Computational design of a modular protein sense-response system

Engineering modular and tunable single-molecule sensors by decoupling sensing from signal output

Biosensors play key roles in medical research and diagnostics. However, the development of biosensors for new biomolecular targets of interest often involves tedious optimization steps to ensure a high signal response at the analyte concentration of interest. Here we show a modular nanosensor platform that facilitates these steps by offering ways to decouple and independently tune the signal output as well as the response window. Our approach utilizes a dynamic DNA origami nanostructure to engineer a high optical signal response based on fluorescence resonance energy transfer. We demonstrate mechanisms to tune the sensor's response window, specificity and cooperativity as well as highlight the modularity of the proposed platform by extending it to different biomolecular targets including more complex sensing schemes. This versatile nanosensor platform offers a promising starting point for the rapid development of biosensors with tailored properties.

Efficient Generation of Protein Pockets with PocketGen

Designing protein-binding proteins is critical for drug discovery. However, the AI-based design of such proteins is challenging due to the complexity of ligand-protein interactions, the flexibility of ligand molecules and amino acid side chains, and sequence-structure dependencies. We introduce PocketGen, a deep generative model that simultaneously produces both the residue sequence and atomic structure of the protein regions where ligand interactions occur. PocketGen ensures consistency between sequence and structure by using a graph transformer for structural encoding and a sequence refinement module based on a protein language model. The bilevel graph transformer captures interactions at multiple scales, including atom, residue, and ligand levels. To enhance sequence refinement, PocketGen integrates a structural adapter into the protein language model, ensuring that structure-based predictions align with sequence-based predictions. PocketGen can generate high-fidelity protein pockets with superior binding affinity and structural validity. It operates ten times faster than physics-based methods and achieves a 95% success rate, defined as the percentage of generated pockets with higher binding affinity than reference pockets. Additionally, it attains an amino acid recovery rate exceeding 64%.

Generative β-hairpin design using a residue-based physicochemical property landscape

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the β-hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of β hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial β-hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.

Rationalizing Diverse Binding Mechanisms to the Same Protein Fold: Insights for Ligand Recognition and Biosensor Design

The engineering of novel protein-ligand binding interactions, particularly for complex drug-like molecules, is an unsolved problem, which could enable many practical applications of protein biosensors. In this work, we analyzed two engineered biosensors, derived from the plant hormone sensor PYR1, to recognize either the agrochemical mandipropamid or the synthetic cannabinoid WIN55,212-2. Using a combination of quantitative deep mutational scanning experiments and molecular dynamics simulations, we demonstrated that mutations at common positions can promote protein-ligand shape complementarity and revealed prominent differences in the electrostatic networks needed to complement diverse ligands. MD simulations indicate that both PYR1 protein-ligand complexes bind a single conformer of their target ligand that is close to the lowest free-energy conformer. Computational design using a fixed conformer and rigid body orientation led to new WIN55,212-2 sensors with nanomolar limits of detection. This work reveals mechanisms by which the versatile PYR1 biosensor scaffold can bind diverse ligands. This work also provides computational methods to sample realistic ligand conformers and rigid body alignments that simplify the computational design of biosensors for novel ligands of interest.

Engineering the next-generation synthetic cell factory driven by protein engineering

Synthetic cell factory offers substantial advantages in economically efficient production of biofuels, chemicals, and pharmaceutical compounds. However, to create a high-performance synthetic cell factory, precise regulation of cellular material and energy flux is essential. In this context, protein components including enzymes, transcription factor-based biosensors and transporters play pivotal roles. Protein engineering aims to create novel protein variants with desired properties by modifying or designing protein sequences. This review focuses on summarizing the latest advancements of protein engineering in optimizing various aspects of synthetic cell factory, including: enhancing enzyme activity to eliminate production bottlenecks, altering enzyme selectivity to steer metabolic pathways towards desired products, modifying enzyme promiscuity to explore innovative routes, and improving the efficiency of transporters. Furthermore, the utilization of protein engineering to modify protein-based biosensors accelerates evolutionary process and optimizes the regulation of metabolic pathways. The remaining challenges and future opportunities in this field are also discussed.

