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

Synthetic transcription factor engineering for cell and gene therapy

Synthetic transcription factors (synTFs) that control beneficial transgene expression are an important method to increase the safety and efficacy of cell and gene therapy. Reliance on synTF components from non-human sources has slowed progress in the field because of concerns about immunogenicity and inducer drug properties. Recent advances in human-derived DNA-binding domains (DBDs) and transcriptional activation domains (TADs) paired with novel control modules responsive to clinically approved small molecules have poised the synTF field to overcome these hurdles. Advances include controllers inducible by autonomous signaling inputs and more complex, multi-input synTF circuits. Demonstrations of advanced control strategies with human-derived transcription factor components in clinically relevant vectors and in vivo models will facilitate progression into the clinic.

Machine learning for functional protein design

Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.

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.

Complementation Dependent Enzyme Prodrug Therapy Enables Targeted Activation of Prodrug on HER2-Positive Cancer Cells

Antibodies have been explored for decades for the delivery of small molecule cytotoxins directly to diseased cells. In antibody-directed enzyme prodrug therapy (ADEPT), antibodies are armed with enzymes that activate nontoxic prodrugs at tumor sites. However, this strategy failed clinically due to off-target toxicity associated with the enzyme prematurely activating prodrug systemically. We describe here the design of an antibody-fragment split enzyme platform that regains activity after binding to HER2, allowing for site-specific activation of a small molecule prodrug. We evaluated a library of fusion constructs for efficient targeting and complementation to identify the most promising split enzyme pair. The optimal pair was screened for substrate specificity among chromogenic, fluorogenic, and prodrug substrates. Evaluation of this system on HER2-positive cells revealed 7-fold higher toxicity of the activated prodrug over prodrug treatment alone. Demonstrating the potential of this strategy against a known clinical target provides the basis for a unique therapeutic platform in oncology.

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.

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

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

Reduction of Pre-Existing Adaptive Immune Responses Against SaCas9 in Humans Using Epitope Mapping and Identification

The CRISPR-Cas9 system is increasingly being used as a gene editing therapeutic technique in complex diseases but concerns remain regarding the clinical risks of Cas9 immunogenicity. In this study, we detected antibodies against Staphylococcus aureus Cas9 (SaCas9) and anti-SaCas9 T cells in 4.8% and 70% of Chinese donors, respectively. We predicted 135 SaCas9-derived B cell epitopes and 50 SaCas9-derived CD8+ T cell epitopes for HLA-A*24:02, HLA-A*11:01, and HLA-A*02:01. We identified R338 as an immunodominant SaCas9 B cell epitope and SaCas9_200-208 as an immunodominant CD8+ T cell epitope for the three human leukocyte antigen allotypes through immunological assays using sera positive for SaCas9-specific antibodies and peripheral blood mononuclear cells positive for SaCas9-reactive T cells, respectively. We also demonstrated that an SaCas9 variant bearing an R338G substitution reduces B cell immunogenicity and retains its gene-editing function. Our study highlights the immunological risks of the CRISPR-Cas9 system and provides a solution to mitigate pre-existing adaptive immune responses against Cas9 in humans.

Accelerating therapeutic protein design

Protein structures provide for defined microenvironments that can support complex pharmacological functions, otherwise unachievable by small molecules. The advent of therapeutic proteins has thus greatly broadened the range of manageable disorders. Leveraging the knowledge and recent advances in de novo protein design methods has the prospect of revolutionizing how protein drugs are discovered and developed. This review lays out the main challenges facing therapeutic proteins discovery and development, and how present and future advancements of protein design can accelerate the protein drug pipelines.

Protein engineering: a driving force toward synthetic immunology

The full application of the diverse toolkit of protein engineering has made it easier to control the immune system. In particular, synthetic cytokine variants and engineered immune receptor platforms have shown promise for the treatment of various indications with dysregulated immune function, particularly cancer. Here, we review recent advances in the control of immune cell signaling and therapeutic potency that have employed protein engineering strategies. We further discuss how safety concerns are driving the design of immunotherapeutics toward 'user-defined' control or requiring multiple distinct inputs before a functional response, highlighting emergent control strategies employed for chimeric antigen receptor (CAR) engineering.

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.

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

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

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

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

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.

