Removing T-cell epitopes with computational protein design

GradDock: rapid simulation and tailored ranking functions for peptide-MHC Class I docking

Motivation

The identification of T-cell epitopes has many profound translational applications in the areas of transplantation, disease diagnosis, vaccine/therapeutic protein development and personalized immunotherapy. While data-driven methods have been widely used for the prediction of peptide binders with notable successes, the structural modeling of peptide binding to MHC molecules is crucial for understanding the underlying molecular mechanism of the immunological processes.

Results

We developed GradDock, a structure-based method for the rapid and accurate modeling of peptide binding to MHC Class I (pMHC-I). GradDock explicitly models diverse unbound peptides in vacuo and inserts them into the MHC-I groove through a steered gradient descent with a topological correction process. The simulation process yields diverse structural conformations including native-like peptides. We completely revised the Rosetta score terms and developed a new ranking function specifically for pMHC-I. Using the diverse peptides, a linear programming approach is applied to find the optimal weights for the individual Rosetta score terms. Our examination revealed that a refinement of the dihedral angles and a modification of the repulsion can dramatically improve the modeling quality. GradDock is five-times faster than a Rosetta-based docking approach for pMHC-I. We also demonstrate that the predictive capability of GradDock with the re-weighted Rosetta ranking function is consistently more accurate than the Rosetta-based method with the standard Rosetta score (approximately three-times better for a cross-docking set).

Availability and implementation

GradDock is freely available for academic purposes. The program and the ranking score weights for Rosetta are available at http://bel.kaist.ac.kr/research/GradDock.

Contact

hskim76@kaist.ac.kr.

Supplementary information

Supplementary data are available at Bioinformatics online.

Strategies to Reduce the Immunogenicity of Recombinant Immunotoxins

Recombinant immunotoxins (RITs) are genetically engineered proteins being developed to treat cancer. They are composed of an Fv that targets a cancer antigen and a fragment of a bacterial toxin that kills tumor cells. Because the toxin is a foreign protein, it is immunogenic. The clinical success of RITs in patients with a normal immune system is limited by their immunogenicity. In this review, we discuss our progress in therapeutic protein deimmunization and the balancing act between immunogenicity and therapeutic potency. One approach is to prevent the activation of B cells by mapping and elimination of B-cell epitopes. A second approach is to prevent helper T-cell activation by interfering with major histocompatibility complex II presentation or T-cell recognition. Immunizing mice with RITs that were deimmunized by elimination of the murine B- or T-cell epitopes showed that both approaches are effective. Another approach to control immunogenicity is to modify the host immune system. Nanoparticles containing synthetic vaccine particles encapsulating rapamycin can induce immune tolerance and prevent anti-drug antibody formation. This treatment restores RIT anti-tumor activity that is otherwise neutralized because of immunogenicity.

Anti-Drug Antibodies: Emerging Approaches to Predict, Reduce or Reverse Biotherapeutic Immunogenicity

The development of anti-drug antibodies (ADAs) following administration of biotherapeutics to patients is a vexing problem that is attracting increasing attention from pharmaceutical and biotechnology companies. This serious clinical problem is also spawning creative research into novel approaches to predict, avoid, and in some cases even reverse such deleterious immune responses. CD4⁺ T cells are essential players in the development of most ADAs, while memory B-cell and long-lived plasma cells amplify and maintain these responses. This review summarizes methods to predict and experimentally identify T-cell and B-cell epitopes in therapeutic proteins, with a particular focus on blood coagulation factor VIII (FVIII), whose immunogenicity is clinically significant and is the subject of intensive current research. Methods to phenotype ADA responses in humans are described, including T-cell stimulation assays, and both established and novel approaches to determine the titers, epitopes and isotypes of the ADAs themselves. Although rational protein engineering can reduce the immunogenicity of many biotherapeutics, complementary, novel approaches to induce specific tolerance, especially during initial exposures, are expected to play significant roles in future efforts to reduce or reverse these unwanted immune responses.

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.

