Structure-guided deimmunization of therapeutic proteins

Functional Deimmunization of Botulinum Neurotoxin Protease Domain via Computationally Driven Library Design and Ultrahigh-Throughput Screening

Botulinum neurotoxin serotype A (BoNT/A) is a widely used cosmetic agent that also has diverse therapeutic applications; however, adverse antidrug immune responses and associated loss of efficacy have been reported in clinical uses. Here, we describe computational design and ultrahigh-throughput screening of a massive BoNT/A light-chain (BoNT/A-LC) library optimized for reduced T cell epitope content and thereby dampened immunogenicity. We developed a functional assay based on bacterial co-expression of BoNT/A-LC library members with a Förster resonance energy transfer (FRET) sensor for BoNT/A-LC enzymatic activity, and we employed high-speed fluorescence-activated cell sorting (FACS) to identify numerous computationally designed variants having wild-type-like enzyme kinetics. Many of these variants exhibited decreased immunogenicity in humanized HLA transgenic mice and manifested in vivo paralytic activity when incorporated into full-length toxin. One variant achieved near-wild-type paralytic potency and a 300% reduction in antidrug antibody response in vivo. Thus, we have achieved a striking level of BoNT/A-LC functional deimmunization by combining computational library design and ultrahigh-throughput screening. This strategy holds promise for deimmunizing other biologics with complex superstructures and mechanisms of action.

Optimizing Antibody-Antigen Binding Affinities with the ADAPT Platform

The ADAPT (Assisted Design of Antibody and Protein Therapeutics) platform guides the selection of mutants that improve/modulate the affinity of antibodies and other biologics. Predicted affinities are based on a consensus z-score from three scoring functions. Computational predictions are interleaved with experimental validation, significantly enhancing the robustness of the design and selection of mutants. A key step is an initial exhaustive virtual single-mutant scan that identifies hot spots and the mutations predicted to improve affinity. A small number of proposed single mutants are then produced and assayed. Only the validated single mutants (i.e., having improved affinity) are used to design double and higher-order mutants in subsequent rounds of design, avoiding the combinatorial explosion that arises from random mutagenesis. Typically, with a total of about 30-50 designed single, double, and triple mutants, affinity improvements of 10- to 100-fold are obtained.

Resistor: An algorithm for predicting resistance mutations via Pareto optimization over multistate protein design and mutational signatures

Resistance to pharmacological treatments is a major public health challenge. Here, we introduce Resistor-a structure- and sequence-based algorithm that prospectively predicts resistance mutations for drug design. Resistor computes the Pareto frontier of four resistance-causing criteria: the change in binding affinity (ΔKa) of the (1) drug and (2) endogenous ligand upon a protein's mutation; (3) the probability a mutation will occur based on empirically derived mutational signatures; and (4) the cardinality of mutations comprising a hotspot. For validation, we applied Resistor to EGFR and BRAF kinase inhibitors treating lung adenocarcinoma and melanoma. Resistor correctly identified eight clinically significant EGFR resistance mutations, including the erlotinib and gefitinib "gatekeeper" T790M mutation and five known osimertinib resistance mutations. Furthermore, Resistor predictions are consistent with BRAF inhibitor sensitivity data from both retrospective and prospective experiments using KinCon biosensors. Resistor is available in the open-source protein design software OSPREY.

Algorithms for protein design

Computational Protein Design has the potential to contribute to major advances in enzyme technology, vaccine design, receptor-ligand engineering, biomaterials, nanosensors, and synthetic biology. Although Protein Design is a challenging problem, proteins can be designed by experts in Protein Design, as well as by non-experts whose primary interests are in the applications of Protein Design. The increased accessibility of Protein Design technology is attributable to the accumulated knowledge and experience with Protein Design as well as to the availability of software and online resources. The objective of this review is to serve as a guide to the relevant literature with a focus on the novel methods and algorithms that have been developed or applied for Protein Design, and to assist in the selection of algorithms for Protein Design. Novel algorithms and models that have been introduced to utilize the enormous amount of experimental data and novel computational hardware have the potential for producing substantial increases in the accuracy, reliability and range of applications of designed proteins.

MHCEpitopeEnergy, a Flexible Rosetta-Based Biotherapeutic Deimmunization Platform

As non-"self" macromolecules, biotherapeutics can trigger an immune response that can reduce drug efficacy, require patients to be taken off therapy, or even cause life-threatening reactions. To enable the flexible and facile design of protein biotherapeutics while reducing the prevalence of T-cell epitopes that drive immune recognition, we have integrated into the Rosetta protein design suite a new scoring term that allows design protocols to account for predicted or experimentally identified epitopes in the optimized objective function. This flexible scoring term can be used in any Rosetta design trajectory, can be targeted to specific regions of a protein, and can be readily extended to work with a variety of epitope predictors. By performing extensive design runs with varied design parameter choices for three case study proteins as well as a larger diverse benchmark, we show that the incorporation of this scoring term enables the effective exploration of an alternative, deimmunized sequence space to discover diverse proteins that are potentially highly deimmunized while retaining physical and chemical qualities similar to those yielded by equivalent nondeimmunizing sequence design protocols.

