Identification of Highly Cross-Reactive Mimotopes for a Public T Cell Response in Murine Melanoma.

While immune checkpoint blockade results in durable responses for some patients, many others have not experienced such benefits. These treatments rely upon reinvigorating specific T cell-antigen interactions. However, it is often unknown what antigens are being recognized by T cells or how to potently induce antigen-specific responses in a broadly applicable manner. Here, we characterized the CD8+ T cell response to a murine model of melanoma following combination immunotherapy to determine the basis of tumor recognition. Sequencing of tumor-infiltrating T cells revealed a repertoire of highly homologous TCR sequences that were particularly expanded in treated mice and which recognized an antigen from an endogenous retrovirus. While vaccination against this peptide failed to raise a protective T cell response in vivo, engineered antigen mimotopes induced a significant expansion of CD8+ T cells cross-reactive to the original antigen. Vaccination with mimotopes resulted in killing of antigen-loaded cells in vivo yet showed modest survival benefit in a prophylactic vaccine paradigm. Together, this work demonstrates the identification of a dominant tumor-associated antigen and generation of mimotopes which can induce robust functional T cell responses that are cross-reactive to the endogenous antigen across multiple individuals.

Screening for CD19-specific chimaeric antigen receptors with enhanced signalling via a barcoded library of intracellular domains.

The immunostimulatory intracellular domains (ICDs) of chimaeric antigen receptors (CARs) are essential for converting antigen recognition into antitumoural function. Although there are many possible combinations of ICDs, almost all current CARs rely on combinations of CD3𝛇, CD28 and 4-1BB. Here we show that a barcoded library of 700,000 unique CD19-specific CARs with diverse ICDs cloned into lentiviral vectors and transduced into Jurkat T cells can be screened at high throughput via cell sorting and next-generation sequencing to optimize CAR signalling for antitumoural functions. By using this screening approach, we identified CARs with new ICD combinations that, compared with clinically available CARs, endowed human primary T cells with comparable tumour control in mice and with improved proliferation, persistence, exhaustion and cytotoxicity after tumour rechallenge in vitro. The screening strategy can be adapted to other disease models, cell types and selection conditions, and could be used to improve adoptive cell therapies and to expand their utility to new disease indications.

Proteome-Scale Screening to Identify High-Expression Signal Peptides with Minimal N-Terminus Biases via Yeast Display.

Signal peptides are critical for the efficient expression and routing of extracellular and secreted proteins. Most protein production and screening technologies rely upon a relatively small set of signal peptides. Despite their central role in biotechnology, there are limited studies comprehensively examining the interplay between signal peptides and expressed protein sequences. Here, we describe a high-throughput method to screen novel signal peptides that maintain a high degree of surface expression across a range of protein scaffolds with highly variable N-termini. We find that the canonical signal peptide used in yeast surface display, derived from Aga2p, fails to achieve high surface expression for 42.5% of constructs containing diverse N-termini. To circumvent this, we have identified two novel signal peptides derived from endogenous yeast proteins, SRL1 and KISH, which are highly tolerant to diverse N-terminal sequences. This pipeline can be used to expand our understanding of signal peptide function, identify improved signal peptides for protein expression, and refine the computational tools used for signal peptide prediction.

Yeast Display for the Identification of Peptide-MHC Ligands of Immune Receptors.

T cells detect peptide antigens presented by major histocompatibility complex (MHC) proteins via their T cell receptor (TCR). The sequence diversity of possible antigens, with trillions of potential peptide-MHC targets, makes it challenging to study, characterize, and manipulate the peptide repertoire of a given TCR. Yeast display has been utilized to study the interactions between peptide-MHCs and T cell receptors to facilitate high-throughput screening of peptide-MHC libraries. Here we present insights on designing and validating a peptide-MHC yeast display construct, designing and constructing peptide libraries, conducting selections, and preparing, processing, and analyzing peptide library sequencing data. Applications for this approach are broad, including characterizing peptide-MHC recognition profiles for a TCR, screening for high-affinity mimotopes of known TCR-binding peptides, and identifying natural ligands of TCRs from expanded T cells.

Vaccination reshapes the virus-specific T cell repertoire in unexposed adults.

We examined how baseline CD4+ T cell repertoire and precursor states impact responses to pathogen infection in humans using primary immunization with yellow fever virus (YFV) vaccine. YFV-specific T cells in unexposed individuals were identified by peptide-MHC tetramer staining and tracked pre- and post-vaccination by tetramers and TCR sequencing. A substantial number of YFV-reactive T cells expressed memory phenotype markers and contained expanded clones in the absence of exposure to YFV. After vaccination, pre-existing YFV-specific T cell populations with low clonal diversity underwent limited expansion, but rare populations with a reservoir of unexpanded TCRs generated robust responses. These altered dynamics reorganized the immunodominance hierarchy and resulted in an overall increase in higher avidity T cells. Thus, instead of further increasing the representation of dominant clones, YFV vaccination recruits rare and more responsive T cells. Our findings illustrate the impact of vaccines in prioritizing T cell responses and reveal repertoire reorganization as a key component of effective vaccination.