An integrative approach to protein sequence design through multiobjective optimization

With recent methodological advances in the field of computational protein design, in particular those based on deep learning, there is an increasing need for frameworks that allow for coherent, direct integration of different models and objective functions into the generative design process. Here we demonstrate how evolutionary multiobjective optimization techniques can be adapted to provide such an approach. With the established Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the optimization framework, we use AlphaFold2 and ProteinMPNN confidence metrics to define the objective space, and a mutation operator composed of ESM-1v and ProteinMPNN to rank and then redesign the least favorable positions. Using the two-state design problem of the foldswitching protein RfaH as an in-depth case study, and PapD and calmodulin as examples of higher-dimensional design problems, we show that the evolutionary multiobjective optimization approach leads to significant reduction in the bias and variance in RfaH native sequence recovery, compared to a direct application of ProteinMPNN. We suggest that this improvement is due to three factors: (i) the use of an informative mutation operator that accelerates the sequence space exploration, (ii) the parallel, iterative design process inherent to the genetic algorithm that improves upon the ProteinMPNN autoregressive sequence decoding scheme, and (iii) the explicit approximation of the Pareto front that leads to optimal design candidates representing diverse tradeoff conditions. We anticipate this approach to be readily adaptable to different models and broadly relevant for protein design tasks with complex specifications.

Molecular glues and induced proximity: An evolution of tools and discovery

Small molecule molecular glues can nucleate protein complexes and rewire interactomes. Molecular glues are widely used as probes for understanding functional proximity at a systems level, and the potential to instigate event-driven pharmacology has motivated their application as therapeutics. Despite advantages such as cell permeability and the potential for low off-target activity, glues are still rare when compared to canonical inhibitors in therapeutic development. Their often simple structure and specific ability to reshape protein-protein interactions pose several challenges for widespread, designer applications. Molecular glue discovery and design campaigns can find inspiration from the fields of synthetic biology and biophysics to mine chemical libraries for glue-like molecules.

Exploring plant-derived phytochrome chaperone proteins for light-switchable transcriptional regulation in mammals

Synthetic biology applications require finely tuned gene expression, often mediated by synthetic transcription factors (sTFs) compatible with the human genome and transcriptional regulation mechanisms. While various DNA-binding and activation domains have been developed for different applications, advanced artificially controllable sTFs with improved regulatory capabilities are required for increasingly sophisticated applications. Here, in mammalian cells and mice, we validate the transactivator function and homo-/heterodimerization activity of the plant-derived phytochrome chaperone proteins, FHY1 and FHL. Our results demonstrate that FHY1/FHL form a photosensing transcriptional regulation complex (PTRC) through interaction with the phytochrome, ΔPhyA, that can toggle between active and inactive states through exposure to red or far-red light, respectively. Exploiting this capability, we develop a light-switchable platform that allows for orthogonal, modular, and tunable control of gene transcription, and incorporate it into a PTRC-controlled CRISPRa system (PTRCdcas) to modulate endogenous gene expression. We then integrate the PTRC with small molecule- or blue light-inducible regulatory modules to construct a variety of highly tunable systems that allow rapid and reversible control of transcriptional regulation in vitro and in vivo. Validation and deployment of these plant-derived phytochrome chaperone proteins in a PTRC platform have produced a versatile, powerful tool for advanced research and biomedical engineering applications.

Rational evolution for altering the ligand preference of estrogen receptor alpha

Estrogen receptor α is commonly used in synthetic biology to control the activity of genome editing tools. The activating ligands, estrogens, however, interfere with various cellular processes, thereby limiting the applicability of this receptor. Altering its ligand preference to chemicals of choice solves this hurdle but requires adaptation of unspecified ligand-interacting residues. Here, we provide a solution by combining rational protein design with multi-site-directed mutagenesis and directed evolution of stably integrated variants in Saccharomyces cerevisiae. This method yielded an estrogen receptor variant, named TERRA, that lost its estrogen responsiveness and became activated by tamoxifen, an anti-estrogenic drug used for breast cancer treatment. This tamoxifen preference of TERRA was maintained in mammalian cells and mice, even when fused to Cre recombinase, expanding the mammalian synthetic biology toolbox. Not only is our platform transferable to engineer ligand preference of any steroid receptor, it can also profile drug-resistance landscapes for steroid receptor-targeted therapies.

