Yoonjoo Choi

FREAD revisited: Accurate loop structure prediction using a database search algorithm

Loops are the most variable regions of protein structure and are, in general, the least accurately predicted. Their prediction has been approached in two ways, ab initio and database search. In recent years, it has been thought that ab initio methods are more powerful. In light of the continued rapid expansion in the number of known protein structures, we have re‐evaluated FREAD, a database search method and demonstrate that the power of database search methods may have been underestimated. We found that sequence similarity as quantified by environment specific substitution scores can be used to significantly improve prediction. In fact, FREAD performs appreciably better for an identifiable subset of loops (two thirds of shorter loops and half of the longer loops tested) than the ab initio methods of MODELLER, PLOP, and RAPPER. Within this subset, FREADs predictive ability is length independent, in general, producing results within 2Å RMSD, compared to an average of over 10Å for loop length 20 for any of the other tested methods. We also benchmarked the prediction protocols on a set of 212 loops from the model structures in CASP 7 and 8. An extended version of FREAD is able to make predictions for 127 of these, it gives the best prediction of the methods tested in 61 of these cases. In examining FREADs ability to predict in the model environment, we found that whole structure quality did not affect the quality of loop predictions. Proteins 2010.

Structure-guided deimmunization of therapeutic proteins.

Therapeutic proteins continue to yield revolutionary new treatments for a growing spectrum of human disease, but the development of these powerful drugs requires solving a unique set of challenges. For instance, it is increasingly apparent that mitigating potential anti-therapeutic immune responses, driven by molecular recognition of a therapeutic proteins peptide fragments, may be best accomplished early in the drug development process. One may eliminate immunogenic peptide fragments by mutating the cognate amino acid sequences, but deimmunizing mutations are constrained by the need for a folded, stable, and functional protein structure. These two concerns may be competing, as the mutations that are best at reducing immunogenicity often involve amino acids that are substantially different physicochemically. We develop a novel approach, called EpiSweep, that simultaneously optimizes both concerns. Our algorithm identifies sets of mutations making such Pareto optimal trade-offs between structure and immunogenicity, embodied by a molecular mechanics energy function and a T-cell epitope predictor, respectively. EpiSweep integrates structure-based protein design, sequence-based protein deimmunization, and algorithms for finding the Pareto frontier of a design space. While structure-based protein design is NP-hard, we employ integer programming techniques that are efficient in practice. Furthermore, EpiSweep only invokes the optimizer once per identified Pareto optimal design. We show that EpiSweep designs of regions of the therapeutics erythropoietin and staphylokinase are predicted to outperform previous experimental efforts. We also demonstrate EpiSweeps capacity for deimmunization of the entire proteins, case analyses involving dozens of predicted epitopes, and tens of thousands of unique side-chain interactions. Ultimately, Epi-Sweep is a powerful protein design tool that guides the protein engineer toward the most promising immunotolerant biotherapeutic candidates.

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.

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 proteins 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. Biotechnol. Bioeng. 2015;112: 1306–1318.

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.

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

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

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.

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

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

Gene and Protein Sequence Optimization for High-Level Production of Fully Active and Aglycosylated Lysostaphin in Pichia pastoris

ABSTRACT Lysostaphin represents a promising therapeutic agent for the treatment of staphylococcal infections, in particular those of methicillin-resistant Staphylococcus aureus (MRSA). However, conventional expression systems for the enzyme suffer from various limitations, and there remains a need for an efficient and cost-effective production process to facilitate clinical translation and the development of nonmedical applications. While Pichia pastoris is widely used for high-level production of recombinant proteins, there are two major barriers to the production of lysostaphin in this industrially relevant host: lack of expression from the wild-type lysostaphin gene and aberrant glycosylation of the wild-type protein sequence. The first barrier can be overcome with a synthetic gene incorporating improved codon usage and balanced A+T/G+C content, and the second barrier can be overcome by disrupting an N-linked glycosylation sequon using a broadened choice of mutations that yield aglyscosylated and fully active lysostaphin. The optimized lysostaphin variants could be produced at approximately 500 mg/liter in a small-scale bioreactor, and 50% of that material could be recovered at high purity with a simple 2-step purification. It is anticipated that this novel high-level expression system will bring down one of the major barriers to future development of biomedical, veterinary, and research applications of lysostaphin and its engineered variants.

Structure‐based redesign of proteins for minimal T‐cell epitope content

The protein universe displays a wealth of therapeutically relevant activities, but T‐cell driven immune responses to non‐“self” biological agents present a major impediment to harnessing the full diversity of these molecular functions. Mutagenic T‐cell epitope deletion seeks to mitigate the immune response, but can typically address only a small number of epitopes. Here, we pursue a “bottom‐up” approach that redesigns an entire protein to remain native‐like but contain few if any immunogenic epitopes. We do so by extending the Rosetta flexible‐backbone protein design software with an epitope scoring mechanism and appropriate constraints. The method is benchmarked with a diverse panel of proteins and applied to three targets of therapeutic interest. We show that the deimmunized designs indeed have minimal predicted epitope content and are native‐like in terms of various quality measures, and moreover that they display levels of native sequence recovery comparable to those of non‐deimmunized designs.