A 45-Amino-Acid Scaffold Mined from the PDB for High-Affinity Ligand Engineering

HOPS-Dependent Endosomal Escape Demands Protein Unfolding

The inefficient translocation of proteins across biological membranes limits their application as potential therapeutics and research tools. In many cases, the translocation of a protein involves two discrete steps: uptake into the endocytic pathway and endosomal escape. Certain charged or amphiphilic molecules can achieve high protein uptake, but few are capable of efficient endosomal escape. One exception to this rule is ZF5.3, a mini-protein that exploits elements of the natural endosomal maturation machinery to translocate across endosomal membranes. Although some ZF5.3-protein conjugates are delivered efficiently to the cytosol or nucleus, overall delivery efficiency varies widely for different cargoes with no obvious design rules. Here we show that delivery efficiency depends on the ability of the cargo to unfold. Using fluorescence correlation spectroscopy, a single-molecule technique that precisely measures intracytosolic protein concentration, we show that regardless of size and pI, low-Tm cargoes of ZF5.3 (including intrinsically disordered domains) bias endosomal escape toward a high-efficiency pathway that requires the homotypic fusion and protein sorting (HOPS) complex. Small protein domains are delivered with moderate efficiency through the same HOPS portal, even if the Tm is high. These findings imply a novel pathway out of endosomes that is exploited by ZF5.3 and provide clear guidance for the selection or design of optimally deliverable therapeutic cargo.

An engineering strategy to target activated EGFR with CAR T cells

Chimeric antigen receptor (CAR) T cells have shown remarkable response rates in hematological malignancies. In contrast, CAR T cell treatment of solid tumors is associated with several challenges, in particular the expression of most tumor-associated antigens at lower levels in vital organs, resulting in on-target/off-tumor toxicities. Thus, innovative approaches to improve the tumor specificity of CAR T cells are urgently needed. Based on the observation that many human solid tumors activate epidermal growth factor receptor (EGFR) on their surface through secretion of EGFR ligands, we developed an engineering strategy for CAR-binding domains specifically directed against the ligand-activated conformation of EGFR. We show, in several experimental systems, that the generated binding domains indeed enable CAR T cells to distinguish between active and inactive EGFR. We anticipate that this engineering concept will be an important step forward to improve the tumor specificity of CAR T cells directed against EGFR-positive solid cancers.

Extensive antibody search with whole spectrum black-box optimization

In designing functional biological sequences with machine learning, the activity predictor tends to be inaccurate due to shortage of data. Top ranked sequences are thus unlikely to contain effective ones. This paper proposes to take prediction stability into account to provide domain experts with a reasonable list of sequences to choose from. In our approach, multiple prediction models are trained by subsampling the training set and the multi-objective optimization problem, where one objective is the average activity and the other is the standard deviation, is solved. The Pareto front represents a list of sequences with the whole spectrum of activity and stability. Using this method, we designed VHH (Variable domain of Heavy chain of Heavy chain) antibodies based on the dataset obtained from deep mutational screening. To solve multi-objective optimization, we employed our sequence design software MOQA that uses quantum annealing. By applying several selection criteria to 19,778 designed sequences, five sequences were selected for wet-lab validation. One sequence, 16 mutations away from the closest training sequence, was successfully expressed and found to possess desired binding specificity. Our whole spectrum approach provides a balanced way of dealing with the prediction uncertainty, and can possibly be applied to extensive search of functional sequences.

Hyperstable Synthetic Mini-Proteins as Effective Ligand Scaffolds

Small, single-domain protein scaffolds are compelling sources of molecular binding ligands with the potential for efficient physiological transport, modularity, and manufacturing. Yet, mini-proteins require a balance between biophysical robustness and diversity to enable new functions. We tested the developability and evolvability of millions of variants of 43 designed libraries of synthetic 40-amino acid βαββ proteins with diversified sheet, loop, or helix paratopes. We discovered a scaffold library that yielded hundreds of binders to seven targets while exhibiting high stability and soluble expression. Binder discovery yielded 6-122 nM affinities without affinity maturation and Tms averaging ≥78 °C. Broader βαββ libraries exhibited varied developability and evolvability. Sheet paratopes were the most consistently developable, and framework 1 was the most evolvable. Paratope evolvability was dependent on target, though several libraries were evolvable across many targets while exhibiting high stability and soluble expression. Select βαββ proteins are strong starting points for engineering performant binders.

Design rules for efficient endosomal escape

The inefficient translocation of proteins across biological membranes limits their application as therapeutic compounds and research tools. In most cases, translocation involves two steps: uptake into the endocytic pathway and endosomal escape. Certain charged or amphiphilic molecules promote protein uptake but few enable efficient endosomal escape. One exception is ZF5.3, a mini-protein that exploits natural endosomal maturation machinery to translocate across endosomal membranes. Although certain ZF5.3-protein conjugates are delivered efficiently into the cytosol or nucleus, overall delivery efficiency varies widely with no obvious design rules. Here we evaluate the role of protein size and thermal stability in the ability to efficiently escape endosomes when attached to ZF5.3. Using fluorescence correlation spectroscopy, a single-molecule technique that provides a precise measure of intra-cytosolic protein concentration, we demonstrate that delivery efficiency depends on both size and the ease with which a protein unfolds. Regardless of size and pI, low-Tm cargos of ZF5.3 (including intrinsically disordered domains) bias its endosomal escape route toward a high-efficiency pathway that requires the homotypic fusion and protein sorting (HOPS) complex. Small protein domains are delivered with moderate efficiency through the same HOPS portal even if the Tm is high. These findings imply a novel protein- and/or lipid-dependent pathway out of endosomes that is exploited by ZF5.3 and provide clear guidance for the selection or design of optimally deliverable therapeutic cargo.

