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Title: Mapping the Pareto Optimal Design Space for a Functionally Deimmunized Biotherapeutic Candidate}
Article:
Abstract:
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.
Introduction:
Therapeutic proteins are revolutionizing disease therapy across a broad range of indications and illnesses, and biotherapeutic sales are an increasingly important part of the pharmaceuticals market[1],[2]. However, these powerful drugs suffer from their own limitations, which, if addressed, could accelerate the pace of biotherapeutic development and approval. A relatively unique risk factor for protein therapeutics is their inherent potential to induce anti-drug immune responses in human patients[3],[4],[5]. These undesirable immune reactions can compromise drug efficacy or cause more serious adverse events[6],[7]. In a healthy human immune system, all extracellular proteins are sampled by antigen presenting cells( APCs). Once internalized by APCs, a protein is cleaved into small peptide fragments, putative immunogenic segments are loaded into the groove of class II major histocompatibility complex proteins( MHC II), and the complexes are trafficked to the APC surface. True immunogenic peptides, termed T cell epitopes, facilitate the formation of ternary MHC II-peptide-T cell receptor complexes with surface receptors of cognate CD4+ T cells[8]. This critical molecular recognition event initiates a signaling cascade that drives stimulation of helper T cells, maturation of B cells, and ultimately production of circulating antibodies that bind to and clear the foreign therapeutic protein. Detailed knowledge of this process enables protein deimmunization via mutation of key residues in immunogenic epitopes, a methodology commonly known as T cell epitope deletion. There exist in the literature numerous examples of successful T cell epitope deletion projects. To date, the majority of these efforts have relied on time, labor, and resource intensive experimental strategies. Experimentally driven approaches entail dividing the target protein's primary sequence into a large panel of overlapping peptides, synthesizing those peptides, and using them for exhaustive epitope mapping with human peripheral blood mononuclear cells and/or purified human MHC II proteins[9],[10],[11],[12],[13],[14]. To circumvent the considerable effort and expense required for experimental epitope mapping, a wide range of epitope prediction tools may be accessed[15],[16],[17],[18],[19],[20],[21],[22],[23]. In some recent cases, such tools have been leveraged to good effect in deimmunizing therapeutic candidates[24],[25]. Identification and mapping of T cell epitopes is a relatively mature field, but selection of mutations that simultaneously delete epitopes and maintain protein function remains a challenging task. Experimentally driven deimmunization typically relies on alanine scanning or similar empirical strategies to select epitope deleting mutations. While Cantor et al. employed epitope prediction to rapidly identify immunogenic regions of asparaginase, their selection of deimmunizing yet function-preserving mutations required construction of large combinatorial protein libraries and implementation of a sophisticated ultra-high throughput screen[24]. As an alternative to the above methods, bioinformatics tools are increasingly used to filter prospective deimmunizing mutations for those least likely to disrupt protein structure and function[12],[26], however these in silico analyses of a mutation's structural and functional consequences have historically been applied post hoc. Any such sequential application of computational tools fails to consider the combined effects of all mutations on immunogenicity and function, thereby precluding a global approach to protein deimmunization. Thus, while T cell epitope deletion is a well validated methodology, the success, efficiency, and general utility of the approach would be enhanced by bringing to bear more advanced protein engineering and design technologies. The next generation of protein deimmunization tools have seamlessly integrated immunoinformatic epitope prediction with in silico analysis of the functional consequences associated with prospective deimmunizing mutations[27],[28],[29],[30]. By packaging both design objectives in a single optimization algorithm, these technologies enable global protein design and deimmunization on a highly compressed time scale. The first two iterations of these novel algorithms, Dynamic Programming for Deimmunizing Proteins( DP2) and Integer Programming for Immunogenic Proteins( IP2), have undergone preliminary experimental validation with Enterobacter cloacae P99 Beta-lactamase( P99βL)[25],[31], a biotherapeutic that has been deimmunized previously using conventional experimentally-driven techniques[9]. Here we deimmunize the P99βL target using a more advanced extension of IP2, embodied in the protein design algorithm “Protein Engineering Pareto Frontier”( Pepfr)[32]. Whereas IP2 samples a subset of the designs that optimally balance the immunogenicity and functionality objectives, Pepfr generates the entire set of Pareto optimal variants, i. e. all enzymes whose predicted immunogenicity and functionality are not simultaneously dominated by any other single design. Eighteen of these Pareto optimal variants have been produced and subjected to a rigorous experimental analysis of key performance parameters. This combined computational and experimental analysis of increasingly aggressive plans has provided new insights into the inherent tradeoffs linking the target enzyme's sequence, function, and immunogenic potential. As a whole, this work outlines a design-based approach to functional deimmunization of biotherapeutic candidates.
