Designing Multivalent and Multispecific Biologics.

In the era of precision medicine, multivalent and multispecific therapeutics present a promising approach for targeted disease intervention. These therapeutics are designed to interact with multiple targets simultaneously, promising enhanced efficacy, reduced side effects, and resilience against drug resistance. We dissect the principles guiding the design of multivalent biologics, highlighting challenges and strategies that must be considered to maximize therapeutic effect. Engineerable elements in multivalent and multispecific biologic design-domain affinities, valency, and spatial presentation-must be considered in the context of the molecular targets as well as the balance of important properties such as target avidity and specificity. We illuminate recent applications of these principles in designing protein and cell therapies and identify exciting future directions in this field, underscored by advances in biomolecular and cellular engineering and computational approaches. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering , Volume 15 is June 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Pulsed stimuli entrain p53 to synchronize single cells and modulate cell-fate determination.

Entrainment to an external stimulus enables a synchronized oscillatory response across a population of cells, increasing coherent responses by reducing cell-to-cell heterogeneity. It is unclear whether the property of entrainability extends to systems where responses are intrinsic to the individual cell, rather than dependent on coherence across a population of cells. Using a combination of mathematical modeling, time-lapse fluorescence microscopy, and single-cell tracking, we demonstrated that p53 oscillations triggered by DNA double-strand breaks (DSBs) can be entrained with a periodic damage stimulus, despite such synchrony not known to function in effective DNA damage responses. Surprisingly, p53 oscillations were experimentally entrained over a wider range of DSB frequencies than predicted by an established computational model for the system. We determined that recapitulating the increased range of entrainment frequencies required, non-intuitively, a less robust oscillator and wider steady-state valley on the energy landscape. Further, we show that p53 entrainment can lead to altered expression dynamics of downstream targets responsible for cell fate in a manner dependent on target mRNA stability. Overall, this study demonstrates that entrainment can occur in a biological oscillator despite the apparent lack of an evolutionary advantage conferred through synchronized responses and highlights the potential of externally entraining p53 dynamics to reduce cellular variability and synchronize cell-fate responses for therapeutic outcomes.

Inertial effect of cell state velocity on the quiescence-proliferation fate decision in breast cancer.

Energy landscapes can provide intuitive depictions of population heterogeneity and dynamics. However, it is unclear whether individual cell behavior, hypothesized to be determined by initial position and noise, is faithfully recapitulated. Using the p21-/Cdk2-dependent quiescence-proliferation decision in breast cancer dormancy as a testbed, we examined single-cell dynamics on the landscape when perturbed by hypoxia, a dormancy-inducing stress. Combining trajectory-based energy landscape generation with single-cell time-lapse microscopy, we found that initial position on a p21/Cdk2 landscape did not fully explain the observed cell-fate heterogeneity under hypoxia. Instead, cells with higher cell state velocities prior to hypoxia, influenced by epigenetic parameters, tended to remain proliferative under hypoxia. Thus, the fate decision on this landscape is significantly influenced by "inertia", a velocity-dependent ability to resist directional changes despite reshaping of the underlying landscape, superseding positional effects. Such inertial effects may markedly influence cell-fate trajectories in tumors and other dynamically changing microenvironments.

Facile Display of Homomultivalent Proteins for In Vitro Selections.

Low-affinity protein binders are emerging as valuable domains for therapeutic applications because of their higher specificity when presented in multivalent ligands that increase the overall strength and selectivity of receptor binding. De novo discovery of low-affinity binders would be enhanced by the large library sizes attainable with in vitro selection systems, but these platforms generally maximize recovery of high-affinity monovalent binders. Here, we present a facile technology that uses rolling circle amplification to create homomultivalent libraries. We show proof of principle of this approach in ribosome display with off-rate selections of a bivalent ligand against monovalent and bivalent targets, thereby demonstrating high enrichment (up to 166-fold) against a low-affinity target that is bivalent but not monovalent. This approach to homomultivalent library construction can be applied to any binder tolerant of N- and C-terminal fusions and provides a platform for performing in vitro display selections with controlled protein valency and orientation.

MVsim is a toolset for quantifying and designing multivalent interactions.

Arising through multiple binding elements, multivalency can specify the avidity, duration, cooperativity, and selectivity of biomolecular interactions, but quantitative prediction and design of these properties has remained challenging. Here we present MVsim, an application suite built around a configurational network model of multivalency to facilitate the quantification, design, and mechanistic evaluation of multivalent binding phenomena through a simple graphical user interface. To demonstrate the utility and versatility of MVsim, we first show that both monospecific and multispecific multivalent ligand-receptor interactions, with their noncanonical binding kinetics, can be accurately simulated. Further, to illustrate the conceptual insights into multivalent systems that MVsim can provide, we apply it to quantitatively predict the ultrasensitivity and performance of multivalent-encoded protein logic gates, evaluate the inherent programmability of multispecificity for selective receptor targeting, and extract rate constants of conformational switching for the SARS-CoV-2 spike protein and model its binding to ACE2 as well as multivalent inhibitors of this interaction. MVsim and instructional tutorials are freely available at https://sarkarlab.github.io/MVsim/ .