The smallest functional antibody fragment: Ultralong CDR H3 antibody knob regions potently neutralize SARS-CoV-2

Cows produce antibodies with a disulfide-bonded antigen-binding domain embedded within ultralong heavy chain third complementarity determining regions. This "knob" domain is analogous to natural cysteine-rich peptides such as knottins in that it is small and stable but can accommodate diverse loops and disulfide bonding patterns. We immunized cattle with SARS-CoV-2 spike and found ultralong CDR H3 antibodies that could neutralize several viral variants at picomolar IC50 potencies in vitro and could protect from disease in vivo. The independent CDR H3 peptide knobs were expressed and maintained the properties of the parent antibodies. The knob interaction with SARS-CoV-2 spike was revealed by electron microscopy, X-ray crystallography, NMR spectroscopy, and mass spectrometry and established ultralong CDR H3-derived knobs as the smallest known recombinant independent antigen-binding fragment. Unlike other vertebrate antibody fragments, these knobs are not reliant on the immunoglobulin domain and have potential as a new class of therapeutics.

Predicting and Interpreting Protein Developability Via Transfer of Convolutional Sequence Representation

Engineered proteins have emerged as novel diagnostics, therapeutics, and catalysts. Often, poor protein developability─quantified by expression, solubility, and stability─hinders utility. The ability to predict protein developability from amino acid sequence would reduce the experimental burden when selecting candidates. Recent advances in screening technologies enabled a high-throughput (HT) developability dataset for 105 of 1020 possible variants of protein ligand scaffold Gp2. In this work, we evaluate the ability of neural networks to learn a developability representation from a HT dataset and transfer this knowledge to predict recombinant expression beyond observed sequences. The model convolves learned amino acid properties to predict expression levels 44% closer to the experimental variance compared to a non-embedded control. Analysis of learned amino acid embeddings highlights the uniqueness of cysteine, the importance of hydrophobicity and charge, and the unimportance of aromaticity, when aiming to improve the developability of small proteins. We identify clusters of similar sequences with increased recombinant expression through nonlinear dimensionality reduction and we explore the inferred expression landscape via nested sampling. The analysis enables the first direct visualization of the fitness landscape and highlights the existence of evolutionary bottlenecks in sequence space giving rise to competing subpopulations of sequences with different developability. The work advances applied protein engineering efforts by predicting and interpreting protein scaffold expression from a limited dataset. Furthermore, our statistical mechanical treatment of the problem advances foundational efforts to characterize the structure of the protein fitness landscape and the amino acid characteristics that influence protein developability.

Titrating Avidity of Yeast-Displayed Proteins Using a Transcriptional Regulator

Yeast surface display is a valuable tool for protein engineering and directed evolution; however, significant variability in the copy number (i.e., avidity) of displayed variants on the yeast cell wall complicates screening and selection campaigns. Here, we report an engineered titratable display platform that modulates the avidity of Aga2-fusion proteins on the yeast cell wall dependent on the concentration of the anhydrotetracycline (aTc) inducer. Our design is based on a genomic Aga1 gene copy and an episomal Aga2-fusion construct both under the control of an aTc-dependent transcriptional regulator that enables stoichiometric and titratable expression, secretion, and display of Aga2-fusion proteins. We demonstrate tunable display levels over 2-3 orders of magnitude for various model proteins, including glucose oxidase enzyme variants, mechanostable dockerin-binding domains, and anti-PDL1 affibody domains. By regulating the copy number of displayed proteins, we demonstrate the effects of titratable avidity levels on several specific phenotypic activities, including enzyme activity and cell adhesion to surfaces under shear flow. Finally, we show that titrating down the display level allows yeast-based binding affinity measurements to be performed in a regime that avoids ligand depletion effects while maintaining small sample volumes, avoiding a well-known artifact in yeast-based binding assays. The ability to titrate the multivalency of proteins on the yeast cell wall through simple inducer control will benefit protein engineering and directed evolution methodology relying on yeast display for broad classes of therapeutic and diagnostic proteins of interest.

Yeast biopanning against site-specific phosphorylations in tau

The detection of site-specific phosphorylation in the microtubule-associated protein tau is emerging as a means to diagnose and monitor the progression of Alzheimer's Disease and other neurodegenerative diseases. However, there is a lack of phospho-specific monoclonal antibodies and limited validation of their binding specificity. Here, we report a novel approach using yeast biopanning against synthetic peptides containing site-specific phosphorylations. Using yeast cells displaying a previously validated phospho-tau (p-tau) single-chain variable region fragment (scFv), we show selective yeast cell binding based on single amino acid phosphorylation on the antigen. We identify conditions that allow phospho-specific biopanning using scFvs with a wide range of affinities (KD = 0.2 to 60 nM). Finally, we demonstrate the capability of screening large libraries by performing biopanning in 6-well plates. These results show that biopanning can effectively select yeast cells based on phospho-site specific antibody binding, opening doors for the facile identification of high-quality monoclonal antibodies.

Selection of target-binding proteins from the information of weakly enriched phage display libraries by deep sequencing and machine learning

Despite the advances in surface-display systems for directed evolution, variants with high affinity are not always enriched due to undesirable biases that increase target-unrelated variants during biopanning. Here, our goal was to design a library containing improved variants from the information of the "weakly enriched" library where functional variants were weakly enriched. Deep sequencing for the previous biopanning result, where no functional antibody mimetics were experimentally identified, revealed that weak enrichment was partly due to undesirable biases during phage infection and amplification steps. The clustering analysis of the deep sequencing data from appropriate steps revealed no distinct sequence patterns, but a Bayesian machine learning model trained with the selected deep sequencing data supplied nine clusters with distinct sequence patterns. Phage libraries were designed on the basis of the sequence patterns identified, and four improved variants with target-specific affinity (EC₅₀ = 80-277 nM) were identified by biopanning. The selection and use of deep sequencing data without undesirable bias enabled us to extract the information on prospective variants. In summary, the use of appropriate deep sequencing data and machine learning with the sequence data has the possibility of finding sequence space where functional variants are enriched.