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.

Advanced engineering of third-generation lysins and formulation strategies for clinical applications

One of the possible solutions for the current antibiotic resistance crisis may be found in (often bacteriophage-derived) peptidoglycan hydrolases. The first clinical trials of these natural enzymes, coined here as first-generation lysins, are currently ongoing. Moving beyond natural endolysins with protein engineering established the second generation of lysins. In second-generation lysins, the focus lies on improving antibacterial and biochemical properties such as antimicrobial activity and stability, as well as expanding their activities towards Gram-negative pathogens. However, solutions to particular key challenges regarding clinical applications are only beginning to emerge in the third generation of lysins, in which protein and biochemical engineering efforts focus on improving properties relevant under clinical conditions. In addition, increasingly advanced formulation strategies are developed to increase the bioavailability, antibacterial activity, and half-life, and to reduce pro-inflammatory responses. This review focuses on third-generation and advanced formulation strategies that are developed to treat infections, ranging from topical to systemic applications. Together, these efforts may fully unlock the potential of lysin therapy and will propel it as a true antibiotic alternative or supplement.

Mitigation of T-cell dependent immunogenicity by reengineering factor VIIa analogue

Vatreptacog alfa (VA), a recombinant activated human factor VII (rFVIIa) variant with 3 amino acid substitutions, was developed to provide increased procoagulant activity in hemophilia patients with inhibitors to factor VIII or factor IX. In phase 3 clinical trials, changes introduced during the bioengineering of VA resulted in the development of undesired anti-drug antibodies in some patients, leading to the termination of a potentially promising therapeutic protein product. Here, we use preclinical biomarkers associated with clinical immunogenicity to validate our deimmunization strategy applied to this bioengineered rFVIIa analog. The reengineered rFVIIa analog variants retained increased intrinsic thrombin generation activity but did not elicit T-cell responses in peripheral blood mononuclear cells isolated from 50 HLA typed subjects representing the human population. Our algorithm, rational immunogenicity determination, offers a broadly applicable deimmunizing strategy for bioengineered proteins.

Construction of a novel Staphylokinase (SAK) mutant with low immunogenicity and its evaluation in rhesus monkey

The heterologous nature of SAK, a thrombolytic drug, elicits high titers of neutralizing antibodies, which limits its clinical use. Here, we aim to establish a SAK mutant with equivalent activity to the wild type but reduced antigenicity, which may allow for multiple injections. Biosun software was used to predict SAK antigenic epitopes, and several main epitopes were modified by gene deletion and mutation. Ten SAK mutants were constructed, and their thrombolytic activity and immunogenicity were analyzed in vitro. SAK6, with a high expression level (45%), similar thrombolytic activity, and lower antibody reaction, was chosen for in vivo analysis in rhesus monkey. In the nearly 8-month experimental period, the antibody level of the SAK6 group was significantly lower than that of the SAK group. Moreover, only 5% of SAK activity was retained, whereas 75.6% of SAK6 activity was retained after incubating with respective antiserum. Overall, these results demonstrated that SAK6, established through comprehensive site-directed mutagenesis program, had identical thrombolytic activity to SAK, low immunogenicity, and less side effects, demonstrating its efficient clinical potential for thrombus disease.

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.

Protein Design by Provable Algorithms

Protein design algorithms can leverage provable guarantees of accuracy to provide new insights and unique optimized molecules.

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.

Pareto Optimization of Combinatorial Mutagenesis Libraries

In order to increase the hit rate of discovering diverse, beneficial protein variants via high-throughput screening, we have developed a computational method to optimize combinatorial mutagenesis libraries for overall enrichment in two distinct properties of interest. Given scoring functions for evaluating individual variants, POCoM (Pareto Optimal Combinatorial Mutagenesis) scores entire libraries in terms of averages over their constituent members, and designs optimal libraries as sets of mutations whose combinations make the best trade-offs between average scores. This represents the first general-purpose method to directly design combinatorial libraries for multiple objectives characterizing their constituent members. Despite being rigorous in mapping out the Pareto frontier, it is also very fast even for very large libraries (e.g., designing 30 mutation, billion-member libraries in only hours). We here instantiate POCoM with scores based on a target's protein structure and its homologs' sequences, enabling the design of libraries containing variants balancing these two important yet quite different types of information. We demonstrate POCoM's generality and power in case study applications to green fluorescent protein, cytochrome P450, and β-lactamase. Analysis of the POCoM library designs provides insights into the trade-offs between structure- and sequence-based scores, as well as the impacts of experimental constraints on library designs. POCoM libraries incorporate mutations that have previously been found favorable experimentally, while diversifying the contexts in which these mutations are situated and maintaining overall variant quality.