Immunity to CRISPR Cas9 and Cas12a therapeutics

Genome-editing therapeutics are poised to treat human diseases. As we enter clinical trials with the most promising CRISPR-Cas9 and CRISPR-Cas12a (Cpf1) modalities, the risks associated with administering these foreign biomolecules into human patients become increasingly salient. Preclinical discovery with CRISPR-Cas9 and CRISPR-Cas12a systems and foundational gene therapy studies indicate that the host immune system can mount undesired responses against the administered proteins and nucleic acids, the gene-edited cells, and the host itself. These host defenses include inflammation via activation of innate immunity, antibody induction in humoral immunity, and cell death by T-cell-mediated cytotoxicity. If left unchecked, these immunological reactions can curtail therapeutic benefits and potentially lead to mortality. Ways to assay and reduce the immunogenicity of Cas9 and Cas12a proteins are therefore critical for ensuring patient safety and treatment efficacy, and for bringing us closer to realizing the vision of permanent genetic cures. WIREs Syst Biol Med 2018, 10:e1408. doi: 10.1002/wsbm.1408 This article is categorized under: Laboratory Methods and Technologies > Genetic/Genomic Methods Translational, Genomic, and Systems Medicine > Translational Medicine Translational, Genomic, and Systems Medicine > Therapeutic Methods.

Designing a mutant Candida uricase with improved polymerization state and enzymatic activity

As human uricase has been silenced during evolution, counterparts from other species become an alternative for the treatment of hyperuricemia. Candida uricase is a promising option among them, but its aggregation propensity remains a major obstacle to clinical use. In this study, we designed two mutations according to homology-modeled 3D structure of Candida uricase: Cys249Ser substitution and C-terminal Leu deletion. The wild-type uricase and three mutants containing either or both of the mutations were expressed in Escherichia coli BL21 and validated by mass spectrometry. Size-exclusion chromatography and electrophoresis analysis demonstrated that aggregation was induced by interchain disulfide bonds and could be significantly avoided by Cys249Ser substitution. In combination with Cys249Ser substitution, deletion of Leu increased the enzymatic activity by 8%. Taken together, mutant containing both mutations is chosen as our target protein which is comparatively more suitable for therapeutic use. In addition, homology-modeled 3D structure was proved to be an efficient approach for protein engineering.

Massively parallel de novo protein design for targeted therapeutics

De novo protein design holds promise for creating small stable proteins with shapes customized to bind therapeutic targets. We describe a massively parallel approach for designing, manufacturing and screening mini-protein binders, integrating large-scale computational design, oligonucleotide synthesis, yeast display screening and next-generation sequencing. We designed and tested 22,660 mini-proteins of 37-43 residues that target influenza haemagglutinin and botulinum neurotoxin B, along with 6,286 control sequences to probe contributions to folding and binding, and identified 2,618 high-affinity binders. Comparison of the binding and non-binding design sets, which are two orders of magnitude larger than any previously investigated, enabled the evaluation and improvement of the computational model. Biophysical characterization of a subset of the binder designs showed that they are extremely stable and, unlike antibodies, do not lose activity after exposure to high temperatures. The designs elicit little or no immune response and provide potent prophylactic and therapeutic protection against influenza, even after extensive repeated dosing.

Development of a strategy and computational application to select candidate protein analogues with reduced HLA binding and immunogenicity

Unwanted immune responses against protein therapeutics can reduce efficacy or lead to adverse reactions. T-cell responses are key in the development of such responses, and are directed against immunodominant regions within the protein sequence, often associated with binding to several allelic variants of HLA class II molecules (promiscuous binders). Herein, we report a novel computational strategy to predict 'de-immunized' peptides, based on previous studies of erythropoietin protein immunogenicity. This algorithm (or method) first predicts promiscuous binding regions within the target protein sequence and then identifies residue substitutions predicted to reduce HLA binding. Further, this method anticipates the effect of any given substitution on flanking peptides, thereby circumventing the creation of nascent HLA-binding regions. As a proof-of-principle, the algorithm was applied to Vatreptacog α, an engineered Factor VII molecule associated with unintended immunogenicity. The algorithm correctly predicted the two immunogenic peptides containing the engineered residues. As a further validation, we selected and evaluated the immunogenicity of seven substitutions predicted to simultaneously reduce HLA binding for both peptides, five control substitutions with no predicted reduction in HLA-binding capacity, and additional flanking region controls. In vitro immunogenicity was detected in 21·4% of the cultures of peptides predicted to have reduced HLA binding and 11·4% of the flanking regions, compared with 46% for the cultures of the peptides predicted to be immunogenic. This method has been implemented as an interactive application, freely available online at http://tools.iedb.org/deimmunization/.