Protein scaffolds: Antibody alternative for cancer diagnosis and therapy

Although antibodies are well developed and widely used in cancer therapy and diagnostic fields, some defects remain, such as poor tissue penetration, long in vivo metabolic retention, potential cytotoxicity, patent limitation, and high production cost. These issues have led scientists to explore and develop novel antibody alternatives. Protein scaffolds are small monomeric proteins with stable tertiary structures and mutable residues, which emerged in the 1990s. By combining robust gene engineering and phage display techniques, libraries with sufficient diversity could be established for target binding scaffold selection. Given the properties of small size, high affinity, and excellent specificity and stability, protein scaffolds have been applied in basic research, and preclinical and clinical fields over the past two decades. To date, more than 20 types of protein scaffolds have been developed, with the most frequently used being affibody, adnectin, ANTICALIN®, DARPins, and knottin. In this review, we focus on the protein scaffold applications in cancer therapy and diagnosis in the last 5 years, and discuss the pros and cons, and strategies of optimization and design.

Although antibodies are well developed and widely used in cancer therapy and diagnostic fields, some defects remain, such as poor tissue penetration, long in vivo metabolic retention, potential cytotoxicity, patent limitation, and high production cost.

Machine Learning-driven Protein Library Design: A Path Toward Smarter Libraries

Proteins are small yet valuable biomolecules that play a versatile role in therapeutics and diagnostics. The intricate sequence-structure-function paradigm in the realm of proteins opens the possibility for directly mapping amino acid sequence to function. However, the rugged nature of the protein fitness landscape and an astronomical number of possible mutations even for small proteins make navigating this system a daunting task. Moreover, the scarcity of functional proteins and the ease with which deleterious mutations are introduced, due to complex epistatic relationships, compound the existing challenges. This highlights the need for auxiliary tools in current techniques such as rational design and directed evolution. To that end, the state-of-the-art machine learning can offer time and cost efficiency in finding high fitness proteins, circumventing unnecessary wet-lab experiments. In the context of improving library design, machine learning provides valuable insights via its unique features such as high adaptation to complex systems, multi-tasking, and parallelism, and the ability to capture hidden trends in input data. Finally, both the advancements in computational resources and the rapidly increasing number of sequences in protein databases will allow more promising and detailed insights delivered from machine learning to protein library design. In this chapter, fundamental concepts and a method for machine learning-driven library design leveraging deep sequencing datasets will be discussed. We elaborate on (1) basic knowledge about machine learning algorithms, (2) the benefit of machine learning in library design, and (3) methodology for implementing machine learning in library design.

Yeast Surface Display for Protein Engineering: Library Generation, Screening, and Affinity Maturation

Yeast surface display is a powerful directed evolution method for developing and engineering protein molecules to attain desired properties. Here, updated protocols are presented for purposes of identification of lead binders and their affinity maturation. Large libraries are screened by magnetic bead selections followed by flow cytometric selections. Upon identification and characterization of single clones, their affinities are improved by an iterative process of mutagenesis and fluorescence-activated cell sorting.

Ancestral Sequence Reconstruction and Alternate Amino Acid States Guide Protein Library Design for Directed Evolution

Engineered proteins possess nearly limitless possibilities in medical and industrial applications but finding a precise amino acid sequence for these applications is challenging. A robust approach for discovering protein sequences with a desired functionality uses a library design method in which combinations of mutations are applied to a robust starting point. Determining useful mutations can be tortuous, yet rewarding; in this chapter, we present a novel library design method that uses information provided by ancestral sequence reconstruction (ASR) to create a library likely to have stable proteins with diverse function. ASR computational tools use a multi-sequence alignment of homologous proteins and an evolutionary model to estimate the protein sequences of the numerous common ancestors. For all ancestors, these tools calculate the probability of every amino acid occurring at each position within the sequence alignment. The alternate amino acid states at individual positions corelate to a region of stability in sequence space around the ancestral sequence which can inform site-wise diversification within a combinatorial library. The method presented in this chapter balances the quality of results, the computational resources needed, and ease of use.

Site-wise Diversification of Combinatorial Libraries Using Insights from Structure-guided Stability Calculations

Many auspicious clinical and industrial accomplishments have improved the human condition by means of protein engineering. Despite these achievements, our incomplete understanding of the sequence-structure-function relationship prevents rapid innovation. To tackle this problem, we must develop and integrate new and existing technologies. To date, directed evolution and rational design have dominated as protein engineering principles. Even so, prior to screening for novel or improved functions, a large collection of variants, within a protein library, exist along an ambiguous mutational terrain. Complicating things further, the choice of where to initialize investigation along a vast sequence space becomes even more difficult given that the majority of any sequence lacks function entirely. Unfortunately, even when considering functionally relevant positions, random substitutions can prove to be destabilizing, causing a hindrance to an otherwise function-inducing, stability-reliant folding process. To enhance productivity in the field, we seek to address this issue of destabilization, and subsequent disfunction, at protein-protein and protein-ligand interacting regions. Herein, the process of choosing amenable positions - and amino acids at those positions - allows for a refined, knowledge-based approach to combinatorial library design. Using structural data, we perform computational stability prediction with FoldX's PositionScan and Rosetta's ddG_monomer in tandem, allowing for the refinement of our thermodynamic stability data through the comparison of results. In turn, we provide a process for selecting in silico predicted mutually stabilizing positions and avoiding overly destabilizing ones that guides the site-wise diversification of combinatorial libraries.