Synthetic transcription factor engineering for cell and gene therapy

Synthetic transcription factors (synTFs) that control beneficial transgene expression are an important method to increase the safety and efficacy of cell and gene therapy. Reliance on synTF components from non-human sources has slowed progress in the field because of concerns about immunogenicity and inducer drug properties. Recent advances in human-derived DNA-binding domains (DBDs) and transcriptional activation domains (TADs) paired with novel control modules responsive to clinically approved small molecules have poised the synTF field to overcome these hurdles. Advances include controllers inducible by autonomous signaling inputs and more complex, multi-input synTF circuits. Demonstrations of advanced control strategies with human-derived transcription factor components in clinically relevant vectors and in vivo models will facilitate progression into the clinic.

Rationalizing diverse binding mechanisms to the same protein fold: insights for ligand recognition and biosensor design

The engineering of novel protein-ligand binding interactions, particularly for complex drug-like molecules, is an unsolved problem which could enable many practical applications of protein biosensors. In this work, we analyzed two engineer ed biosensors, derived from the plant hormone sensor PYR1, to recognize either the agrochemical mandipropamid or the synthetic cannabinoid WIN55,212-2. Using a combination of quantitative deep mutational scanning experiments and molecular dynamics simulations, we demonstrated that mutations at common positions can promote protein-ligand shape complementarity and revealed prominent differences in the electrostatic networks needed to complement diverse ligands. MD simulations indicate that both PYR1 protein-ligand complexes bind a single conformer of their target ligand that is close to the lowest free energy conformer. Computational design using a fixed conformer and rigid body orientation led to new WIN55,212-2 sensors with nanomolar limits of detection. This work reveals mechanisms by which the versatile PYR1 biosensor scaffold can bind diverse ligands. This work also provides computational methods to sample realistic ligand conformers and rigid body alignments that simplify the computational design of biosensors for novel ligands of interest.

Selective induction of programmed cell death using synthetic biology tools

Regulated cell death (RCD) controls the removal of dispensable, infected or malignant cells, and is thus essential for development, homeostasis and immunity of multicellular organisms. Over the last years different forms of RCD have been described (among them apoptosis, necroptosis, pyroptosis and ferroptosis), and the cellular signaling pathways that control their induction and execution have been characterized at the molecular level. It has also become apparent that different forms of RCD differ in their capacity to elicit inflammation or an immune response, and that RCD pathways show a remarkable plasticity. Biochemical and genetic studies revealed that inhibition of a given pathway often results in the activation of back-up cell death mechanisms, highlighting close interconnectivity based on shared signaling components and the assembly of multivalent signaling platforms that can initiate different forms of RCD. Due to this interconnectivity and the pleiotropic effects of 'classical' cell death inducers, it is challenging to study RCD pathways in isolation. This has led to the development of tools based on synthetic biology that allow the targeted induction of RCD using chemogenetic or optogenetic methods. Here we discuss recent advances in the development of such toolset, highlighting their advantages and limitations, and their application for the study of RCD in cells and animals.

An integrative approach to protein sequence design through multiobjective optimization

With recent methodological advances in the field of computational protein design, in particular those based on deep learning, there is an increasing need for frameworks that allow for coherent, direct integration of different models and objective functions into the generative design process. Here we demonstrate how evolutionary multiobjective optimization techniques can be adapted to provide such an approach. With the established Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the optimization framework, we use AlphaFold2 and ProteinMPNN confidence metrics to define the objective space, and a mutation operator composed of ESM-1v and ProteinMPNN to rank and then redesign the least favorable positions. Using the multistate design problem of the foldswitching protein RfaH as an in-depth case study, we show that the evolutionary multiobjective optimization approach leads to significant reduction in the bias and variance in RfaH native sequence recovery, compared to a direct application of ProteinMPNN. We suggest that this improvement is due to three factors: (i) the use of an informative mutation operator that accelerates the sequence space exploration, (ii) the parallel, iterative design process inherent to the genetic algorithm that improves upon the ProteinMPNN autoregressive sequence decoding scheme, and (iii) the explicit approximation of the Pareto front that leads to optimal design candidates representing diverse tradeoff conditions. We anticipate this approach to be readily adaptable to different models and broadly relevant for protein design tasks with complex specifications.