In silico methods for design of biological therapeutics

It has been twenty years since the first rationally designed small molecule drug was introduced into the market. Since then, we have progressed from designing small molecules to designing biotherapeutics. This class of therapeutics includes designed proteins, peptides and nucleic acids that could more effectively combat drug resistance and even act in cases where the disease is caused because of a molecular deficiency. Computational methods are crucial in this design exercise and this review discusses the various elements of designing biotherapeutic proteins and peptides. Many of the techniques discussed here, such as the deterministic and stochastic design methods, are generally used in protein design. We have devoted special attention to the design of antibodies and vaccines. In addition to the methods for designing these molecules, we have included a comprehensive list of all biotherapeutics approved for clinical use. Also included is an overview of methods that predict the binding affinity, cell penetration ability, half-life, solubility, immunogenicity and toxicity of the designed therapeutics. Biotherapeutics are only going to grow in clinical importance and are set to herald a new generation of disease management and cure.

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.

A critical analysis of computational protein design with sparse residue interaction graphs

Protein design algorithms enumerate a combinatorial number of candidate structures to compute the Global Minimum Energy Conformation (GMEC). To efficiently find the GMEC, protein design algorithms must methodically reduce the conformational search space. By applying distance and energy cutoffs, the protein system to be designed can thus be represented using a sparse residue interaction graph, where the number of interacting residue pairs is less than all pairs of mutable residues, and the corresponding GMEC is called the sparse GMEC. However, ignoring some pairwise residue interactions can lead to a change in the energy, conformation, or sequence of the sparse GMEC vs. the original or the full GMEC. Despite the widespread use of sparse residue interaction graphs in protein design, the above mentioned effects of their use have not been previously analyzed. To analyze the costs and benefits of designing with sparse residue interaction graphs, we computed the GMECs for 136 different protein design problems both with and without distance and energy cutoffs, and compared their energies, conformations, and sequences. Our analysis shows that the differences between the GMECs depend critically on whether or not the design includes core, boundary, or surface residues. Moreover, neglecting long-range interactions can alter local interactions and introduce large sequence differences, both of which can result in significant structural and functional changes. Designs on proteins with experimentally measured thermostability show it is beneficial to compute both the full and the sparse GMEC accurately and efficiently. To this end, we show that a provable, ensemble-based algorithm can efficiently compute both GMECs by enumerating a small number of conformations, usually fewer than 1000. This provides a novel way to combine sparse residue interaction graphs with provable, ensemble-based algorithms to reap the benefits of sparse residue interaction graphs while avoiding their potential inaccuracies.

Computational structure-based protein design algorithms have successfully redesigned proteins to fold and bind target substrates in vitro, and even in vivo. Because the complexity of a computational design increases dramatically with the number of mutable residues, many design algorithms employ cutoffs (distance or energy) to neglect some pairwise residue interactions, thereby reducing the effective search space and computational cost. However, the energies neglected by such cutoffs can add up, which may have nontrivial effects on the designed sequence and its function. To study the effects of using cutoffs on protein design, we computed the optimal sequence both with and without cutoffs, and showed that neglecting long-range interactions can significantly change the computed conformation and sequence. Designs on proteins with experimentally measured thermostability showed the benefits of computing the optimal sequences (and their conformations), both with and without cutoffs, efficiently and accurately. Therefore, we also showed that a provable, ensemble-based algorithm can efficiently compute the optimal conformation and sequence, both with and without applying cutoffs, by enumerating a small number of conformations, usually fewer than 1000. This provides a novel way to combine cutoffs with provable, ensemble-based algorithms to reap the computational efficiency of cutoffs while avoiding their potential inaccuracies.