A Protein-Engineered, Enhanced Yeast Display Platform for Rapid Evolution of Challenging Targets

Here, we enhanced the popular yeast display method by multiple rounds of DNA and protein engineering. We introduced surface exposure-tailored reporters, eUnaG2 and DnbALFA, creating a new platform of C and N terminal fusion vectors. The optimization of eUnaG2 resulted in five times brighter fluorescence and 10 °C increased thermostability than UnaG. The optimized DnbALFA has 10-fold the level of expression of the starting protein. Following this, different plasmids were developed to create a complex platform allowing a broad range of protein expression organizations and labeling strategies. Our platform showed up to five times better separation between nonexpressing and expressing cells compared with traditional pCTcon2 and c-myc labeling, allowing for fewer rounds of selection and achieving higher binding affinities. Testing 16 different proteins, the enhanced system showed consistently stronger expression signals over c-myc labeling. In addition to gains in simplicity, speed, and cost-effectiveness, new applications were introduced to monitor protein surface exposure and protein retention in the secretion pathway that enabled successful protein engineering of hard-to-express proteins. As an example, we show how we optimized the WD40 domain of the ATG16L1 protein for yeast surface and soluble bacterial expression, starting from a nonexpressing protein. As a second example, we show how using the here-presented enhanced yeast display method we rapidly selected high-affinity binders toward two protein targets, demonstrating the simplicity of generating new protein-protein interactions. While the methodological changes are incremental, it results in a qualitative enhancement in the applicability of yeast display for many applications.

De novo developed protein binders mimicking Interferon lambda signaling

We hereby describe the process of design and selection of nonantibody protein binders mimicking cytokine signaling. We chose to mimic signaling of IFN-λ1, type 3 interferon (also known as IL-29) for its novelty and the importance of its biological functions. All four known interferons λ signal through binding to the extracellular domains of IL-28 receptor 1 (IL-28R1) and IL-10 receptor 2 (IL-10R2). Our binders were therefore trained to bind both receptors simultaneously. The bifunctional binder molecules were developed by yeast display, a method of directed evolution. The signaling capacity of the bivalent binders was tested by measuring phosphorylation of the JAK/STAT signaling pathway and production of mRNA of six selected genes naturally induced by IFN- λ1 in human cell lines. The newly developed bivalent binders offer opportunities to study cytokine-related biological functions and modulation of the cell behavior by receptor activation on the cell surfaces alternative to the use of natural IFN-λ.

A Platform for Deep Sequence-Activity Mapping and Engineering Antimicrobial Peptides

Developing potent antimicrobials, and platforms for their study and engineering, is critical as antibiotic resistance grows. A high-throughput method to quantify antimicrobial peptide and protein (AMP) activity across a broad continuum would be powerful to elucidate sequence-activity landscapes and identify potent mutants. Yet the complexity of antimicrobial activity has largely constrained the scope and mechanistic bandwidth of AMP variant analysis. We developed a platform to efficiently perform sequence-activity mapping of AMPs via depletion (SAMP-Dep): a bacterial host culture is transformed with an AMP mutant library, induced to intracellularly express AMPs, grown under selective pressure, and deep sequenced to quantify mutant depletion. The slope of mutant growth rate versus induction level indicates potency. Using SAMP-Dep, we mapped the sequence-activity landscape of 170 000 mutants of oncocin, a proline-rich AMP, for intracellular activity against Escherichia coli. Clonal validation supported the platform's sensitivity and accuracy. The mapped landscape revealed an extended oncocin pharmacophore contrary to earlier structural studies, clarified the C-terminus role in internalization, identified functional epistasis, and guided focused, successful synthetic peptide library design, yielding a mutant with 2-fold enhancement in both intracellular and extracellular activity. The efficiency of SAMP-Dep poises the platform to transform AMP engineering, characterization, and discovery.

An engineered CD81-based combinatorial library for selecting recombinant binders to cell surface proteins: Laminin binding CD81 enhances cellular uptake of extracellular vesicles

The research of extracellular vesicles (EVs) has boomed in the last decade, with the promise of them functioning as target-directed drug delivery vehicles, able to modulate proliferation, migration, differentiation, and other properties of the recipient cell that are vital for health of the host organism. To enhance the ability of their targeted delivery, we employed an intrinsically overrepresented protein, CD81, to serve for recognition of the desired target antigen. Yeast libraries displaying mutant variants of the large extracellular loop of CD81 have been selected for binders to human placental laminin as an example target. Their specific interaction with laminin was confirmed in a mammalian display system. Derived sequences were reformatted to full-length CD81 and expressed in EVs produced by HeLa cells. These EVs were examined for the presence of the recombinant protein and were shown to exhibit an enhanced uptake into laminin-secreting mammalian cell lines. For the best candidate, the specificity of antigen interaction was demonstrated with a competition experiment. To our knowledge, this is the first example of harnessing an EV membrane protein as mediator of de novo target antigen recognition via in vitro molecular evolution, opening horizons to a broad range of applications in various therapeutic settings.

High-throughput developability assays enable library-scale identification of producible protein scaffold variants

Proteins require high developability-quantified by expression, solubility, and stability-for robust utility as therapeutics, diagnostics, and in other biotechnological applications. Measuring traditional developability metrics is low throughput in nature, often slowing the developmental pipeline. We evaluated the ability of 10 variations of three high-throughput developability assays to predict the bacterial recombinant expression of paratope variants of the protein scaffold Gp2. Enabled by a phenotype/genotype linkage, assay performance for 105 variants was calculated via deep sequencing of populations sorted by proxied developability. We identified the most informative assay combination via cross-validation accuracy and correlation feature selection and demonstrated the ability of machine learning models to exploit nonlinear mutual information to increase the assays' predictive utility. We trained a random forest model that predicts expression from assay performance that is 35% closer to the experimental variance and trains 80% more efficiently than a model predicting from sequence information alone. Utilizing the predicted expression, we performed a site-wise analysis and predicted mutations consistent with enhanced developability. The validated assays offer the ability to identify developable proteins at unprecedented scales, reducing the bottleneck of protein commercialization.

