Karl E. Griswold

Tumor cell targeting by iron oxide nanoparticles is dominated by different factors in vitro versus in vivo.

Realizing the full potential of iron oxide nanoparticles (IONP) for cancer diagnosis and therapy requires selective tumor cell accumulation. Here, we report a systematic analysis of two key determinants for IONP homing to human breast cancers: (i) particle size and (ii) active vs passive targeting. In vitro, molecular targeting to the HER2 receptor was the dominant factor driving cancer cell association. In contrast, size was found to be the key determinant of tumor accumulation in vivo, where molecular targeting increased tumor tissue concentrations for 30 nm but not 100 nm IONP. Similar to the in vitro results, PEGylation did not influence in vivo IONP biodistribution. Thus, the results reported here indicate that the in vitro advantages of molecular targeting may not consistently extend to pre-clinical in vivo settings. These observations may have important implications for the design and clinical translation of advanced, multifunctional, IONP platforms.

A high-throughput screen for antibiotic drug discovery.

We describe an ultra‐high‐throughput screening platform enabling discovery and/or engineering of natural product antibiotics. The methodology involves creation of hydrogel‐in‐oil emulsions in which recombinant microorganisms are co‐emulsified with bacterial pathogens; antibiotic activity is assayed by use of a fluorescent viability dye. We have successfully utilized both bulk emulsification and microfluidic technology for the generation of hydrogel microdroplets that are size‐compatible with conventional flow cytometry. Hydrogel droplets are ∼25 pL in volume, and can be synthesized and sorted at rates exceeding 3,000 drops/s. Using this technique, we have achieved screening throughputs exceeding 5 million clones/day. Proof‐of‐concept experiments demonstrate efficient selection of antibiotic‐secreting yeast from a vast excess of negative controls. In addition, we have successfully used this technique to screen a metagenomic library for secreted antibiotics that kill the human pathogen Staphylococcus aureus. Our results establish the practical utility of the screening platform, and we anticipate that the accessible nature of our methods will enable others seeking to identify and engineer the next generation of antibacterial biomolecules. Biotechnol. Bioeng. 2014;111: 232–243.

Optimization algorithms for functional deimmunization of therapeutic proteins

BackgroundTo develop protein therapeutics from exogenous sources, it is necessary to mitigate the risks of eliciting an anti-biotherapeutic immune response. A key aspect of the response is the recognition and surface display by antigen-presenting cells of epitopes, short peptide fragments derived from the foreign protein. Thus, developing minimal-epitope variants represents a powerful approach to deimmunizing protein therapeutics. Critically, mutations selected to reduce immunogenicity must not interfere with the proteins therapeutic activity.ResultsThis paper develops methods to improve the likelihood of simultaneously reducing the anti-biotherapeutic immune response while maintaining therapeutic activity. A dynamic programming approach identifies optimal and near-optimal sets of conservative point mutations to minimize the occurrence of predicted T-cell epitopes in a target protein. In contrast with existing methods, those described here integrate analysis of immunogenicity and stability/activity, are broadly applicable to any protein class, guarantee global optimality, and provide sufficient flexibility for users to limit the total number of mutations and target MHC alleles of interest. The input is simply the primary amino acid sequence of the therapeutic candidate, although crystal structures and protein family sequence alignments may also be input when available. The output is a scored list of sets of point mutations predicted to reduce the proteins immunogenicity while maintaining structure and function. We demonstrate the effectiveness of our approach in a number of case study applications, showing that, in general, our best variants are predicted to be better than those produced by previous deimmunization efforts in terms of either immunogenicity or stability, or both factors.ConclusionsBy developing global optimization algorithms leveraging well-established immunogenicity and stability prediction techniques, we provide the protein engineer with a mechanism for exploring the favorable sequence space near a targeted protein therapeutic. Our mechanism not only helps identify designs more likely to be effective, but also provides insights into the interrelated implications of design choices.

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.

Enhanced antimicrobial activity of engineered human lysozyme

Lysozymes contain a disproportionately large fraction of cationic residues, and are thereby attracted toward the negatively charged surface of bacterial targets. Importantly, this conserved biophysical property may inhibit lysozyme antibacterial function during acute and chronic infections. A mouse model of acute pulmonary Pseudomonas aeruginosa infection demonstrated that anionic biopolymers accumulate to high concentrations in the infected lung, and the presence of these species correlates with decreased endogenous lysozyme activity. To develop antibacterial enzymes designed specifically to be used as antimicrobial agents in the infected airway, the electrostatic potential of human lysozyme (hLYS) was remodeled by protein engineering. A novel, high-throughput screen was implemented to functionally interrogate combinatorial libraries of charge-engineered hLYS proteins, and variants with improved bactericidal activity were isolated and characterized in detail. These studies illustrate a general mechanism by which polyanions inhibit lysozyme function, and they are the first direct demonstration that decreasing hLYSs net cationic character improves its antibacterial activity in the presence of disease-associated biopolymers. In addition to avoiding electrostatic sequestration, at least one charge-engineered variant also kills bacteria more rapidly in the absence of inhibitory biopolymers; this observation supports a novel hypothesis that tuning the cellular affinity of peptidoglycan hydrolases may be a general strategy for improving kinetics of bacterial killing.