Rational design of low immunogenic anti CD25 recombinant immunotoxin for T cell malignancies by elimination of T cell epitopes in PE38

LMB-2, is a potent recombinant immunotoxin (RIT) that is composed of scFv antibody that targets CD25 (Tac) and a toxin fragment (PE38). It is used to treat T cell leukemias and lymphomas. To make LMB-2 less immunogenic, we introduced a large deletion in domain II and six point mutations in domain III that were previously shown to reduce T cell activation in other RITs. We found that unlike other RITs, deletion of domain II from LMB-2 severely compromised its activity. Rather than deletion, we identified T cell epitopes in domain II and used alanine substitutions to identify point mutations that diminished those epitopes. The novel RIT, LMB-142 contains a 38kDa toxin and nine point mutations that diminished T cell response to the corresponding peptides by an average of 75%. LMB-142 has good cytotoxic activity and has lower nonspecific toxicity in mice. LMB-142 should be more efficient in cancer therapy because more treatment cycles can be given.

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.

Achievements and Challenges in Computational Protein Design

Computational protein design (CPD), a yet evolving field, includes computer-aided engineering for partial or full de novo designs of proteins of interest. Designs are defined by a requested structure, function, or working environment. This chapter describes the birth and maturation of the field by presenting 101 CPD examples in a chronological order emphasizing achievements and pending challenges. Integrating these aspects presents the plethora of CPD approaches with the hope of providing a "CPD 101". These reflect on the broader structural bioinformatics and computational biophysics field and include: (1) integration of knowledge-based and energy-based methods, (2) hierarchical designated approach towards local, regional, and global motifs and the integration of high- and low-resolution design schemes that fit each such region, (3) systematic differential approaches towards different protein regions, (4) identification of key hot-spot residues and the relative effect of remote regions, (5) assessment of shape-complementarity, electrostatics and solvation effects, (6) integration of thermal plasticity and functional dynamics, (7) negative design, (8) systematic integration of experimental approaches, (9) objective cross-assessment of methods, and (10) successful ranking of potential designs. Future challenges also include dissemination of CPD software to the general use of life-sciences researchers and the emphasis of success within an in vivo milieu. CPD increases our understanding of protein structure and function and the relationships between the two along with the application of such know-how for the benefit of mankind. Applied aspects range from biological drugs, via healthier and tastier food products to nanotechnology and environmentally friendly enzymes replacing toxic chemicals utilized in the industry.

Role of HLA-DP in the Presentation of Epitopes from the Truncated Bacterial PE38 Immunotoxin

Identification of helper T-cell epitopes is important in many fields of medicine. We previously used an experimental approach to identify T-cell epitopes in PE38, a truncated bacterial toxin used in immunotoxins. Here, we evaluated the ability of antibodies to DR, DP, or DQ to block T-cell responses to PE38 epitopes in 36 PBMC samples. We predicted the binding affinities of peptides to DR, DP, and DQ alleles using computational tools and analyzed their ability to predict the T-cell epitopes. We found that HLA-DR is responsible for 65% of the responses, DP 24%, and DQ 4%. One epitope that is presented in 20% of the samples (10/50) is entirely DP restricted and was not predicted to bind to DR or DP reference alleles using binding algorithms. We conclude that DP has an important role in helper T-cell response to PE38.

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.

Immunogenicity of therapeutic recombinant immunotoxins

Recombinant immunotoxins (RITs) are chimeric proteins designed to treat cancer. They are made up of an Fv or Fab that targets an antigen on a cancer cell fused to a 38-kDa portion of Pseudomonas exotoxin A (PE38). Because PE38 is a bacterial protein, it is highly immunogenic in patients with solid tumors that have normal immune systems, but much less immunogenic in patients with hematologic malignancies where the immune system is suppressed. RITs have shown efficacy in refractory hairy cell leukemia and in some children with acute lymphoblastic leukemia, but have been much less effective in solid tumors, because neutralizing antibodies develop and prevent additional treatment cycles. In this paper we will (i) review data from clinical trials describing the immunogenicity of PE38 in different patient populations; (ii) review results from clinical trials using different immunosuppressive drugs; and (iii) describe our efforts to make new less-immunogenic RITs by identifying and removing T- and B-cell epitopes to hide the RIT from the immune system.