Globally deimmunized lysostaphin evades human immune surveillance and enables highly efficacious repeat dosing

There is a critical need for novel therapies to treat methicillin-resistant Staphylococcus aureus (MRSA) and other drug-resistant pathogens, and lysins are among the vanguard of innovative antibiotics under development. Unfortunately, lysins' own microbial origins can elicit detrimental antidrug antibodies (ADAs) that undermine efficacy and threaten patient safety. To create an enhanced anti-MRSA lysin, a novel variant of lysostaphin was engineered by T cell epitope deletion. This "deimmunized" lysostaphin dampened human T cell activation, mitigated ADA responses in human HLA transgenic mice, and enabled safe and efficacious repeated dosing during a 6-week longitudinal infection study. Furthermore, the deimmunized lysostaphin evaded established anti-wild-type immunity, thereby providing significant anti-MRSA protection for animals that were immune experienced to the wild-type enzyme. Last, the enzyme synergized with daptomycin to clear a stringent model of MRSA endocarditis. By mitigating T cell-driven antidrug immunity, deimmunized lysostaphin may enable safe, repeated dosing to treat refractory MRSA infections.

A High-Throughput Single-Clone Phage Fluorescence Microwell Immunoassay and Laser-Driven Clonal Retrieval System

Phage display is one of the most frequently used platform technologies utilized to screen and select therapeutic antibodies, and has contributed to the development of more than 10 therapeutic antibodies used in the clinic. Despite advantages like efficiency and low cost, it has intrinsic technical limitations, such as the asymmetrical amplification of the library after each round of biopanning, which is regarded as a reason for it yielding a very limited number of antigen binders. In this study, we developed a high-throughput single-clonal screening system comprised of fluorescence immunoassays and a laser-driven clonal DNA retrieval system using microchip technology. Using this system, from a single-chain variable fragment (scFv) library displayed on phages with a complexity of 5.21 × 105 harboring random mutations at five amino acid residues, more than 70,000 clones-corresponding to ~14% of the library complexity-were screened, resulting in 78 antigen-reactive scFv sequences with mutations restricted to the randomized residues. Our results demonstrate that this system can significantly reduce the number of biopanning rounds, or even eliminate the need for this process for libraries with lower complexity, providing an opportunity to obtain more diverse clones from the library.

Advances in protein structure prediction and design

The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem - designing an amino acid sequence that will fold into a specified three-dimensional structure - has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and the rapid growth in protein sequence and structure databases have fuelled the development of new data-intensive and computationally demanding approaches for structure prediction. New algorithms for designing protein folds and protein-protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties, as well as signalling proteins with therapeutic potential. In this Review, we describe current approaches for protein structure prediction and design and highlight a selection of the successful applications they have enabled.

ImmunomeBrowser: a tool to aggregate and visualize complex and heterogeneous epitopes in reference proteins

Motivation

Datasets that are derived from different studies (e.g. MHC ligand elution, MHC binding, B/T cell epitope screening etc.) often vary in terms of experimental approaches, sizes of peptides tested, including partial and or nested overlapping peptides and in the number of donors tested.

Results

We present a customized application of the Immune Epitope Database’s ImmunomeBrowser tool, which can be used to effectively aggregate and visualize heterogeneous immunological data. User provided peptide sets and associated response data is mapped to a user-provided protein reference sequence. The output consists of tables and figures representing the aggregated data represented by a Response Frequency score and associated estimated confidence interval. This allows the user to visualizing regions associated with dominant responses and their boundaries. The results are presented both as a user interactive javascript based web interface and a tabular format in a selected reference sequence.

Availability and implementation

The ‘ImmunomeBrowser’ has been a longstanding feature of the IEDB (http://www.iedb.org). The present application extends the use of this tool to work with user-provided datasets, rather than the output of IEDB queries. This new server version of the ImmunomeBrowser is freely accessible at http://tools.iedb.org/immunomebrowser/.

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.

Balancing sensitivity and specificity in distinguishing TCR groups by CDR sequence similarity

Background

Repertoire sequencing is enabling deep explorations into the cellular immune response, including the characterization of commonalities and differences among T cell receptor (TCR) repertoires from different individuals, pathologies, and antigen specificities. In seeking to understand the generality of patterns observed in different groups of TCRs, it is necessary to balance how well each pattern represents the diversity among TCRs from one group (sensitivity) vs. how many TCRs from other groups it also represents (specificity). The variable complementarity determining regions (CDRs), particularly the third CDRs (CDR3s) interact with major histocompatibility complex (MHC)-presented epitopes from putative antigens, and thus encode the determinants of recognition.