Biophysical Characterization Platform Informs Protein Scaffold Evolvability.

Evolving specific molecular recognition function of proteins requires strategic navigation of a complex mutational landscape. Protein scaffolds aid evolution via a conserved platform on which a modular paratope can be evolved to alter binding specificity. Although numerous protein scaffolds have been discovered, the underlying properties that permit binding evolution remain unknown. We present an algorithm to predict a protein scaffold's ability to evolve novel binding function based upon computationally calculated biophysical parameters. The ability of 17 small proteins to evolve binding functionality across seven discovery campaigns was determined via magnetic activated cell sorting of 1010 yeast-displayed protein variants. Twenty topological and biophysical properties were calculated for 787 small protein scaffolds and reduced into independent components. Regularization deduced which extracted features best predicted binding functionality, providing a 4/6 true positive rate, a 9/11 negative predictive value, and a 4/6 positive predictive value. Model analysis suggests a large, disconnected paratope will permit evolved binding function. Previous protein engineering endeavors have suggested that starting with a highly developable (high producibility, stability, solubility) protein will offer greater mutational tolerance. Our results support this connection between developability and evolvability by demonstrating a relationship between protein production in the soluble fraction of Escherichia coli and the ability to evolve binding function upon mutation. We further explain the necessity for initial developability by observing a decrease in proteolytic stability of protein mutants that possess binding functionality over nonfunctional mutants. Future iterations of protein scaffold discovery and evolution will benefit from a combination of computational prediction and knowledge of initial developability properties.

A Bayesian framework for high-throughput T cell receptor pairing.

MOTIVATION: The study of T cell receptor (TCR) repertoires has generated new insights into immune system recognition. However, the ability to robustly characterize these populations has been limited by technical barriers and an inability to reliably infer heterodimeric chain pairings for TCRs. RESULTS: Here, we describe a novel analytical approach to an emerging immune repertoire sequencing method, improving the resolving power of this low-cost technology. This method relies upon the distribution of a T cell population across a 96-well plate, followed by barcoding and sequencing of the relevant transcripts from each T cell. Multicell Analytical Deconvolution for High Yield Paired-chain Evaluation (MAD-HYPE) uses Bayesian inference to more accurately extract TCR information, improving our ability to study and characterize T cell populations for immunology and immunotherapy applications. AVAILABILITY AND IMPLEMENTATION: The MAD-HYPE algorithm is released as an open-source project under the Apache License and is available from https://github.com/birnbaumlab/MAD-HYPE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Gradient of Sitewise Diversity Promotes Evolutionary Fitness for Binder Discovery in a Three-Helix Bundle Protein Scaffold.

Engineered proteins provide clinically and industrially impactful molecules and utility within fundamental research, yet inefficiencies in discovering lead variants with new desired functionality, while maintaining stability, hinder progress. Improved function, which can result from a few strategic mutations, is fundamentally separate from discovering novel function, which often requires large leaps in sequence space. While a highly diverse combinatorial library covering immense sequence space would empower protein discovery, the ability to sample only a minor subset of sequence space and the typical destabilization of random mutations preclude this strategy. A balance must be reached. At library scale, compounding several destabilizing mutations renders many variants unable to properly fold and devoid of function. Broadly searching sequence space while reducing the level of destabilization may enhance evolution. We exemplify this balance with affibody, a three-helix bundle protein scaffold. Using natural ligand data sets, stability and structural computations, and deep sequencing of thousands of binding variants, a protein library was designed on a sitewise basis with a gradient of mutational levels across 29% of the protein. In direct competition of biased and uniform libraries, both with 1 × 109 variants, for discovery of 6 × 104 ligands (5 × 103 clusters) toward seven targets, biased amino acid frequency increased ligand discovery 13 ± 3-fold. Evolutionarily favorable amino acids, both globally and site-specifically, are further elucidated. The sitewise amino acid bias aids evolutionary discovery by reducing the level of mutant destabilization as evidenced by a midpoint of denaturation (62 ± 4 °C) 15 °C higher than that of unbiased mutants (47 ± 11 °C; p < 0.001). Sitewise diversification, identified by high-throughput evolution and rational library design, improves discovery efficiency.

PyMOL360: Multi-user gamepad control of molecular visualization software.