Molecular sensing with magnetic nanoparticles using magnetic spectroscopy of nanoparticle Brownian motion.

Functionalized magnetic nanoparticles (mNPs) have shown promise in biosensing and other biomedical applications. Here we use functionalized mNPs to develop a highly sensitive, versatile sensing strategy required in practical biological assays and potentially in vivo analysis. We demonstrate a new sensing scheme based on magnetic spectroscopy of nanoparticle Brownian motion (MSB) to quantitatively detect molecular targets. MSB uses the harmonics of oscillating mNPs as a metric for the freedom of rotational motion, thus reflecting the bound state of the mNP. The harmonics can be detected in vivo from nanogram quantities of iron within 5s. Using a streptavidin-biotin binding system, we show that the detection limit of the current MSB technique is lower than 150 pM (0.075 pmole), which is much more sensitive than previously reported techniques based on mNP detection. Using mNPs conjugated with two anti-thrombin DNA aptamers, we show that thrombin can be detected with high sensitivity (4 nM or 2 pmole). A DNA-DNA interaction was also investigated. The results demonstrated that sequence selective DNA detection can be achieved with 100 pM (0.05 pmole) sensitivity. The results of using MSB to sense these interactions, show that the MSB based sensing technique can achieve rapid measurement (within 10s), and is suitable for detecting and quantifying a wide range of biomarkers or analytes. It has the potential to be applied in variety of biomedical applications or diagnostic analyses.

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.

Crystal structure of a charge engineered human lysozyme having enhanced bactericidal activity.

Human lysozyme is a key component of the innate immune system, and recombinant forms of the enzyme represent promising leads in the search for therapeutic agents able to treat drug-resistant infections. The wild type protein, however, fails to participate effectively in clearance of certain infections due to inherent functional limitations. For example, wild type lysozymes are subject to electrostatic sequestration and inactivation by anionic biopolymers in the infected airway. A charge engineered variant of human lysozyme has recently been shown to possess improved antibacterial activity in the presence of disease associated inhibitory molecules. Here, the 2.04 Å crystal structure of this variant is presented along with an analysis that provides molecular level insights into the origins of the proteins enhanced performance. The charge engineered variants two mutated amino acids exhibit stabilizing interactions with adjacent native residues, and from a global perspective, the mutations cause no gross structural perturbations or loss of stability. Importantly, the two substitutions dramatically expand the negative electrostatic potential that, in the wild type enzyme, is restricted to a small region near the catalytic residues. The net result is a reduction in the overall strength of the engineered enzymes electrostatic potential field, and it appears that the specific nature of this remodeled field underlies the variants reduced susceptibility to inhibition by anionic biopolymers.

Optimization of combinatorial mutagenesis

Protein engineering by combinatorial site-directed mutagenesis evaluates a portion of the sequence space near a target protein, seeking variants with improved properties (e.g., stability, activity, immunogenicity). In order to improve the hit-rate of beneficial variants in such mutagenesis libraries, we develop methods to select optimal positions and corresponding sets of the mutations that will be used, in all combinations, in constructing a library for experimental evaluation. Our approach, OCoM (Optimization of Combinatorial Mutagenesis), encompasses both degenerate oligonucleotides and specified point mutations, and can be directed accordingly by requirements of experimental cost and library size. It evaluates the quality of the resulting library by one- and two-body sequence potentials, averaged over the variants. To ensure that it is not simply recapitulating extant sequences, it balances the quality of a library with an explicit evaluation of the novelty of its members. We show that, despite dealing with a combinatorial set of variants, in our approach the resulting library optimization problem is actually isomorphic to single-variant optimization. By the same token, this means that the two-body sequence potential results in an NP-hard optimization problem. We present an efficient dynamic programming algorithm for the one-body case and a practically-efficient integer programming approach for the general two-body case. We demonstrate the effectiveness of our approach in designing libraries for three different case study proteins targeted by previous combinatorial libraries--a green fluorescent protein, a cytochrome P450, and a beta lactamase. We found that OCoM worked quite efficiently in practice, requiring only 1 hour even for the massive design problem of selecting 18 mutations to generate 10⁷ variants of a 443-residue P450. We demonstrate the general ability of OCoM in enabling the protein engineer to explore and evaluate trade-offs between quality and novelty as well as library construction technique, and identify optimal libraries for experimental evaluation.