OSPREY 3.0: Open-source protein redesign for you, with powerful new features

We present osprey 3.0, a new and greatly improved release of the osprey protein design software. Osprey 3.0 features a convenient new Python interface, which greatly improves its ease of use. It is over two orders of magnitude faster than previous versions of osprey when running the same algorithms on the same hardware. Moreover, osprey 3.0 includes several new algorithms, which introduce substantial speedups as well as improved biophysical modeling. It also includes GPU support, which provides an additional speedup of over an order of magnitude. Like previous versions of osprey, osprey 3.0 offers a unique package of advantages over other design software, including provable design algorithms that account for continuous flexibility during design and model conformational entropy. Finally, we show here empirically that osprey 3.0 accurately predicts the effect of mutations on protein-protein binding. Osprey 3.0 is available at http://www.cs.duke.edu/donaldlab/osprey.php as free and open-source software. © 2018 Wiley Periodicals, Inc.

Recent advances in automated protein design and its future challenges

INTRODUCTION

Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques. Areas covered: The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion: Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.

Population-specific design of de-immunized protein biotherapeutics

Immunogenicity is a major problem during the development of biotherapeutics since it can lead to rapid clearance of the drug and adverse reactions. The challenge for biotherapeutic design is therefore to identify mutants of the protein sequence that minimize immunogenicity in a target population whilst retaining pharmaceutical activity and protein function. Current approaches are moderately successful in designing sequences with reduced immunogenicity, but do not account for the varying frequencies of different human leucocyte antigen alleles in a specific population and in addition, since many designs are non-functional, require costly experimental post-screening. Here, we report a new method for de-immunization design using multi-objective combinatorial optimization. The method simultaneously optimizes the likelihood of a functional protein sequence at the same time as minimizing its immunogenicity tailored to a target population. We bypass the need for three-dimensional protein structure or molecular simulations to identify functional designs by automatically generating sequences using probabilistic models that have been used previously for mutation effect prediction and structure prediction. As proof-of-principle we designed sequences of the C2 domain of Factor VIII and tested them experimentally, resulting in a good correlation with the predicted immunogenicity of our model.

Application of Assisted Design of Antibody and Protein Therapeutics (ADAPT) improves efficacy of a Clostridium difficile toxin A single-domain antibody

Assisted Design of Antibody and Protein Therapeutics (ADAPT) is an affinity maturation platform interleaving predictions and testing that was previously validated on monoclonal antibodies (mAbs). This study expands the applicability of ADAPT to single-domain antibodies (sdAbs), a promising class of recombinant antibody-based biologics. As a test case, we used the camelid sdAb A26.8, a V_HH that binds Clostridium difficile toxin A (TcdA) relatively weakly but displays a reasonable level of TcdA neutralization. ADAPT-guided A26.8 affinity maturation resulted in an improvement of one order of magnitude by point mutations only, reaching an equilibrium dissociation constant (K_D) of 2 nM, with the best binding mutants having similar or improved stabilities relative to the parent sdAb. This affinity improvement generated a 6-fold enhancement of efficacy at the cellular level; the A26.8 double-mutant T56R,T103R neutralizes TcdA cytotoxicity with an IC₅₀ of 12 nM. The designed mutants with increased affinities are predicted to establish novel electrostatic interactions with the antigen. Almost full additivity of mutation effects is observed, except for positively charged residues introduced at adjacent positions. Furthermore, analysis of false-positive predictions points to general directions for improving the ADAPT platform. ADAPT guided the efficacy enhancement of an anti-toxin sdAb, an alternative therapeutic modality for C. difficile.

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.

Scratching the Surface: Resurfacing Proteins to Endow New Properties and Function

Protein engineering is an emerging discipline that dovetails modern molecular biology techniques with high-throughput screening, laboratory evolution technologies, and computational approaches to modify sequence, structure, and, in some cases, function and properties of proteins. The ultimate goal is to develop new proteins with improved or designer functions for use in biotechnology, medicine, and basic research. One way to engineer proteins is to change their solvent-exposed regions through focused or random "protein resurfacing." In this review we explain what protein resurfacing is, and discuss recent examples of how this strategy is used to generate proteins with altered or broadened recognition profiles, improved stability, solubility, and expression, cell-penetrating ability, and reduced immunogenicity. Additionally we comment on how these properties can be further improved using chemical resurfacing approaches. Protein resurfacing will likely play an increasingly important role as more biologics enter clinical use, and we present some arguments to support this view.