Chemical Diversification of Simple Synthetic Antibodies

Antibodies possess properties that make them valuable as therapeutics, diagnostics, and basic research tools. However, antibody chemical reactivity and covalent antigen binding are constrained, or even prevented, by the narrow range of chemistries encoded in canonical amino acids. In this work, we investigate strategies for leveraging an expanded range of chemical functionality using yeast displayed antibodies containing noncanonical amino acids (ncAAs) in or near antibody complementarity determining regions (CDRs). To enable systematic characterization of the effects of ncAA incorporation on antibody function, we first investigated whether diversification of a single antibody loop would support the isolation of binding clones against immunoglobulins from three species. We constructed and screened a billion-member library containing canonical amino acid diversity and loop length diversity only within the third complementarity determining region of the heavy chain (CDR-H3). Isolated clones exhibited moderate affinities (double- to triple-digit nanomolar affinities) and, in several cases, single-species specificity, confirming that antibody specificity can be mediated by a single CDR. This constrained diversity enabled the utilization of additional CDRs for the installation of chemically reactive and photo-cross-linkable ncAAs. Binding studies of ncAA-substituted antibodies revealed that ncAA incorporation is reasonably well tolerated, with observed changes in affinity occurring as a function of ncAA side chain identity, substitution site, and the ncAA incorporation machinery used. Multiple azide-containing ncAAs supported copper-catalyzed azide-alkyne cycloaddition (CuAAC) and strain-promoted azide-alkyne cycloaddition (SPAAC) without the abrogation of binding function. Similarly, several alkyne substitutions facilitated CuAAC without the apparent disruption of binding. Finally, antibodies substituted with a photo-cross-linkable ncAA were evaluated for ultraviolet-mediated cross-linking on the yeast surface. Competition-based assays revealed position-dependent covalent linkages, strongly suggesting successful cross-linking. Key findings regarding CuAAC reactions and photo-cross-linking on the yeast surface were confirmed using soluble forms of ncAA-substituted clones. The consistency of findings on the yeast surface and in solution suggest that chemical diversification can be incorporated into yeast display screening approaches. Taken together, our results highlight the power of integrating the use of yeast display and ncAAs in search of proteins with "chemically augmented" binding functions. This includes strategies for systematically introducing small molecule functionality within binding protein structures and evaluating protein-based covalent target binding. The efficient preparation and chemical diversification of antibodies on the yeast surface open up new possibilities for discovering "drug-like" protein leads in high throughput.

Protein Binder (ProBi) as a New Class of Structurally Robust Non-Antibody Protein Scaffold for Directed Evolution

Engineered small non-antibody protein scaffolds are a promising alternative to antibodies and are especially attractive for use in protein therapeutics and diagnostics. The advantages include smaller size and a more robust, single-domain structural framework with a defined binding surface amenable to mutation. This calls for a more systematic approach in designing new scaffolds suitable for use in one or more methods of directed evolution. We hereby describe a process based on an analysis of protein structures from the Protein Data Bank and their experimental examination. The candidate protein scaffolds were subjected to a thorough screening including computational evaluation of the mutability, and experimental determination of their expression yield in E. coli, solubility, and thermostability. In the next step, we examined several variants of the candidate scaffolds including their wild types and alanine mutants. We proved the applicability of this systematic procedure by selecting a monomeric single-domain human protein with a fold different from previously known scaffolds. The newly developed scaffold, called ProBi (Protein Binder), contains two independently mutable surface patches. We demonstrated its functionality by training it as a binder against human interleukin-10, a medically important cytokine. The procedure yielded scaffold-related variants with nanomolar affinity.

Magnetic Bead-Immobilized Mammalian Cells Are Effective Targets to Enrich Ligand-Displaying Yeast

Yeast surface display empowers selection of protein binding ligands, typically using recombinant soluble antigens. However, ectodomain fragments of transmembrane targets may fail to recapitulate their true, membrane-bound form. Direct selections against adhered mammalian cells empower enrichment of genuine binders yet benefit from high target expression, robustly adherent mammalian cells, and nanomolar affinity ligands. This study evaluates a modified format with mammalian cells immobilized to magnetic beads; yeast-displayed fibronectin domain and affibody ligands of known affinities and cells with expression ranges of epidermal growth factor receptor (EGFR) and CD276 elucidate important parameters to ligand enrichment and yield in cell suspension panning with comparison to adherent panning. Cell suspension panning is hindered by significant background of nondisplaying yeast but exhibits yield advantages in model EGFR systems for a high affinity (K_D = 2 nM) binder on cells with both high (10⁶ per cell) target expression (9.6 ± 0.6% vs 3.2 ± 0.4%, p < 0.0001) and mid (10⁵) target expression (2.3 ± 0.5% vs 0.41 ± 0.09%, p = 0.0008), as well as for a low affinity (K_D > 600 nM) binder on high target expression cells (2.0 ± 0.5% vs 0.017 ± 0.005%; p = 0.001). Significant enrichment was observed for all EGFR systems except the low-affinity, high expression system. The CD276 system failed to provide significant enrichment, indicating that this technique may not be suitable for all targets. Collectively, this study highlights new approaches that yield successful enrichment of yeast-displayed ligands via panning on immobilized mammalian cells.

Ligand Engineering via Yeast Surface Display and Adherent Cell Panning

High-throughput ligand discovery and evolution-via genotype-phenotype linkage strategies-empower molecularly targeted therapy, diagnostics, and fundamental science. Maintaining high-quality target antigen in these selections, particularly for membrane targets, is often a technical challenge. Panning yeast-displayed ligand libraries on intact mammalian cells expressing the molecular target has emerged as an effective strategy. Herein we describe the techniques used to select target-binding ligands via this approach including the use of target-negative cells to deplete non-specific binders and avidity reduction to preferentially select high-affinity ligands.

