of broadly neutralizing HIV-1 antibodies

HIV/AIDS has become a worldwide pandemic. Before an effective HIV-1 vaccine eliciting broadly neutralizing monoclonal antibodies (bnmAbs) is fully developed, passive immunization for prevention and treatment of HIV-1 infection may alleviate the burden caused by the pandemic. Among HIV-1 infected individuals, about 20% of them generated cross-reactive neutralizing antibodies two to four years after infection, the details of which could provide knowledge for effective vaccine design. Recent progress in techniques for isolation of human broadly neutralizing antibodies has facilitated the study of passive immunization. The isolation and characterization of large panels of potent human broadly neutralizing antibodies has revealed new insights into the principles of antibody-mediated neutralization of HIV. In this paper, we review the current effective techniques in broadly neutralizing antibody isolation.

Searching for the Pareto frontier in multi-objective protein design

The goal of protein engineering and design is to identify sequences that adopt three-dimensional structures of desired function. Often, this is treated as a single-objective optimization problem, identifying the sequence-structure solution with the lowest computed free energy of folding. However, many design problems are multi-state, multi-specificity, or otherwise require concurrent optimization of multiple objectives. There may be tradeoffs among objectives, where improving one feature requires compromising another. The challenge lies in determining solutions that are part of the Pareto optimal set-designs where no further improvement can be achieved in any of the objectives without degrading one of the others. Pareto optimality problems are found in all areas of study, from economics to engineering to biology, and computational methods have been developed specifically to identify the Pareto frontier. We review progress in multi-objective protein design, the development of Pareto optimization methods, and present a specific case study using multi-objective optimization methods to model the tradeoff between three parameters, stability, specificity, and complexity, of a set of interacting synthetic collagen peptides.

Identification of Strategic Residues at the Interface of Antigen-Antibody Interactions by In Silico Mutagenesis

Structural information pertaining to antigen-antibody interactions is fundamental in immunology, and benefits structure-based vaccine design. Modeling of antigen-antibody immune complexes from co-crystal structures or molecular docking simulations provides an extensive profile of the epitope at the interface; however, the key amino acids involved in the interaction must be further clarified, often through the use of experimental mutagenesis and subsequent binding assays. Here, we describe an in silico mutagenesis method to identify key sites at antigen-antibody interfaces, using significant increase in pH-dependency energy among saturated point mutations. Through a comprehensive analysis of the crystal structures of three antigen-antibody immune complexes, we show that a cutoff value of 1 kcal/mol of increased interaction energy provides good congruency with the experimental non-binding mutations conducted in vitro. This in silico mutagenesis strategy, in association with energy calculations, may provide an efficient tool for antibody-antigen interface analyses, epitope optimization, and/or conformation prediction in structure-based vaccine design.

VH-VL orientation prediction for antibody humanization candidate selection: A case study

Antibody humanization describes the procedure of grafting a non-human antibody's complementarity-determining regions, i.e., the variable loop regions that mediate specific interactions with the antigen, onto a β-sheet framework that is representative of the human variable region germline repertoire, thus reducing the number of potentially antigenic epitopes that might trigger an anti-antibody response. The selection criterion for the so-called acceptor frameworks (one for the heavy and one for the light chain variable region) is traditionally based on sequence similarity. Here, we propose a novel approach that selects acceptor frameworks such that the relative orientation of the 2 variable domains in 3D space, and thereby the geometry of the antigen-binding site, is conserved throughout the process of humanization. The methodology relies on a machine learning-based predictor of antibody variable domain orientation that has recently been shown to improve the quality of antibody homology models. Using data from 3 humanization campaigns, we demonstrate that preselecting humanization variants based on the predicted difference in variable domain orientation with regard to the original antibody leads to subsets of variants with a significant improvement in binding affinity.

Andre M, Svenson K, Morris C, Tchistiakova L. Beyond CDR-grafting: Structure-guided humanization of framework and CDR regions of an anti-myostatin antibody

Antibodies are an important class of biotherapeutics that offer specificity to their antigen, long half-life, effector function interaction and good manufacturability. The immunogenicity of non-human-derived antibodies, which can be a major limitation to development, has been partially overcome by humanization through complementarity-determining region (CDR) grafting onto human acceptor frameworks. The retention of foreign content in the CDR regions, however, is still a potential immunogenic liability. Here, we describe the humanization of an anti-myostatin antibody utilizing a 2-step process of traditional CDR-grafting onto a human acceptor framework, followed by a structure-guided approach to further reduce the murine content of CDR-grafted antibodies. To accomplish this, we solved the co-crystal structures of myostatin with the chimeric (Protein Databank (PDB) id 5F3B) and CDR-grafted anti-myostatin antibody (PDB id 5F3H), allowing us to computationally predict the structurally important CDR residues as well as those making significant contacts with the antigen. Structure-based rational design enabled further germlining of the CDR-grafted antibody, reducing the murine content of the antibody without affecting antigen binding. The overall "humanness" was increased for both the light and heavy chain variable regions.

Algorithms for protein design

Computational structure-based protein design programs are becoming an increasingly important tool in molecular biology. These programs compute protein sequences that are predicted to fold to a target structure and perform a desired function. The success of a program's predictions largely relies on two components: first, the input biophysical model, and second, the algorithm that computes the best sequence(s) and structure(s) according to the biophysical model. Improving both the model and the algorithm in tandem is essential to improving the success rate of current programs, and here we review recent developments in algorithms for protein design, emphasizing how novel algorithms enable the use of more accurate biophysical models. We conclude with a list of algorithmic challenges in computational protein design that we believe will be especially important for the design of therapeutic proteins and protein assemblies.

Computationally driven antibody engineering enables simultaneous humanization and thermostabilization

Humanization reduces the immunogenicity risk of therapeutic antibodies of non-human origin. Thermostabilization can be critical for clinical development and application of therapeutic antibodies. Here, we show that the computational antibody redesign method Computationally Driven Antibody Humanization (CoDAH) enables these two goals to be accomplished simultaneously and seamlessly. A panel of CoDAH designs for the murine parent of cetuximab, a chimeric anti-EGFR antibody, exhibited both substantially improved thermostabilities and substantially higher levels of humanness, while retaining binding activity near the parental level. The consistently high quality of the turnkey CoDAH designs, over a whole panel of variants, suggests that the computationally directed approach encapsulates key determinants of antibody structure and function.

Design and engineering of deimmunized biotherapeutics

Therapeutic proteins are powerful next-generation drugs able to effectively treat diverse and devastating diseases, but the development and use of biotherapeutics entails unique challenges and risks. In particular, protein drugs are subject to immune surveillance in the human body, and ensuing antidrug immune responses can cause a wide range of problems including altered pharmacokinetics, loss of efficacy, and even life-threating complications. Here we review recent progress in technologies for engineering deimmunized biotherapeutics, placing particular emphasis on deletion of immunogenic antibody and T cell epitopes via experimentally or computationally guided mutagenesis.