Computationally optimized deimmunization libraries yield highly mutated enzymes with low immunogenicity and enhanced activity

Therapeutic proteins of wide-ranging function hold great promise for treating disease, but immune surveillance of these macromolecules can drive an antidrug immune response that compromises efficacy and even undermines safety. To eliminate widespread T-cell epitopes in any biotherapeutic and thereby mitigate this key source of detrimental immune recognition, we developed a Pareto optimal deimmunization library design algorithm that optimizes protein libraries to account for the simultaneous effects of combinations of mutations on both molecular function and epitope content. Active variants identified by high-throughput screening are thus inherently likely to be deimmunized. Functional screening of an optimized 10-site library (1,536 variants) of P99 β-lactamase (P99βL), a component of ADEPT cancer therapies, revealed that the population possessed high overall fitness, and comprehensive analysis of peptide-MHC II immunoreactivity showed the population possessed lower average immunogenic potential than the wild-type enzyme. Although similar functional screening of an optimized 30-site library (2.15 × 10⁹ variants) revealed reduced population-wide fitness, numerous individual variants were found to have activity and stability better than the wild type despite bearing 13 or more deimmunizing mutations per enzyme. The immunogenic potential of one highly active and stable 14-mutation variant was assessed further using ex vivo cellular immunoassays, and the variant was found to silence T-cell activation in seven of the eight blood donors who responded strongly to wild-type P99βL. In summary, our multiobjective library-design process readily identified large and mutually compatible sets of epitope-deleting mutations and produced highly active but aggressively deimmunized constructs in only one round of library screening.

Insights into Hunter syndrome from the structure of iduronate-2-sulfatase

Hunter syndrome is a rare but devastating childhood disease caused by mutations in the IDS gene encoding iduronate-2-sulfatase, a crucial enzyme in the lysosomal degradation pathway of dermatan sulfate and heparan sulfate. These complex glycosaminoglycans have important roles in cell adhesion, growth, proliferation and repair, and their degradation and recycling in the lysosome is essential for cellular maintenance. A variety of disease-causing mutations have been identified throughout the IDS gene. However, understanding the molecular basis of the disease has been impaired by the lack of structural data. Here, we present the crystal structure of human IDS with a covalently bound sulfate ion in the active site. This structure provides essential insight into multiple mechanisms by which pathogenic mutations interfere with enzyme function, and a compelling explanation for severe Hunter syndrome phenotypes. Understanding the structural consequences of disease-associated mutations will facilitate the identification of patients that may benefit from specific tailored therapies.

Hunter syndrome is a lysosomal storage disease caused by mutations in the enzyme iduronate-2-sulfatase (IDS). Here, the authors present the IDS crystal structure and give mechanistic insights into mutations that cause Hunter syndrome.

OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design

Drug resistance in protein targets is an increasingly common phenomenon that reduces the efficacy of both existing and new antibiotics. However, knowledge of future resistance mutations during pre-clinical phases of drug development would enable the design of novel antibiotics that are robust against not only known resistant mutants, but also against those that have not yet been clinically observed. Computational structure-based protein design (CSPD) is a transformative field that enables the prediction of protein sequences with desired biochemical properties such as binding affinity and specificity to a target. The use of CSPD to predict previously unseen resistance mutations represents one of the frontiers of computational protein design. In a recent study (Reeve et al. Proc Natl Acad Sci U S A 112(3):749-754, 2015), we used our OSPREY (Open Source Protein REdesign for You) suite of CSPD algorithms to prospectively predict resistance mutations that arise in the active site of the dihydrofolate reductase enzyme from methicillin-resistant Staphylococcus aureus (SaDHFR) in response to selective pressure from an experimental competitive inhibitor. We demonstrated that our top predicted candidates are indeed viable resistant mutants. Since that study, we have significantly enhanced the capabilities of OSPREY with not only improved modeling of backbone flexibility, but also efficient multi-state design, fast sparse approximations, partitioned continuous rotamers for more accurate energy bounds, and a computationally efficient representation of molecular-mechanics and quantum-mechanical energy functions. Here, using SaDHFR as an example, we present a protocol for resistance prediction using the latest version of OSPREY. Specifically, we show how to use a combination of positive and negative design to predict active site escape mutations that maintain the enzyme's catalytic function but selectively ablate binding of an inhibitor.

EpiSweep: Computationally Driven Reengineering of Therapeutic Proteins to Reduce Immunogenicity While Maintaining Function

Therapeutic proteins are yielding ever more advanced and efficacious new drugs, but the biological origins of these highly effective therapeutics render them subject to immune surveillance within the patient's body. When recognized by the immune system as a foreign agent, protein drugs elicit a coordinated response that can manifest a range of clinical complications including rapid drug clearance, loss of functionality and efficacy, delayed infusion-like allergic reactions, more serious anaphylactic shock, and even induced auto-immunity. It is thus often necessary to deimmunize an exogenous protein in order to enable its clinical application; critically, the deimmunization process must also maintain the desired therapeutic activity.To meet the growing need for effective, efficient, and broadly applicable protein deimmunization technologies, we have developed the EpiSweep suite of protein design algorithms. EpiSweep seamlessly integrates computational prediction of immunogenic T cell epitopes with sequence- or structure-based assessment of the impacts of mutations on protein stability and function, in order to select combinations of mutations that make Pareto optimal trade-offs between the competing goals of low immunogenicity and high-level function. The methods are applicable both to the design of individual functionally deimmunized variants as well as the design of combinatorial libraries enriched in functionally deimmunized variants. After validating EpiSweep in a series of retrospective case studies providing comparisons to conventional approaches to T cell epitope deletion, we have experimentally demonstrated it to be highly effective in prospective application to deimmunization of a number of different therapeutic candidates. We conclude that our broadly applicable computational protein design algorithms guide the engineer towards the most promising deimmunized therapeutic candidates, and thereby have the potential to accelerate development of new protein drugs by shortening time frames and improving hit rates.