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.

CD4+ T-cell epitope prediction using antigen processing constraints

T-cell CD4+ epitopes are important targets of immunity against infectious diseases and cancer. State-of-the-art methods for MHC class II epitope prediction rely on supervised learning methods in which an implicit or explicit model of sequence specificity is constructed using a training set of peptides with experimentally tested MHC class II binding affinity. In this paper we present a novel method for CD4+ T-cell eptitope prediction based on modeling antigen-processing constraints. Previous work indicates that dominant CD4+ T-cell epitopes tend to occur adjacent to sites of initial proteolytic cleavage. Given an antigen with known three-dimensional structure, our algorithm first aggregates four types of conformational stability data in order to construct a profile of stability that allows us to identify regions of the protein that are most accessible to proteolysis. Using this profile, we then construct a profile of epitope likelihood based on the pattern of transitions from unstable to stable regions. We validate our method using 35 datasets of experimentally measured CD4+ T cell responses of mice bearing I-Ab or HLA-DR4 alleles as well as of human subjects. Overall, our results show that antigen processing constraints provide a significant source of predictive power. For epitope prediction in single-allele systems, our approach can be combined with sequence-based methods, or used in instances where little or no training data is available. In multiple-allele systems, sequence-based methods can only be used if the allele distribution of a population is known. In contrast, our approach does not make use of MHC binding prediction, and is thus agnostic to MHC class II genotypes.

BWM*: A Novel, Provable, Ensemble-based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design

Sparse energy functions that ignore long range interactions between residue pairs are frequently used by protein design algorithms to reduce computational cost. Current dynamic programming algorithms that fully exploit the optimal substructure produced by these energy functions only compute the GMEC. This disproportionately favors the sequence of a single, static conformation and overlooks better binding sequences with multiple low-energy conformations. Provable, ensemble-based algorithms such as A* avoid this problem, but A* cannot guarantee better performance than exhaustive enumeration. We propose a novel, provable, dynamic programming algorithm called Branch-Width Minimization* (BWM*) to enumerate a gap-free ensemble of conformations in order of increasing energy. Given a branch-decomposition of branch-width w for an n-residue protein design with at most q discrete side-chain conformations per residue, BWM* returns the sparse GMEC in O([Formula: see text]) time and enumerates each additional conformation in merely O([Formula: see text]) time. We define a new measure, Total Effective Search Space (TESS), which can be computed efficiently a priori before BWM* or A* is run. We ran BWM* on 67 protein design problems and found that TESS discriminated between BWM*-efficient and A*-efficient cases with 100% accuracy. As predicted by TESS and validated experimentally, BWM* outperforms A* in 73% of the cases and computes the full ensemble or a close approximation faster than A*, enumerating each additional conformation in milliseconds. Unlike A*, the performance of BWM* can be predicted in polynomial time before running the algorithm, which gives protein designers the power to choose the most efficient algorithm for their particular design problem.

Drug leads for interactive protein targets with unknown structure

The disruption of protein-protein interfaces (PPIs) remains a challenge in drug discovery. The problem becomes daunting when the structure of the target protein is unknown and is even further complicated when the interface is susceptible to disruptive phosphorylation. Based solely on protein sequence and information about phosphorylation-susceptible sites within the PPI, a new technology has been developed to identify drug leads to inhibit protein associations. Here we reveal this technology and contrast it with current structure-based technologies for the generation of drug leads. The novel technology is illustrated by a patented invention to treat heart failure. The success of this technology shows that it is possible to generate drug leads in the absence of target structure.