Results

We here systematically characterize the predictive power that can be obtained from CDR3 sequences, using representative, readily interpretable methods for evaluating CDR sequence similarity and then clustering and classifying sequences based on similarity. An initial analysis of CDR3s of known structure, clustered by structural similarity, helps calibrate the limits of sequence diversity among CDRs that might have a common mode of interaction with presented epitopes. Subsequent analyses demonstrate that this same range of sequence similarity strikes a favorable specificity/sensitivity balance in distinguishing twins from non-twins based on overall CDR3 repertoires, classifying CDR3 repertoires by antigen specificity, and distinguishing general pathologies.

Conclusion

We conclude that within a fairly broad range of sequence similarity, matching CDR3 sequences are likely to share specificities.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2864-8) contains supplementary material, which is available to authorized users.

Activation of Prodrugs by NIR-Triggered Release of Exogenous Enzymes for Locoregional Chemo-photothermal Therapy

Enzymes have been used to direct the conversion of prodrugs in cancer therapy. However, non-specific distribution of endogenous enzymes seriously hinders their bioapplications. Herein, we developed a near-infrared-triggered locoregional chemo-photothermal therapy based on the exogenous enzyme delivery and remolded tumor mivroenvironment. The catalytic efficiency of enzymes was enhanced by the hyperthermia, and the therapeutic efficacy of photothermal therapy (PTT) was improved owing to the inhibition of heat shock protein 90 by chemotherapeutics. The locoregional chemo-phototherapy achieved a one-time successful cure in 4T1 tumor-bearing mice model. Thus, a mutually reinforcing feedback loop between PTT and chemotherapy can be initiated by the irradiation, which holds a promising future in cancer therapy.

Multifunctional CRISPR-Cas9 with engineered immunosilenced human T cell epitopes

The CRISPR-Cas9 system has raised hopes for developing personalized gene therapies for complex diseases. Its application for genetic and epigenetic therapies in humans raises concerns over immunogenicity of the bacterially derived Cas9 protein. Here we detect antibodies to Streptococcus pyogenes Cas9 (SpCas9) in at least 5% of 143 healthy individuals. We also report pre-existing human CD8+T cell immunity in the majority of healthy individuals screened. We identify two immunodominant SpCas9 T cell epitopes for HLA-A*02:01 using an enhanced prediction algorithm that incorporates T cell receptor contact residue hydrophobicity and HLA binding and evaluated them by T cell assays using healthy donor PBMCs. In a proof-of-principle study, we demonstrate that Cas9 protein can be modified to eliminate immunodominant epitopes through targeted mutation while preserving its function and specificity. Our study highlights the problem of pre-existing immunity against CRISPR-associated nucleases and offers a potential solution to mitigate the T cell immune response.

Possible immunogenicity of the Cas9 protein raises concerns about therapeutic applications. Here the authors identify pre-existing CD8+T-cell immunity in healthy individuals and in response modify Cas9 to remove the immunodominant epitopes.

Peptide design by optimization on a data-parameterized protein interaction landscape

Many applications in protein engineering require optimizing multiple protein properties simultaneously, such as binding one target but not others or binding a target while maintaining stability. Such multistate design problems require navigating a high-dimensional space to find proteins with desired characteristics. A model that relates protein sequence to functional attributes can guide design to solutions that would be hard to discover via screening. In this work, we measured thousands of protein-peptide binding affinities with the high-throughput interaction assay amped SORTCERY and used the data to parameterize a model of the alpha-helical peptide-binding landscape for three members of the Bcl-2 family of proteins: Bcl-x_L, Mcl-1, and Bfl-1. We applied optimization protocols to explore extremes in this landscape to discover peptides with desired interaction profiles. Computational design generated 36 peptides, all of which bound with high affinity and specificity to just one of Bcl-x_L, Mcl-1, or Bfl-1, as intended. We designed additional peptides that bound selectively to two out of three of these proteins. The designed peptides were dissimilar to known Bcl-2-binding peptides, and high-resolution crystal structures confirmed that they engaged their targets as expected. Excellent results on this challenging problem demonstrate the power of a landscape modeling approach, and the designed peptides have potential uses as diagnostic tools or cancer therapeutics.

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.

Antibody Directed Enzyme Prodrug Therapy (ADEPT): Trials and tribulations

Antibody directed enzyme prodrug therapy has the potential to be an effective therapy for most common solid cancers. Clinical studies with CPG2 system have shown the feasibility of this approach. The key limitation has been immunogenicity of the enzyme. Technologies now exist to eliminate this problem. Non-immunogenic enzymes in combination with prodrugs that generate potent cytotoxic drugs can provide a powerful approach to cancer therapy. ADEPT has the potential to be non -toxic to normal tissue and can therefore be combined with other modalities including immunotherapy for greater clinical benefit.