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.

Depletion of T cell epitopes in lysostaphin mitigates anti-drug antibody response and enhances antibacterial efficacy in vivo

The enzyme lysostaphin possesses potent anti-staphylococcal activity and represents a promising antibacterial drug candidate; however, its immunogenicity poses a barrier to clinical translation. Here, structure-based biomolecular design enabled widespread depletion of lysostaphin DRB1(∗)0401 restricted T cell epitopes, and resulting deimmunized variants exhibited striking reductions in anti-drug antibody responses upon administration to humanized HLA-transgenic mice. This reduced immunogenicity translated into improved efficacy in the form of protection against repeated challenges with methicillin-resistant Staphylococcus aureus (MRSA). In contrast, while wild-type lysostaphin was efficacious against the initial MRSA infection, it failed to clear subsequent bacterial challenges that were coincident with escalating anti-drug antibody titers. These results extend the existing deimmunization literature, in which reduced immunogenicity and retained efficacy are assessed independently of each other. By correlating in vivo efficacy with longitudinal measures of anti-drug antibody development, we provide the first direct evidence that T cell epitope depletion manifests enhanced biotherapeutic efficacy.

Antibody humanization by structure-based computational protein design

Antibodies derived from non-human sources must be modified for therapeutic use so as to mitigate undesirable immune responses. While complementarity-determining region (CDR) grafting-based humanization techniques have been successfully applied in many cases, it remains challenging to maintain the desired stability and antigen binding affinity upon grafting. We developed an alternative humanization approach called CoDAH ("Computationally-Driven Antibody Humanization") in which computational protein design methods directly select sets of amino acids to incorporate from human germline sequences to increase humanness while maintaining structural stability. Retrospective studies show that CoDAH is able to identify variants deemed beneficial according to both humanness and structural stability criteria, even for targets lacking crystal structures. Prospective application to TZ47, a murine anti-human B7H6 antibody, demonstrates the approach. Four diverse humanized variants were designed, and all possible unique VH/VL combinations were produced as full-length IgG1 antibodies. Soluble and cell surface expressed antigen binding assays showed that 75% (6 of 8) of the computationally designed VH/VL variants were successfully expressed and competed with the murine TZ47 for binding to B7H6 antigen. Furthermore, 4 of the 6 bound with an estimated KD within an order of magnitude of the original TZ47 antibody. In contrast, a traditional CDR-grafted variant could not be expressed. These results suggest that the computational protein design approach described here can be used to efficiently generate functional humanized antibodies and provide humanized templates for further affinity maturation.

Protein deimmunization via structure-based design enables efficient epitope deletion at high mutational loads

Anti-drug immune responses are a unique risk factor for biotherapeutics, and undesired immunogenicity can alter pharmacokinetics, compromise drug efficacy, and in some cases even threaten patient safety. To fully capitalize on the promise of biotherapeutics, more efficient and generally applicable protein deimmunization tools are needed. Mutagenic deletion of a protein's T cell epitopes is one powerful strategy to engineer immunotolerance, but deimmunizing mutations must maintain protein structure and function. Here, EpiSweep, a structure-based protein design and deimmunization algorithm, has been used to produce a panel of seven beta-lactamase drug candidates having 27-47% reductions in predicted epitope content. Despite bearing eight mutations each, all seven engineered enzymes maintained good stability and activity. At the same time, the variants exhibited dramatically reduced interaction with human class II major histocompatibility complex proteins, key regulators of anti-drug immune responses. When compared to 8-mutation designs generated with a sequence-based deimmunization algorithm, the structure-based designs retained greater thermostability and possessed fewer high affinity epitopes, the dominant drivers of anti-biotherapeutic immune responses. These experimental results validate the first structure-based deimmunization algorithm capable of mapping optimal biotherapeutic design space. By designing optimal mutations that reduce immunogenic potential while imparting favorable intramolecular interactions, broadly distributed epitopes may be simultaneously targeted using high mutational loads.