Progress and prospects of gene therapy clinical trials for the muscular dystrophies

Clinical trials represent a critical avenue for new treatment development, where early phases (I, I/II) are designed to test safety and effectiveness of new therapeutics or diagnostic indicators. A number of recent advances have spurred renewed optimism toward initiating clinical trials and developing refined therapies for the muscular dystrophies (MD's) and other myogenic disorders. MD's encompass a heterogeneous group of degenerative disorders often characterized by progressive muscle weakness and fragility. Many of these diseases result from mutations in genes encoding proteins of the dystrophin-glycoprotein complex (DGC). The most common and severe form among children is Duchenne muscular dystrophy, caused by mutations in the dystrophin gene, with an average life expectancy around 25 years of age. Another group of MD's referred to as the limb-girdle muscular dystrophies (LGMDs) can affect boys or girls, with different types caused by mutations in different genes. Mutation of the α-sarcoglycan gene, also a DGC component, causes LGMD2D and represents the most common form of LGMD. Early preclinical and clinical trial findings support the feasibility of gene therapy via recombinant adeno-associated viral vectors as a viable treatment approach for many MDs. In this mini-review, we present an overview of recent progress in clinical gene therapy trials of the MD's and touch upon promising preclinical advances.

Poor correlation between T-cell activation assays and HLA-DR binding prediction algorithms in an immunogenic fragment of Pseudomonas exotoxin A

The ability to identify immunogenic determinants that activate T-cells is important for the development of new vaccines, allergy therapy and protein therapeutics. In silico MHC-II binding prediction algorithms are often used for T-cell epitope identification. To understand how well those programs predict immunogenicity, we computed HLA binding to peptides spanning the sequence of PE38, a fragment of an anti-cancer immunotoxin, and compared the predicted and experimentally identified T-cell epitopes. We found that the prediction for individual donors did not correlate well with the experimental data. Furthermore, prediction of T-cell epitopes in an HLA heterogenic population revealed that the two strongest epitopes were predicted at multiple cutoffs but the third epitope was predicted negative at all cutoffs and overall 4/9 epitopes were missed at several cutoffs. We conclude that MHC class-II binding predictions are not sufficient to predict the T-cell epitopes in PE38 and should be supplemented by experimental work.

Adaptive Immunity to Leukemia Is Inhibited by Cross-Reactive Induced Regulatory T Cells

BCR-ABL(+) acute lymphoblastic leukemia patients have transient responses to current therapies. However, the fusion of BCR to ABL generates a potential leukemia-specific Ag that could be a target for immunotherapy. We demonstrate that the immune system can limit BCR-ABL(+) leukemia progression although ultimately this immune response fails. To address how BCR-ABL(+) leukemia escapes immune surveillance, we developed a peptide: MHC class II tetramer that labels endogenous BCR-ABL-specific CD4(+) T cells. Naive mice harbored a small population of BCR-ABL-specific T cells that proliferated modestly upon immunization. The small number of naive BCR-ABL-specific T cells was due to negative selection in the thymus, which depleted BCR-ABL-specific T cells. Consistent with this observation, we saw that BCR-ABL-specific T cells were cross-reactive with an endogenous peptide derived from ABL. Despite this cross-reactivity, the remaining population of BCR-ABL reactive T cells proliferated upon immunization with the BCR-ABL fusion peptide and adjuvant. In response to BCR-ABL(+) leukemia, BCR-ABL-specific T cells proliferated and converted into regulatory T (Treg) cells, a process that was dependent on cross-reactivity with self-antigen, TGF-β1, and MHC class II Ag presentation by leukemic cells. Treg cells were critical for leukemia progression in C57BL/6 mice, as transient Treg cell ablation led to extended survival of leukemic mice. Thus, BCR-ABL(+) leukemia actively suppresses antileukemia immune responses by converting cross-reactive leukemia-specific T cells into Treg cells.

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.