Model-guided engineering of DNA sequences with predictable site-specific recombination rates.

Site-specific recombination (SSR) is an important tool in synthetic biology, but its applications are limited by the inability to predictably tune SSR reaction rates. Facile rate manipulation could be achieved by modifying the DNA substrate sequence; however, this approach lacks rational design principles. Here, we develop an integrated experimental and computational method to engineer the DNA attachment sequence attP for predictably modulating the inversion reaction mediated by the recombinase Bxb1. After developing a qPCR method to measure SSR reaction rate, we design, select, and sequence attP libraries to inform a machine-learning model that computes Bxb1 inversion rate as a function of attP sequence. We use this model to predict reaction rates of attP variants in vitro and demonstrate their utility in gene circuit design in Escherichia coli. Our high-throughput, model-guided approach for rationally tuning SSR reaction rates enhances our understanding of recombinase function and expands the synthetic biology toolbox.

A cell-free approach to identify binding hotspots in plant immune receptors.

Plant immune receptors are often difficult to express heterologously, hindering study of direct interactions between these receptors and their targets with traditional biochemical approaches. The cell-free method ribosome display (RD) enables expression of such recalcitrant proteins by keeping each nascent polypeptide chain tethered to its ribosome, which can enhance protein folding by virtue of its size and solubility. Moreover, in contrast to an in planta readout of receptor activity such as a hypersensitive response that conflates binding and signaling, RD enables direct probing of the interaction between plant immune receptors and their targets. Here, we demonstrate the utility of this approach using tomato recognition of Trichoderma viride ethylene-inducing xylanase (EIX) as a case study. Leveraging the modular nature of the tomato LeEIX2 and LeEIX1 leucine-rich repeat (LRR) receptors, we applied an entropy-informed algorithm to maximize the information content in our receptor segmentation RD experiments to identify segments implicated in EIX binding. Unexpectedly, two distinct EIX-binding hotspots were discovered on LeEIX2 and both hotspots are shared with decoy LeEIX1, suggesting that their contrasting receptor functions are not due to differential modes of ligand binding. Given that most plant immune receptors are thought to engage targets via their LRR sequences, this approach should be of broad utility in rapidly identifying their binding hotspots.

Engineering a Minimal Leucine-rich Repeat IgG-binding Module.

Sea lamprey immunization can yield leucine-rich repeat (LRR) protein binders analogous to globular antibodies developed from mammals. A novel minimal LRR was discovered through lamprey immunization with human immunoglobulin G Fc domain (IgG Fc). Initial attempts to solubly express this LRR protein, VLRB.IgGFc, in Escherichia coli proved challenging, so it was analyzed using the cell-free method ribosome display. In ribosome display, VLRB.IgGFc was found to bind specifically to the Fc domain of IgG, with little observed cross-reactivity to IgA or IgM. The minimal repeat protein architecture of VLRB.IgGFc may facilitate modular LRR extensions to incorporate additional or augmented functionality within a continuous, structurally defined scaffold. We exploited this modularity to design a chimera of a well-characterized, soluble LRR repebody and the initially insoluble VLRB.IgGFc to produce soluble Repe-VLRB.IgGFc. The minimal IgG Fc-binding module, Repe-VLRB.IgGFc, and future-engineered variants thereof should be useful additions to the biotechnological toolbox for detecting, purifying, or targeting IgGs. More generally, this two-step approach of minimal LRR binder discovery via sea lamprey immunization followed by modular augmentation of functionality may be of general utility in protein engineering.

MVsim : a toolset for quantifying and designing multivalent interactions.

Arising through multiple binding elements, multivalency can specify the avidity, duration, cooperativity, and selectivity of biomolecular interactions, but quantitative prediction and design of these properties has remained challenging. Here we present MVsim , an application suite built around a configurational network model of multivalency to facilitate the quantification, design, and mechanistic evaluation of multivalent binding phenomena through a simple graphical user interface. To demonstrate the utility and versatility of MVsim , we first show that both monospecific and multispecific multivalent ligand-receptor interactions, with their noncanonical binding kinetics, can be accurately simulated. We then quantitatively predict the ultrasensitivity and performance of multivalent-encoded protein logic gates, evaluate the inherent programmability of multispecificity for selective receptor targeting, and extract rate constants of conformational switching for the SARS-CoV-2 spike protein and model its binding to ACE2 as well as multivalent inhibitors of this interaction. MVsim is freely available at https://sarkarlab.github.io/MVsim/ .