Engineered binding to erythrocytes induces immunological tolerance to E. coli asparaginase

Antigen-specific immune responses to protein drugs can hinder efficacy and compromise safety because of drug neutralization and secondary clinical complications. We report a tolerance induction strategy to prevent antigen-specific humoral immune responses to therapeutic proteins. Our modular, biomolecular approach involves engineering tolerizing variants of proteins such that they bind erythrocytes in vivo upon injection, on the basis of the premise that aged erythrocytes and the payloads they carry are cleared tolerogenically, driving the deletion of antigen-specific T cells. We demonstrate that binding the clinical therapeutic enzyme Escherichia coli l-asparaginase to erythrocytes in situ antigen-specifically abrogates development of antibody titers by >1000-fold and extends the pharmacodynamic effect of the drug 10-fold in mice. Additionally, a single pretreatment dose of erythrocyte-binding asparaginase tolerized mice to multiple subsequent doses of the wild-type enzyme. This strategy for reducing antigen-specific humoral responses may enable more effective and safer treatment with therapeutic proteins and drug candidates that are hampered by immunogenicity.

Structure-based redesign of lysostaphin yields potent antistaphylococcal enzymes that evade immune cell surveillance

Staphylococcus aureus infections exert a tremendous burden on the health-care system, and the threat of drug-resistant strains continues to grow. The bacteriolytic enzyme lysostaphin is a potent antistaphylococcal agent with proven efficacy against both drug-sensitive and drug-resistant strains; however, the enzyme's own bacterial origins cause undesirable immunogenicity and pose a barrier to clinical translation. Here, we deimmunized lysostaphin using a computationally guided process that optimizes sets of mutations to delete immunogenic T cell epitopes without disrupting protein function. In vitro analyses showed the methods to be both efficient and effective, producing seven different deimmunized designs exhibiting high function and reduced immunogenic potential. Two deimmunized candidates elicited greatly suppressed proliferative responses in splenocytes from humanized mice, while at the same time the variants maintained wild-type efficacy in a staphylococcal pneumonia model. Overall, the deimmunized enzymes represent promising leads in the battle against S. aureus.

NICTABA and UDA, two GlcNAc-binding lectins with unique antiviral activity profiles

OBJECTIVES

This study aimed to assess the antiviral properties of a unique lectin (NICTABA) produced by the tobacco plant, Nicotiana tabacum.

METHODS

Cellular assays were used to investigate the antiviral activity of NICTABA and Urtica dioica agglutinin (UDA). Surface plasmon resonance (SPR) studies were performed to study the sugar specificity and the interactions of both lectins with the envelope glycoproteins of HIV-1.

RESULTS

The N-acetyl-d-glucosamine (GlcNAc)-binding lectins exhibited broad-spectrum activity against several families of enveloped viruses including influenza A/B, Dengue virus type 2, herpes simplex virus types 1 and 2 and HIV-1/2. The IC50 of NICTABA for various HIV-1 strains, clinical isolates and HIV-2 assessed in PBMCs ranged from 5 to 30 nM. Furthermore, NICTABA inhibited syncytium formation between persistently HIV-1-infected T cells and uninfected CD4+ T lymphocytes and prevented DC-SIGN-mediated HIV-1 transmission to CD4+ target T lymphocytes. However, unlike many other antiviral carbohydrate-binding agents (CBAs) described so far, NICTABA did not block HIV-1 capture to DC-SIGN+ cells and it did not interfere with the binding of the human monoclonal antibody 2G12 to gp120. SPR studies with HIV-1 envelope glycoproteins showed that the affinity of NICTABA for gp120 and gp41 was in the low nanomolar range. The specific binding of NICTABA to gp120 could be prevented in the presence of a GlcNAc trimer, but not in the presence of mannose trimers. NICTABA displayed no antiviral activity against non-enveloped viruses.

CONCLUSIONS

Since CBAs possess a high genetic barrier for the development of viral resistance and NICTABA shows a broad antiviral activity profile, this CBA may qualify as a potential antiviral candidate with a pleiotropic mode of action aimed at targeting the entry of enveloped viruses.

Development of a high affinity, non-covalent biologic to add functionality to Fabs

Functionalization of monoclonal antibodies (mAbs) requires chemical derivatization and/or genetic manipulation. Inherent in these methods are challenges with protein heterogeneity, stability and solubility. Such perturbations could potentially be avoided by using a high affinity, non-covalent intermediate to bridge the desired functionality to a stable mAb. Recently, we engineered a binding site for a peptide named “meditope” within the Fab of trastuzumab. Proximity of the meditope site to that of protein L suggested an opportunity to enhance the meditope's moderate affinity. Joined by a peptide linker, the meditope-protein L construct has a K_D ~ 180 pM - a 7000-fold increase in affinity. The construct is highly specific to the engineered trastuzumab, as demonstrated by flow cytometry. Moreover, the fusion of a bulky GFP to this construct did not affect the association with cell surface antigens. Collectively, these data indicate this specific, high affinity construct can be developed to rapidly add new functionality to mAbs.