Programmable Assembly of Adeno-Associated Virus-Antibody Composites for Receptor-Mediated Gene Delivery

Adeno-associated virus (AAV) has emerged as a viral gene delivery vector that is safe in humans, able to infect both dividing and arrested cells and drive long-term expression (>6 months). Unfortunately, the naturally evolved properties of many AAV serotypes-including low cell type specificity and largely overlapping tropism-are mismatched to applications that require cell type-specific infection, such as neural circuit mapping or precision gene therapy. A variety of approaches to redirect AAV tropism exist, but there is still the need for a universal solution for directing AAV tropism toward user-defined cellular receptors that does not require extensive case-by-case optimization and works with readily available components. Here, we report AAV engineering approaches that enable programmable receptor-mediated gene delivery. First, we genetically encode small targeting scaffolds into a variable region of an AAV capsid and show that this redirects tropism toward the receptor recognized by these targeting scaffolds and also renders this AAV variant resistant to neutralizing antibodies present in nonhuman primate serum. We then simplify retargeting of tropism by engineering the same variable loop to encode a HUH tag, which forms a covalent bond to single-stranded DNA oligos conjugated to store-bought antibodies. We demonstrate that retargeting this HUH-AAVs toward different receptors is as simple as "arming" a premade noninfective AAV template with a different antibody in a conjugation process that uses widely available reagents and requires no optimization or extensive purification. Composite antibody-AAV nanoparticles structurally separate tropism and payload encapsulation, allowing each to be engineered independently.

Molecular evolution of peptides by yeast surface display technology

Genetically encoded peptides possess unique properties, such as a small molecular weight and ease of synthesis and modification, that make them suitable to a large variety of applications. However, despite these favorable qualities, naturally occurring peptides are often limited by intrinsic weak binding affinities, poor selectivity and low stability that ultimately restrain their final use. To overcome these limitations, a large variety of in vitro display methodologies have been developed over the past few decades to evolve genetically encoded peptide molecules with superior properties. Phage display, mRNA display, ribosome display, bacteria display, and yeast display are among the most commonly used methods to engineer peptides. While most of these in vitro methodologies have already been described in detail elsewhere, this review describes solely the yeast surface display technology and its valuable use for the evolution of a wide range of peptide formats.

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.

Cellular-Based Selections Aid Yeast-Display Discovery of Genuine Cell-Binding Ligands: Targeting Oncology Vascular Biomarker CD276

Yeast surface display is a proven tool for the selection and evolution of ligands with novel binding activity. Selections from yeast surface display libraries against transmembrane targets are generally carried out using recombinant soluble extracellular domains. Unfortunately, these molecules may not be good models of their true, membrane-bound form for a variety of reasons. Such selection campaigns often yield ligands that bind a recombinant target but not target-expressing cells or tissues. Advances in cell-based selections with yeast surface display may aid the frequency of evolving ligands that do bind true, membrane-bound antigens. This study aims to evaluate ligand selection strategies using both soluble target-driven and cellular selection techniques to determine which methods yield translatable ligands most efficiently and generate novel binders against CD276 (B7-H3) and Thy1, two promising tumor vasculature targets. Out of four ligand selection campaigns carried out using only soluble extracellular domains, only an affibody library sorted against CD276 yielded translatable binders. In contrast, fibronectin domains against CD276 and affibodies against CD276 were discovered in campaigns that either combined soluble target and cellular selection methods or used cellular selection methods alone. A high frequency of non target-specific ligands discovered from the use of cellular selection methods alone motivated the development of a depletion scheme using disadhered, antigen-negative mammalian cells as a blocking agent. Affinity maturation of CD276-binding affibodies by error-prone PCR and helix walking resulted in strong, specific cellular CD276 affinity ( Kd = 0.9 ± 0.6 nM). Collectively, these results motivate the use of cellular selections in tandem with recombinant selections and introduce promising affibody molecules specific to CD276 for further applications.

Engineering an EGFR-binding Gp2 domain for increased hydrophilicity

The Gp2 domain is a 45 amino-acid scaffold that has been evolved for specific, high-affinity binding towards multiple targets and was proven useful in molecular imaging and biological antagonism. It was hypothesized that Gp2 may benefit from increased hydrophilicity for improved physiological distribution as well as for physicochemical robustness. We identified seven exposed hydrophobic sites for hydrophilic mutations and experimentally evaluated single mutants, which yielded six mutations that do not substantially hinder expression, binding affinity or specificity (to epidermal growth factor receptor), and thermal stability. Eight combinations of these mutations improved hydrophilicity relative to the parental Gp2 clone as assessed by reverse-phase high-performance liquid chromatography (p < 0.05). Secondary structures and refolding abilities of the selected single mutants and all multimutants were unchanged relative to the parental ligand. A variant with five hydrophobic-to-hydrophilic mutations was identified with enhanced solubility as well as reasonable binding affinity ( K d  = 53-63 nM), recombinant yield (1.3 ± 0.8 mg/L), and thermal stability ( T m  = 53 ± 3°C). An alternative variant with a cluster of three leucine-to-hydrophilic mutations was identified with increased solubility, nominally increased binding affinity ( K d  = 13-28 nM) and reasonable thermal stability ( T m  = 54.0 ± 0.6°C) but reduced yield (0.4 ± 0.3 mg/L). In addition, a ≥7°C increase in the midpoint of thermal denaturation was observed in one of the single mutants (T21N). These mutants highlight the physicochemical tradeoffs associated with hydrophobic-to-hydrophilic mutation within a small protein, improve the solubility and hydrophilicity of an existent molecular imaging probe, and provide a more hydrophilic starting point for discovery of new Gp2 ligands towards additional targets.