Target-specific cytotoxic effects on HER2-expressing cells by the tripartite fusion toxin ZHER2:2891-ABD-PE38X8, including a targeting affibody molecule and a half-life extension domain

Development of cancer treatment regimens including immunotoxins is partly hampered by their immunogenicity. Recently, deimmunized versions of toxins have been described, potentially being better suited for translation to the clinic. In this study, a recombinant tripartite fusion toxin consisting of a deimmunized version of exotoxin A from Pseudomonas aeruginosa (PE38) genetically fused to an affibody molecule specifically interacting with the human epidermal growth factor receptor 2 (HER2), and also an albumin binding domain (ABD) for half-life extension, has been produced and characterized in terms of functionality of the three moieties. Biosensor based assays showed that the fusion toxin was able to interact with human and mouse serum albumin, but not with bovine serum albumin and that it interacted with HER2 (KD=5 nM). Interestingly, a complex of the fusion toxin and human serum albumin also interacted with HER2 but with a somewhat weaker affinity (KD=12 nM). The IC50-values of the fusion toxin ranged from 6 to 300 pM on SKOV-3, SKBR-3 and A549 cells and was lower for cells with higher surface densities of HER2. The fusion toxin was found specific for HER2 as shown by blocking available HER2 receptors with free affibody molecule before subjecting the cells to the toxin. Analysis of contact time showed that 10 min was sufficient to kill 50% of the cells. In conclusion, all three regions of the fusion toxin were found to be functional.

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.

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.

Elimination of tumorigenic human pluripotent stem cells by a recombinant lectin-toxin fusion protein

The application of stem-cell-based therapies in regenerative medicine is hindered by the tumorigenic potential of residual human pluripotent stem cells. Previously, we identified a human pluripotent stem-cell-specific lectin probe, called rBC2LCN, by comprehensive glycome analysis using high-density lectin microarrays. Here we developed a recombinant lectin-toxin fusion protein of rBC2LCN with a catalytic domain of Pseudomonas aeruginosa exotoxin A, termed rBC2LCN-PE23, which could be expressed as a soluble form from the cytoplasm of Escherichia coli and purified to homogeneity by one-step affinity chromatography. rBC2LCN-PE23 bound to human pluripotent stem cells, followed by its internalization, allowing intracellular delivery of a cargo of cytotoxic protein. The addition of rBC2LCN-PE23 to the culture medium was sufficient to completely eliminate human pluripotent stem cells. Thus, rBC2LCN-PE23 has the potential to contribute to the safety of stem-cell-based therapies.

Immunosilencing a highly immunogenic protein trimerization domain

Many therapeutic proteins and protein subunit vaccines contain heterologous trimerization domains, such as the widely used GCN4-based isoleucine zipper (IZ) and the T4 bacteriophage fibritin foldon (Fd) trimerization domains. We found that these domains induced potent anti-IZ or anti-Fd antibody responses in animals when fused to an HIV-1 envelope glycoprotein (Env) immunogen. To dampen IZ-induced responses, we constructed an IZ domain containing four N-linked glycans (IZN4) to shield the underlying protein surface. When fused to two different vaccine antigens, HIV-1 Env and influenza hemagglutinin (HA), IZN4 strongly reduced the antibody responses against the IZ, but did not affect the antibody titers against Env or HA. Silencing of immunogenic multimerization domains with glycans might be relevant for therapeutic proteins and protein vaccines.

Recombinant immunotoxin for cancer treatment with low immunogenicity by identification and silencing of human T-cell epitopes

Nonhuman proteins have valuable therapeutic properties, but their efficacy is limited by neutralizing antibodies. Recombinant immunotoxins (RITs) are potent anticancer agents that have produced many complete remissions in leukemia, but immunogenicity limits the number of doses that can be given to patients with normal immune systems. Using human cells, we identified eight helper T-cell epitopes in PE38, a portion of the bacterial protein Pseudomonas exotoxin A which consists of the toxin moiety of the RIT, and used this information to make LMB-T18 in which three epitopes were deleted and five others diminished by point mutations in key residues. LMB-T18 has high cytotoxic and antitumor activity and is very resistant to thermal denaturation. The new immunotoxin has a 93% decrease in T-cell epitopes and should have improved efficacy in patients because more treatment cycles can be given. Furthermore, the deimmunized toxin can be used to make RITs targeting other antigens, and the approach we describe can be used to deimmunize other therapeutically useful nonhuman proteins.