Mapping the Pareto optimal design space for a functionally deimmunized biotherapeutic candidate

The immunogenicity of biotherapeutics can bottleneck development pipelines and poses a barrier to widespread clinical application. As a result, there is a growing need for improved deimmunization technologies. We have recently described algorithms that simultaneously optimize proteins for both reduced T cell epitope content and high-level function. In silico analysis of this dual objective design space reveals that there is no single global optimum with respect to protein deimmunization. Instead, mutagenic epitope deletion yields a spectrum of designs that exhibit tradeoffs between immunogenic potential and molecular function. The leading edge of this design space is the Pareto frontier, i.e. the undominated variants for which no other single design exhibits better performance in both criteria. Here, the Pareto frontier of a therapeutic enzyme has been designed, constructed, and evaluated experimentally. Various measures of protein performance were found to map a functional sequence space that correlated well with computational predictions. These results represent the first systematic and rigorous assessment of the functional penalty that must be paid for pursuing progressively more deimmunized biotherapeutic candidates. Given this capacity to rapidly assess and design for tradeoffs between protein immunogenicity and functionality, these algorithms may prove useful in augmenting, accelerating, and de-risking experimental deimmunization efforts.

Protein therapeutics have created a revolution in disease therapy, providing improved outcomes for prevalent illnesses and conditions while at the same time yielding treatments for diseases that were previously intractable. However, this powerful class of drugs is subject to their own unique challenges and risk factors. In particular, the biological origins of therapeutic proteins predispose them towards eliciting a detrimental immune response from the patient's own body. Therefore, fully capitalizing on the medicinal reservoir of natural and engineered proteins will require efficient, effective, and broadly applicable deimmunization technologies. We have developed deimmunization algorithms that simultaneously optimize therapeutic candidates for both low immunogenicity and high-level activity and stability. Here, we combine computational modeling and experimental analysis to show that the process of protein deimmunization manifests inherent tradeoffs between immunogenic potential and biomolecular function. Our experimental results demonstrate that dual objective optimization allows us to assess and design for these tradeoffs, thereby enabling facile construction of deimmunized variants that span a broad range of immunogenicity and functionality performance parameters. Thus, we can rapidly map the design space for deimmunized drug candidates, and we can use this information to guide selection of engineered proteins that are most likely to meet performance benchmarks for a given clinical application.

HIV-1 and its resistance to peptidic carbohydrate-binding agents (CBAs): an overview

The glycoproteins on the surfaces of enveloped viruses, such as HIV, can be considered as a unique target for antiviral therapy. Different carbohydrate-binding agents (CBAs) target specific glycans present on viral glycoproteins of enveloped viruses. It has been shown that long-term CBA pressure in vitro can result in mutant HIV-1 isolates with several N-linked glycan deletions on gp120. These studies demonstrated that mainly high-mannose type glycans are deleted. However, interestingly, N241, N262 and N356 on gp120 have never been found to be affected after prolonged CBA exposure. Here, we review the mutation and (cross)-resistance profiles of eleven specific generated CBA-resistant HIV-1 strains. We observed that the broad-neutralizing anti-carbohydrate binding mAb 2G12 became completely inactive against all the generated CBA-resistant HIV-1 clade B isolates. In addition, all of the CBAs discussed in this review, with the exception of NICTABA, interfered with the binding of 2G12 mAb to gp120 expressed on HIV-1-infected T cells. The cross-resistance profiles of mutant HIV-1 strains are varying from increased susceptibility to very high resistance levels, even among different classes of CBAs with dissimilar sugar specificities or binding moieties [e.g., α(1,3), α(1,2), α(1,6)]. Recent studies demonstrated promising results in non-topical formulations (e.g., intranasally or subcutaneously), highlighting their potential for prevention (microbicides) and antiviral therapy.

OptMAVEn--a new framework for the de novo design of antibody variable region models targeting specific antigen epitopes

Antibody-based therapeutics provides novel and efficacious treatments for a number of diseases. Traditional experimental approaches for designing therapeutic antibodies rely on raising antibodies against a target antigen in an immunized animal or directed evolution of antibodies with low affinity for the desired antigen. However, these methods remain time consuming, cannot target a specific epitope and do not lead to broad design principles informing other studies. Computational design methods can overcome some of these limitations by using biophysics models to rationally select antibody parts that maximize affinity for a target antigen epitope. This has been addressed to some extend by OptCDR for the design of complementary determining regions. Here, we extend this earlier contribution by addressing the de novo design of a model of the entire antibody variable region against a given antigen epitope while safeguarding for immunogenicity (Optimal Method for Antibody Variable region Engineering, OptMAVEn). OptMAVEn simulates in silico the in vivo steps of antibody generation and evolution, and is capable of capturing the critical structural features responsible for affinity maturation of antibodies. In addition, a humanization procedure was developed and incorporated into OptMAVEn to minimize the potential immunogenicity of the designed antibody models. As case studies, OptMAVEn was applied to design models of neutralizing antibodies targeting influenza hemagglutinin and HIV gp120. For both HA and gp120, novel computational antibody models with numerous interactions with their target epitopes were generated. The observed rates of mutations and types of amino acid changes during in silico affinity maturation are consistent with what has been observed during in vivo affinity maturation. The results demonstrate that OptMAVEn can efficiently generate diverse computational antibody models with both optimized binding affinity to antigens and reduced immunogenicity.