Trajectory-based energy landscapes of gene regulatory networks.

Multistability and natural biological variability can result in significant heterogeneity within a cell population, leading to challenges in understanding and modulating cell behavior. Energy landscapes can offer qualitatively intuitive visualizations of cell phenotype and facilitate a more quantitative understanding of cellular dynamics, but current methods for landscape generation are mathematically involved and often require specific system properties (e.g., ergodicity or independent gene/protein probability distributions) that do not always hold. Here, we present a simple kinetic Monte Carlo-based method for landscape generation from a system of ordinary differential equations using only simulation trajectories initialized throughout the phase space of interest. The resulting landscape produces three quantitative features relevant to understanding cell behavior: stability (reflected by the depth or potential of landscape valleys), velocity (representing average directional movement on the landscape), and variance in velocity (indicative of landscape positions with heterogeneous movements). We applied this method to a genetic toggle switch, a core decision-making network in binary cellular responses, to elucidate effects of biologically relevant intrinsic and extrinsic cues. Intrinsic noise, such as stochasticity in transcription-translation and differences in cell cycle position, manifests through changes in valley width and position, reflecting increased population heterogeneity and more probabilistic cell fate transitions. The landscapes also capture the effect of an external inducer, revealing a quantitative correlation between the rate of cell fate transition and the energy barrier above a threshold inducer concentration determined by the permissivity of the valley. Further, in tracking dynamically changing landscapes under time-varying external cues, we unexpectedly found that an oscillatory inducer input can modulate cell fate heterogeneity and lead to periodic cell fate transitions entrained to the input frequency, depending on the intrinsic degradation rate of the switch. The landscape generation approach outlined herein is generalizable to other network topologies and may provide new quantitative insights into their dynamics.

Mechanisms of noncanonical binding dynamics in multivalent protein-protein interactions.

Protein multivalency can provide increased affinity and specificity relative to monovalent counterparts, but these emergent biochemical properties and their mechanistic underpinnings are difficult to predict as a function of the biophysical properties of the multivalent binding partners. Here, we present a mathematical model that accurately simulates binding kinetics and equilibria of multivalent protein-protein interactions as a function of the kinetics of monomer-monomer binding, the structure and topology of the multidomain interacting partners, and the valency of each partner. These properties are all experimentally or computationally estimated a priori, including approximating topology with a worm-like chain model applicable to a variety of structurally disparate systems, thus making the model predictive without parameter fitting. We conceptualize multivalent binding as a protein-protein interaction network: ligand and receptor valencies determine the number of interacting species in the network, with monomer kinetics and structural properties dictating the dynamics of each species. As predicted by the model and validated by surface plasmon resonance experiments, multivalent interactions can generate several noncanonical macroscopic binding dynamics, including a transient burst of high-energy configurations during association, biphasic equilibria resulting from interligand competition at high concentrations, and multiexponential dissociation arising from differential lifetimes of distinct network species. The transient burst was only uncovered when extending our analysis to trivalent interactions due to the significantly larger network, and we were able to predictably tune burst magnitude by altering linker rigidity. This study elucidates mechanisms of multivalent binding and establishes a framework for model-guided analysis and engineering of such interactions.

Variable cellular decision-making behavior in a constant synthetic network topology.

BACKGROUND: Modules of interacting components arranged in specific network topologies have evolved to perform a diverse array of cellular functions. For a network with a constant topological structure, its function within a cell may still be tuned by changing the number of instances of a particular component (e.g., gene copy number) or by modulating the intrinsic biochemical properties of a component (e.g., binding strength or catalytic efficiency). How such perturbations affect cellular response dynamics remains poorly understood. Here, we explored these effects in a common decision-making motif, cross-antagonism with autoregulation, by synthetically constructing this network in yeast. RESULTS: We employed the engineering design strategy of reuse to build this topology with a single protein building block, TetR, creating necessary components through TetR mutations and fusion partners. We then studied the impact of several topology-preserving perturbations - strength of cross-antagonism, number of operator sites in a promoter, and gene dosage - on decision-making behavior. We found that reducing TetR repression strength, which hinders cross-antagonism, resulted in a loss of mutually exclusive cell responses. Unexpectedly, increasing the number of operator sites also impeded decision-making exclusivity, which may be a consequence of the averaging effect that arises when multiple transcriptional activators and repressors are accommodated at a given locus. Stochastic simulations of this topology revealed that, even for networks with high TetR repression strength and a low number of operator sites, increasing gene dosage can reduce exclusivity in response dynamics. We further demonstrated this result experimentally by quantifying gene copy numbers in selected yeast clones with differing phenotypic responses. CONCLUSIONS: Our study illustrates how parameters that do not change the topological structure of a decision-making network can nonetheless exert significant influence on its response dynamics. These findings should further inform the study of native motifs, including the effects of topology-preserving mutations, and the robust engineering of synthetic networks.