Discussion:
Mutagenic deletion of T cell epitopes, which has been successfully implemented with diverse proteins, is a powerful means for deimmunizing biotherapeutics. With very few exceptions, however, published studies of T cell epitope deletion, in full length proteins, have focused on disrupting one or two immunogenic regions[9],[10],[11],[12],[14],[25],[43]. Indeed, there is debate regarding the feasibility of broad, protein-wide epitope deletion, which is complicated by the high degree of MHC II polymorphism in human populations[44]. Thus, while there are many reports of limited but successful T cell epitope deletion, one is left to wonder how many projects might have failed due to the presence of numerous and dispersed immunogenic regions that could not be targeted simultaneously using conventional strategies. To more fully understand this challenge, we have conducted a combined computational and experimental analysis of the immunogenicity and functionality tradeoffs that are inherent to the deimmunization problem. The studies described here were enabled by an advanced deimmunization algorithm that seamlessly integrates immunogenic epitope prediction with in silico analysis of the functional consequences associated with deimmunizing mutations. We combined the IP2 deimmunization formulation with the Pepfr optimization algorithm[28],[32] to design a suite of 18 Pareto optimal P99βL variants. Each of these designs optimally balances two objective functions – one modeling immunogenicity and the other functionality – such that no other single variant is predicted to outperform with respect to both design objectives. Inspection of the Pareto optimal designs reveals that, in the context of the mathematical model, there is an inverse relationship wherein ever greater deimmunization is achieved at the expense of progressively reduced function( Fig. 8A). To assess the practical implications of these predicted tradeoffs, we have recombinantly produced all 18 designed enzymes and rigorously characterized their stability, activity, and immunoreactivity with human MHC II proteins. The results of this analysis represent the first systematic assessment of the functional penalty that is paid for pursuing progressively more deimmunized drug candidates. Our previous work had demonstrated the capacity to design P99βL variants bearing 1–5 deimmunizing mutations yet retaining wild type or better activity and near wild type stability[25],[31]. By more thoroughly mapping the Pareto optimal design space, we show here that up to seven immunogenic regions can be simultaneously targeted while incurring essentially no loss in molecular stability and function( see design 8T, Table 1). Considering only the most aggressive designs( 8T to 8Z), the computational Pareto curve suggested that there would be an accelerating loss of molecular function throughout the series, ultimately resulting in a 6-fold deterioration relative to 8T( Fig. 8A). The experimental analysis verified the predicted trend, revealing that the 8-mutation series did in fact exhibit a systematic reduction in stability and catalytic efficiency. Importantly, however, design 8Z showed a mere 40% reduction in catalytic proficiency relative to 8T. This dramatic difference in the magnitude of ΔSseq versus measured change in molecular function suggests a non-linear relationship between the statistical sequence potential and actual experimental performance. Indeed, a non-linear correlation is suggested by the above graphical analysis( Fig. 4A, C, D). Thus, it seems likely that more aggressive designs exhibiting even higher Sseq penalties might be realized experimentally before reaching the point of diminishing returns. In other words, it appears we have yet to reach the practical limit of epitope depletion for P99βL. While the quantified losses in activity and stability were not as sharp as predicted by the Sseq design parameter, there was in fact a general trend towards escalating loss of function with more aggressive deimmunization. To better visualize these real world tradeoffs, we constructed the experimental analog of the Pareto curve( Fig. 8B). This analysis entailed plotting an integrated experimental performance score( averaging the normalized, reciprocal values for Tm, kcat, and kcat/Km; equation 3) vs. the quantitative global immunoreactivity score( equation 4). The graphical analysis clearly shows that more aggressively deimmunized enzymes sustained progressively greater losses of molecular function. Analogous plots were constructed for various individual performance parameters as well as alternative combinations of these parameters( Fig. 8C-H). It is interesting that the computationally generated Pareto plot most effectively captures the general trends observed with integrated, as opposed to individual, experimental performance measures( compare Fig. 8 panels B, G and H to panels C, D, E and F). This is a notable and advantageous outcome, as biotherapeutic researchers will typically be interested in overall molecular performance as opposed to any single metric( e. g. thermostability, binding affinity, rate acceleration, or catalytic efficiency). As a whole, the parallels between the computational and experimental Pareto plots are striking, and this observation underscores the Pepfr algorithm's capacity to effectively factor in the inherent tradeoffs between immunogenicity and molecular function. As a final note, previous engineering of P99βL with the IP2 algorithm had produced higher activity variants than those designed with the earlier DP2 algorithm[31]. However, the IP2 designs from the former study were generated after locking down all residues in close proximity the active site. This raised the question of whether or not the combined 1-body + 2-body sequence potential of IP2 was in fact more effective than the 1-body potential implemented in DP2, where mutations to active site residues had been allowed[25]. The Pareto optimal designs from the present study did not benefit from locked active site residues, yet the 4-mutation and 5-mutation designs from this study substantially outperformed previous 2-mutation DP2 designs and were largely equivalent to previous 4-mutation and 5-mutation IP2 designs in which the active site had been held invariant( S3 Fig.). This result shows that the more advanced sequence potential of IP2 can in fact generate highly mutated and yet highly active proteins in the absence of detailed structure-function information. Moreover, when residues need not be locked down during the design process, there is greater inherent capacity for epitope deletion. In conclusion, we have computationally and experimentally mapped the deimmunized Pareto frontier of P99βL. The predictions underlying the design process correlated well with experimental analyses of protein function. In particular, we observed that incremental deletion of progressively more T cell epitopes lead to a relative escalation in concomitant loss of function. Thus, the predicted tradeoffs underlying protein deimmunization were borne out in real world analyses. Nonetheless, all 18 of the computationally designed enzymes proved to exhibit reasonable thermostability and impressive activity; not a single design failed to express or function. The most highly engineered enzyme, which incorporated eight mutations targeting seven distinct epitope clusters, was found to have a 39% reduction in high affinity MHC II binding interactions while maintaining well over 50% of the wild type enzyme's catalytic activity. It is therefore evident that we have additional capacity for designing even more extensively deimmunized yet functional P99βL variants. If these trends translate to other therapeutic proteins, as anticipated, the integrated design algorithms evaluated here will accelerate identification of engineered variants spanning a broad spectrum of immunogenic potential and biological function. These panels of deimmunized proteins should prove a rich resource from which to select therapeutic candidates that meet diverse clinical needs.
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 .