Computationally driven deletion of broadly distributed T cell epitopes in a biotherapeutic candidate

Biotherapeutics are subject to immune surveillance within the body, and anti-biotherapeutic immune responses can compromise drug efficacy and patient safety. Initial development of targeted antidrug immune memory is coordinated by T cell recognition of immunogenic subsequences, termed "T cell epitopes." Biotherapeutics may therefore be deimmunized by mutating key residues within cognate epitopes, but there exist complex trade-offs between immunogenicity, mutational load, and protein structure-function. Here, a protein deimmunization algorithm has been applied to P99 beta-lactamase, a component of antibody-directed enzyme prodrug therapies. The algorithm, integer programming for immunogenic proteins, seamlessly integrates computational prediction of T cell epitopes with both 1- and 2-body sequence potentials that assess protein tolerance to epitope-deleting mutations. Compared to previously deimmunized P99 variants, which bore only one or two mutations, the enzymes designed here contain 4-5 widely distributed substitutions. As a result, they exhibit broad reductions in major histocompatibility complex recognition. Despite their high mutational loads and markedly reduced immunoreactivity, all eight engineered variants possessed wild-type or better catalytic activity. Thus, the protein design algorithm is able to disrupt broadly distributed epitopes while maintaining protein function. As a result, this computational tool may prove useful in expanding the repertoire of next-generation biotherapeutics.

A high throughput MHC II binding assay for quantitative analysis of peptide epitopes

Biochemical assays with recombinant human MHC II molecules can provide rapid, quantitative insights into immunogenic epitope identification, deletion, or design(1,2). Here, a peptide-MHC II binding assay is scaled to 384-well format. The scaled down protocol reduces reagent costs by 75% and is higher throughput than previously described 96-well protocols(1,3-5). Specifically, the experimental design permits robust and reproducible analysis of up to 15 peptides against one MHC II allele per 384-well ELISA plate. Using a single liquid handling robot, this method allows one researcher to analyze approximately ninety test peptides in triplicate over a range of eight concentrations and four MHC II allele types in less than 48 hr. Others working in the fields of protein deimmunization or vaccine design and development may find the protocol to be useful in facilitating their own work. In particular, the step-by-step instructions and the visual format of JoVE should allow other users to quickly and easily establish this methodology in their own labs.

Beyond humanization and de-immunization: tolerization as a method for reducing the immunogenicity of biologics

Immune responses to some monoclonal antibodies (mAbs) and biologic proteins interfere with their efficacy due to the development of anti-drug antibodies (ADA). In the case of mAbs, most ADA target 'foreign' sequences present in the complementarity determining regions (CDRs). Humanization of the mAb sequence is one approach that has been used to render biologics less foreign to the human immune system. However, fully human mAbs can also drive immunogenicity. De-immunization (removing epitopes) has been used to reduce biologic protein immunogenicity. Here, we discuss a third approach to reducing the immunogenicity of biologics: introduction of Treg epitopes that stimulate Treg function and induce tolerance to the biologic protein. Supplementing humanization (replacing xeno-sequences with human) and de-immunization (reducing T effector epitopes) with tolerization (introducing Treg epitopes) where feasible, as a means of improving biologics 'quality by design', may lead to the development of ever more clinically effective, but less immunogenic, biologics.

Activity of and effect of subcutaneous treatment with the broad-spectrum antiviral lectin griffithsin in two laboratory rodent models

Griffithsin (GRFT) is a red-alga-derived lectin that binds the terminal mannose residues of N-linked glycans found on the surface of human immunodeficiency virus type 1 (HIV-1), HIV-2, and other enveloped viruses, including hepatitis C virus (HCV), severe acute respiratory syndrome coronavirus (SARS-CoV), and Ebola virus. GRFT displays no human T-cell mitogenic activity and does not induce production of proinflammatory cytokines in treated human cell lines. However, despite the growing evidence showing the broad-spectrum nanomolar or better antiviral activity of GRFT, no study has reported a comprehensive assessment of GRFT safety as a potential systemic antiviral treatment. The results presented in this work show that minimal toxicity was induced by a range of single and repeated daily subcutaneous doses of GRFT in two rodent species, although we noted treatment-associated increases in spleen and liver mass suggestive of an antidrug immune response. The drug is systemically distributed, accumulating to high levels in the serum and plasma after subcutaneous delivery. Further, we showed that serum from GRFT-treated animals retained antiviral activity against HIV-1-enveloped pseudoviruses in a cell-based neutralization assay. Overall, our data presented here show that GRFT accumulates to relevant therapeutic concentrations which are tolerated with minimal toxicity. These studies support further development of GRFT as a systemic antiviral therapeutic agent against enveloped viruses, although deimmunizing the molecule may be necessary if it is to be used in long-term treatment of chronic viral infections.