Towards a Quantitative Understanding of Cell Identity.

Cells have traditionally been characterized using expression levels of a few proteins that are thought to specify phenotype. This requires a priori selection of proteins, which can introduce descriptor bias, and neglects the wealth of additional molecular information nested within each cell in a population, which often makes these sparse descriptors qualitative. Recently, more unbiased and quantitative cell characterization has been made possible by new high-throughput, information-dense experimental approaches and data-driven computational methods. This review discusses such quantitative descriptors in the context of three central concepts of cell identity: definition, creation, and stability. Collectively, these concepts are essential for constructing quantitative phenotypic landscapes, which will enhance our understanding of cell biology and facilitate cell engineering for research and clinical applications.

RNA Thermometers for the PURExpress System.

Cell-free synthetic biology approaches enable engineering of biomolecular systems exhibiting complex, cell-like behaviors in the absence of living entities. Often essential to these systems are user-controllable mechanisms to regulate gene expression. Here we describe synthetic RNA thermometers that enable temperature-dependent translation in the PURExpress in vitro protein synthesis system. Previously described cellular thermometers lie wholly in the 5' untranslated region and do not retain their intended function in PURExpress. By contrast, we designed hairpins between the Shine-Dalgarno sequence and complementary sequences within the gene of interest. The resulting thermometers enable high-yield, cell-free protein expression in an inducible temperature range compatible with in vitro translation systems (30-37 °C). Moreover, expression efficiency and switching behavior are tunable via small variations to the coding sequence. Our approach and resulting thermometers provide new tools for exploiting temperature as a rapid, external trigger for in vitro gene regulation.

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.

Robust hematopoietic progenitor cell commitment in the presence of a conflicting cue.

Hematopoietic lineage commitment is regulated by cytokines and master transcription factors, but it remains unclear how a progenitor cell chooses a lineage in the face of conflicting cues. Through transcript counting in megakaryocyte-erythroid progenitors undergoing erythropoiesis, we show that the expression levels of the pro-erythropoiesis transcription factor EKLF (also known as KLF1) and receptor EpoR are inversely correlated with their pro-megakaryopoiesis counterparts, FLI-1 and TpoR (also known as MPL). Notably, as progenitors commit to the erythrocyte lineage, EpoR is upregulated and TpoR is strongly downregulated, thus boosting the potency of the pro-erythropoiesis cue erythropoietin and effectively eliminating the activity of the pro-megakaryopoiesis cue thrombopoietin. Based on these findings, we propose a new model for exclusive decision making that explicitly incorporates signals from extrinsic cues, and we experimentally confirm a model prediction of temporal changes in transcript noise levels in committing progenitors. Our study suggests that lineage-specific receptor levels can modulate potencies of cues to achieve robust commitment decisions.

Conceptual and methodological advances in cell-free directed evolution.

Although cell-free directed evolution methods have been used to engineer proteins for nearly two decades, selections on more complex phenotypes have largely remained in the domain of cell-based engineering approaches. Here, we review recent conceptual advances that now enable in vitro display of multimeric proteins, integral membrane proteins, and proteins with an expanded amino acid repertoire. Additionally, we discuss methodological improvements that have enhanced the accessibility, efficiency, and robustness of cell-free approaches. Coupling these advances with the in vitro advantages of creating exceptionally large libraries and precisely controlling all experimental conditions, cell-free directed evolution is poised to contribute significantly to our understanding and engineering of more complex protein phenotypes.

Computationally guided design of robust gene circuits.

The inability to rationally design and construct circuits that robustly enable complex behaviors is perhaps the most fundamental challenge in synthetic biology. While systems modeling can aid this process and help reduce the space of design strategies, the unavailability and dynamic variability of kinetic parameters limits the utility of such models. Here, we present a general approach that employs an exhaustive enumeration of network architectures to suggest topologies that robustly enable a desired behavior.

Engineering Escherichia coli for light-activated cytolysis of mammalian cells.

By delivering payloads in response to specific exogenous stimuli, smart bacterial therapeutics have the potential to overcome many limitations of conventional therapies, including poor targeting specificity and dosage control in current cancer treatments. Although not yet explored as a trigger for bacterial drug delivery, light is an ideal induction mechanism because it offers fine spatiotemporal control and is easily and safely administered. Using recent advances in optogenetics, we have engineered two strains of Escherichia coli to secrete a potent mammalian cytotoxin in response to blue or red light. The tools in this study demonstrate the initial feasibility of light-activated bacterial therapeutics for applications such as tumor cytolysis, and their modular nature should enable simple substitution of other payloads of interest.