Discovering Selective Binders for Photoswitchable Proteins Using Phage Display

Nature provides an array of proteins that change conformation in response to light. The discovery of a complementary array of proteins that bind only the light-state or dark-state conformation of their photoactive partner proteins would allow each light-switchable protein to be used as an optogenetic tool to control protein-protein interactions. However, as many photoactive proteins have no known binding partner, the advantages of optogenetic control-precise spatial and temporal resolution-are currently restricted to a few well-defined natural systems. In addition, the affinities and kinetics of native interactions are often suboptimal and are difficult to engineer in the absence of any structural information. We report a phage display strategy using a small scaffold protein that can be used to discover new binding partners for both light and dark states of a given light-switchable protein. We used our approach to generate binding partners that interact specifically with the light state or the dark state conformation of two light-switchable proteins: PYP, a test case for a protein with no known partners, and AsLOV2, a well-characterized protein. We show that these novel light-switchable protein-protein interactions can function in living cells to control subcellular localization processes.

Constrained Combinatorial Libraries of Gp2 Proteins Enhance Discovery of PD-L1 Binders

Engineered protein ligands are used for molecular therapy, diagnostics, and industrial biotechnology. The Gp2 domain is a 45-amino acid scaffold that has been evolved for specific, high-affinity binding to multiple targets by diversification of two solvent-exposed loops. Inspired by sitewise enrichment of select amino acids, including cysteine pairs, in earlier Gp2 discovery campaigns, we hypothesized that the breadth and efficiency of de novo Gp2 discovery will be aided by sitewise amino acid constraint within combinatorial library design. We systematically constructed eight libraries and comparatively evaluated their efficacy for binder discovery via yeast display against a panel of targets. Conservation of a cysteine pair at the termini of the first diversified paratope loop increased binder discovery 16-fold ( p < 0.001). Yet two other libraries with conserved cysteine pairs, within the second loop or an interloop pair, did not aid discovery thereby indicating site-specific impact. Via a yeast display protease resistance assay, Gp2 variants from the loop one cysteine pair library were 3.3 ± 2.1-fold ( p = 0.005) more stable than nonconstrained variants. Sitewise constraint of noncysteine residues-guided by previously evolved binders, natural Gp2 homology, computed stability, and structural analysis-did not aid discovery. A panel of binders to programmed death ligand 1 (PD-L1), a key target in cancer immunotherapy, were discovered from the loop 1 cysteine constraint library. Affinity maturation via loop walking resulted in strong, specific cellular PD-L1 affinity ( K_d = 6-9 nM).

Engineered Charge Redistribution of Gp2 Proteins through Guided Diversity for Improved PET Imaging of Epidermal Growth Factor Receptor

The Gp2 domain is a protein scaffold for synthetic ligand engineering. However, the native protein function results in a heterogeneous distribution of charge on the conserved surface, which may hinder further development and utility. We aim to modulate charge, without diminishing function, which is challenging in small proteins where each mutation is a significant fraction of protein structure. We constructed rationally guided combinatorial libraries with charge-neutralizing or charge-flipping mutations and sorted them, via yeast display and flow cytometry, for stability and target binding. Deep sequencing of functional variants revealed effective mutations both in clone-dependent contexts and broadly across binders to epidermal growth factor receptor (EGFR), insulin receptor, and immunoglobulin G. Functional mutants averaged 4.3 charge neutralizing mutations per domain while maintaining net negative charge. We evolved an EGFR-targeted Gp2 mutant that reduced charge density by 33%, maintained net charge, and improved charge distribution homogeneity while elevating thermal stability ( Tm = 87 ± 1 °C), improving binding specificity, and maintaining affinity ( Kd = 8.8 ± 0.6 nM). This molecule was conjugated with 1,4,7-triazacyclononane,1-glutaric acid-4,7-acetic acid for 64Cu chelation and evaluated for physiological distribution in mice with xenografted A431 (EGFRhigh) and MDA-MB-435 (EGFRlow) tumors. Excised tissue gamma counting and positron emission tomography/computed tomography imaging revealed good EGFRhigh tumor signal (4.7 ± 0.5%ID/g) at 2 h post-injection and molecular specificity evidenced by low uptake in EGFRlow tumors (0.6 ± 0.1%ID/g, significantly lower than for non-charge-modified Gp2, p = 0.01). These results provide charge mutations for an improved Gp2 framework, validate an effective approach to charge engineering, and advance performance of physiological EGFR targeting for molecular imaging.

Beyond antibody engineering: directed evolution of alternative binding scaffolds and enzymes using yeast surface display

Pioneered exactly 20 years ago, yeast surface display (YSD) continues to take a major role in protein engineering among the high-throughput display methodologies that have been developed to date. The classical yeast display technology relies on tethering an engineered protein to the cell wall by genetic fusion to one subunit of a dimeric yeast-mating agglutination receptor complex. This method enables an efficient genotype-phenotype linkage while exploiting the benefits of a eukaryotic expression machinery. Over the past two decades, a plethora of protein engineering efforts encompassing conventional antibody Fab and scFv fragments have been reported. In this review, we will focus on the versatility of YSD beyond conventional antibody engineering and, instead, place the focus on alternative scaffold proteins and enzymes which have successfully been tailored for purpose with regard to improving binding, activity or specificity.

Titratable Avidity Reduction Enhances Affinity Discrimination in Mammalian Cellular Selections of Yeast-Displayed Ligands

Yeast surface display selections against mammalian cell monolayers have proven effective in isolating proteins with novel binding activity. Recent advances in this technique allow for the recovery of clones with even micromolar binding affinities. However, no efficient method has been shown for affinity-based selection in this context. This study demonstrates the effectiveness of titratable avidity reduction using dithiothreitol to achieve this goal. A series of epidermal growth factor receptor binding fibronectin domains with a range of affinities are used to quantitatively identify the number of ligands per yeast cell that yield the strongest selectivity between strong, moderate, and weak affinities. Notably, reduction of ligand display to 3,000-6,000 ligands per yeast cell of a 2 nM binder yields 16-fold better selectivity than that to a 17 nM binder. These lessons are applied to affinity maturation of an EpCAM-binding fibronectin population, yielding an enriched pool of ligands with significantly stronger affinity than that of an analogous pool sorted by standard cellular selection methods. Collectively, this study offers a facile approach for affinity selection of yeast-displayed ligands against full-length cellular targets and demonstrates the effectiveness of this method by generating EpCAM-binding ligands that are promising for further applications.