Interactive visualization of biomolecular structure is a powerful tool for scientists, students, and audiences from the laboratory to the classroom and beyond. However, existing platforms lack the ability to generate a collaborative environment to engage multiple users simultaneously. To address this need, PyMOL360 is described, which enables control of PyMOL molecular visualization software from multiple gamepad controllers. Extensive modularity enables user-defined configurations while default settings ease adoption by new users. A walkthrough of the software demonstrates its ability to select relevant molecules and modify viewing perspectives, molecular styles, colors, highlighted residues, or chains. Provision of source code encourages use and further development. © 2016 Wiley Periodicals, Inc.

ScaffoldSeq: Software for characterization of directed evolution populations.

ScaffoldSeq is software designed for the numerous applications-including directed evolution analysis-in which a user generates a population of DNA sequences encoding for partially diverse proteins with related functions and would like to characterize the single site and pairwise amino acid frequencies across the population. A common scenario for enzyme maturation, antibody screening, and alternative scaffold engineering involves naïve and evolved populations that contain diversified regions, varying in both sequence and length, within a conserved framework. Analyzing the diversified regions of such populations is facilitated by high-throughput sequencing platforms; however, length variability within these regions (e.g., antibody CDRs) encumbers the alignment process. To overcome this challenge, the ScaffoldSeq algorithm takes advantage of conserved framework sequences to quickly identify diverse regions. Beyond this, unintended biases in sequence frequency are generated throughout the experimental workflow required to evolve and isolate clones of interest prior to DNA sequencing. ScaffoldSeq software uniquely handles this issue by providing tools to quantify and remove background sequences, cluster similar protein families, and dampen the impact of dominant clones. The software produces graphical and tabular summaries for each region of interest, allowing users to evaluate diversity in a site-specific manner as well as identify epistatic pairwise interactions. The code and detailed information are freely available at http://research.cems.umn.edu/hackel. Proteins 2016; 84:869-874. © 2016 Wiley Periodicals, Inc.

Toward Bioremediation of Methylmercury Using Silica Encapsulated Escherichia coli Harboring the mer Operon.

Mercury is a highly toxic heavy metal and the ability of the neurotoxin methylmercury to biomagnify in the food chain is a serious concern for both public and environmental health globally. Because thousands of tons of mercury are released into the environment each year, remediation strategies are urgently needed and prompted this study. To facilitate remediation of both organic and inorganic forms of mercury, Escherichia coli was engineered to harbor a subset of genes (merRTPAB) from the mercury resistance operon. Protein products of the mer operon enable transport of mercury into the cell, cleavage of organic C-Hg bonds, and subsequent reduction of ionic mercury to the less toxic elemental form, Hg(0). E. coli containing merRTPAB was then encapsulated in silica beads resulting in a biological-based filtration material. Performing encapsulation in aerated mineral oil yielded silica beads that were smooth, spherical, and similar in diameter. Following encapsulation, E. coli containing merRTPAB retained the ability to degrade methylmercury and performed similarly to non-encapsulated cells. Due to the versatility of both the engineered mercury resistant strain and silica bead technology, this study provides a strong foundation for use of the resulting biological-based filtration material for methylmercury remediation.

High-Throughput Ligand Discovery Reveals a Sitewise Gradient of Diversity in Broadly Evolved Hydrophilic Fibronectin Domains.

Discovering new binding function via a combinatorial library in small protein scaffolds requires balance between appropriate mutations to introduce favorable intermolecular interactions while maintaining intramolecular integrity. Sitewise constraints exist in a non-spatial gradient from diverse to conserved in evolved antibody repertoires; yet non-antibody scaffolds generally do not implement this strategy in combinatorial libraries. Despite the fact that biased amino acid distributions, typically elevated in tyrosine, serine, and glycine, have gained wider use in synthetic scaffolds, these distributions are still predominantly applied uniformly to diversified sites. While select sites in fibronectin domains and DARPins have shown benefit from sitewise designs, they have not been deeply evaluated. Inspired by this disparity between diversity distributions in natural libraries and synthetic scaffold libraries, we hypothesized that binders resulting from discovery and evolution would exhibit a non-spatial, sitewise gradient of amino acid diversity. To identify sitewise diversities consistent with efficient evolution in the context of a hydrophilic fibronectin domain, >105 binders to six targets were evolved and sequenced. Evolutionarily favorable amino acid distributions at 25 sites reveal Shannon entropies (range: 0.3-3.9; median: 2.1; standard deviation: 1.1) supporting the diversity gradient hypothesis. Sitewise constraints in evolved sequences are consistent with complementarity, stability, and consensus biases. Implementation of sitewise constrained diversity enables direct selection of nanomolar affinity binders validating an efficient strategy to balance inter- and intra-molecular interaction demands at each site.