Targeting Insulin Receptor in Breast Cancer Using Small Engineered Protein Scaffolds

Insulin receptor (InsR) and the type I insulin-like growth factor (IGF1R) are homologous receptors necessary for signal transduction by their cognate ligands insulin, IGF-I and IGF-II. IGF1R mAbs, intended to inhibit malignant phenotypic signaling, failed to show benefit in patients with endocrine-resistant tumors in phase III clinical trials. Our previous work showed that in tamoxifen-resistant cells, IGF1R expression was lacking, but InsR inhibition effectively blocked growth. In endocrine-sensitive breast cancer cells, insulin was not growth stimulatory, likely due to the presence of hybrid InsR/IGF1R, which has high affinity for IGF-I, but not insulin. Combination inhibition of InsR and IGF1R showed complete suppression of the system in endocrine-sensitive breast cancer cells. To develop InsR-binding agents, we employed a small protein scaffold, T7 phage gene 2 protein (Gp2) with the long-term goal of creating effective InsR inhibitors and diagnostics. Using yeast display and directed evolution, we identified three Gp2 variants (Gp2 #1, #5, and #10) with low nanomolar affinity and specific binding to cell surface InsR. These Gp2 variants inhibited insulin-mediated monolayer proliferation in both endocrine-sensitive and resistant breast cancer, but did not downregulate InsR expression. Gp2 #5 and Gp2 #10 disrupted InsR function by inhibiting ligand-induced receptor activation. In contrast, Gp2 #1 did not block InsR phosphorylation. Notably, Gp2 #1 binding was enhanced by pretreatment of cells with insulin, suggesting a unique receptor-ligand-binding mode. These Gp2 variants are the first nonimmunoglobulin protein scaffolds to target insulin receptor and present compelling opportunity for modulation of InsR signaling. Mol Cancer Ther; 16(7); 1324-34. ©2017 AACR.

64Cu-Labeled Gp2 Domain for PET Imaging of Epidermal Growth Factor Receptor

This purpose of this study is to determine the efficacy of a 45-amino acid Gp2 domain, engineered to bind to epidermal growth factor receptor (EGFR), as a positron emission tomography (PET) probe of EGFR in a xenograft mouse model. The EGFR-targeted Gp2 (Gp2-EGFR) and a nonbinding control were site-specifically labeled with 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) chelator. Binding affinity was tested toward human EGFR and mouse EGFR. Biological activity on downstream EGFR signaling was examined in cell culture. DOTA-Gp2 molecules were labeled with ⁶⁴Cu and intravenously injected (0.6-2.3 MBq) into mice bearing EGFR^high (n = 7) and EGFR^low (n = 4) xenografted tumors. PET/computed tomography (CT) images were acquired at 45 min, 2 h, and 24 h. Dynamic PET (25 min) was also acquired. Tomography results were verified with gamma counting of resected tissues. Two-tailed t tests with unequal variances provided statistical comparison. DOTA-Gp2-EGFR bound strongly to human (K_D = 7 ± 5 nM) and murine (K_D = 29 ± 6 nM) EGFR, and nontargeted Gp2 had no detectable binding. Gp2-EGFR did not agonize EGFR nor antagonize EGF-EGFR. ⁶⁴Cu-Gp2-EGFR tracer effectively localized to EGFR^high tumors at 45 min (3.2 ± 0.5%ID/g). High specificity was observed with significantly lower uptake in EGFR^low tumors (0.9 ± 0.3%ID/g, p < 0.001), high tumor-to-background ratios (11 ± 6 tumor/muscle, p < 0.001). Nontargeted Gp2 tracer had low uptake in EGFR^high tumors (0.5 ± 0.3%ID/g, p < 0.001). Similar data was observed at 2 h, and tumor signal was retained at 24 h (2.9 ± 0.3%ID/g). An engineered Gp2 PET imaging probe exhibited low background and target-specific EGFR^high tumor uptake at 45 min, with tumor signal retained at 24 h postinjection, and compared favorably with published EGFR PET probes for alternative protein scaffolds. These beneficial in vivo characteristics, combined with thermal stability, efficient evolution, and small size of the Gp2 domain validate its use as a future class of molecular imaging agents.

Geometry and expression enhance enrichment of functional yeast-displayed ligands via cell panning

Yeast surface display has proven to be an effective tool in the discovery and evolution of ligands with new or improved binding activity. Selections for binding activity are generally carried out using immobilized or fluorescently labeled soluble domains of target molecules such as recombinant ectodomain fragments. While this method typically provides ligands with high affinity and specificity for the soluble molecular target, translation to binding true membrane-bound cellular target is commonly problematic. Direct selections against mammalian cell surfaces can be carried out either exclusively or in combination with soluble target-based selections to further direct towards ligands for genuine cellular target. Using a series of fibronectin domain, affibody, and Gp2 ligands and human cell lines expressing a range of their targets, epidermal growth factor receptor and carcinoembryonic antigen, this study quantitatively identifies the elements that dictate ligand enrichment and yield. Most notably, extended flexible linkers between ligand and yeast enhance enrichment ratios from 1.4 ± 0.8 to 62 ± 57 for a low-affinity (>600 nM) binder on cells with high target expression and from 14 ± 13 to 74 ± 25 for a high-affinity binder (2 nM) on cells with medium valency. Inversion of the yeast display fusion from C-terminal display to N-terminal display still enables enrichment albeit with 40-97% reduced efficacy. Collectively, this study further enlightens the conditions-while highlighting new approaches-that yield successful enrichment of yeast-displayed binding ligands via panning on mammalian cells. Biotechnol. Bioeng. 2016;113: 2328-2341. © 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.