De novo protein design-From new structures to programmable functions

Methods from artificial intelligence (AI) trained on large datasets of sequences and structures can now "write" proteins with new shapes and molecular functions de novo, without starting from proteins found in nature. In this Perspective, I will discuss the state of the field of de novo protein design at the juncture of physics-based modeling approaches and AI. New protein folds and higher-order assemblies can be designed with considerable experimental success rates, and difficult problems requiring tunable control over protein conformations and precise shape complementarity for molecular recognition are coming into reach. Emerging approaches incorporate engineering principles-tunability, controllability, and modularity-into the design process from the beginning. Exciting frontiers lie in deconstructing cellular functions with de novo proteins and, conversely, constructing synthetic cellular signaling from the ground up. As methods improve, many more challenges are unsolved.

Programmable synthetic receptors: the next-generation of cell and gene therapies

Cell and gene therapies hold tremendous promise for treating a range of difficult-to-treat diseases. However, concerns over the safety and efficacy require to be further addressed in order to realize their full potential. Synthetic receptors, a synthetic biology tool that can precisely control the function of therapeutic cells and genetic modules, have been rapidly developed and applied as a powerful solution. Delicately designed and engineered, they can be applied to finetune the therapeutic activities, i.e., to regulate production of dosed, bioactive payloads by sensing and processing user-defined signals or biomarkers. This review provides an overview of diverse synthetic receptor systems being used to reprogram therapeutic cells and their wide applications in biomedical research. With a special focus on four synthetic receptor systems at the forefront, including chimeric antigen receptors (CARs) and synthetic Notch (synNotch) receptors, we address the generalized strategies to design, construct and improve synthetic receptors. Meanwhile, we also highlight the expanding landscape of therapeutic applications of the synthetic receptor systems as well as current challenges in their clinical translation.

Advances in ligand-specific biosensing for structurally similar molecules

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

A simeprevir-inducible molecular switch for the control of cell and gene therapies

Chemical inducer of dimerization (CID) modules can be used effectively as molecular switches to control biological processes, and thus there is significant interest within the synthetic biology community in identifying novel CID systems. To date, CID modules have been used primarily in engineering cells for in vitro applications. To broaden their utility to the clinical setting, including the potential to control cell and gene therapies, the identification of novel CID modules should consider factors such as the safety and pharmacokinetic profile of the small molecule inducer, and the orthogonality and immunogenicity of the protein components. Here we describe a CID module based on the orally available, approved, small molecule simeprevir and its target, the NS3/4A protease from hepatitis C virus. We demonstrate the utility of this CID module as a molecular switch to control biological processes such as gene expression and apoptosis in vitro, and show that the CID system can be used to rapidly induce apoptosis in tumor cells in a xenograft mouse model, leading to complete tumor regression.

Engineering Ca2+-Dependent DNA Polymerase Activity

Advancements in synthetic biology have provided new opportunities in biosensing, with applications ranging from genetic programming to diagnostics. Next generation biosensors aim to expand the number of accessible environments for measurements, increase the number of measurable phenomena, and improve the quality of the measurement. To this end, an emerging area in the field has been the integration of DNA as an information storage medium within biosensor outputs, leveraging nucleic acids to record the biosensor state over time. However, slow signal transduction steps, due to the time scales of transcription and translation, bottleneck many sensing-DNA recording approaches. DNA polymerases (DNAPs) have been proposed as a solution to the signal transduction problem by operating as both the sensor and responder, but there is presently a lack of DNAPs with functional sensitivity to many desirable target ligands. Here, we engineer components of the Pol δ replicative polymerase complex of Saccharomyces cerevisiae to sense and respond to Ca2+, a metal cofactor relevant to numerous biological phenomena. Through domain insertion and binding site grafting to Pol δ subunits, we demonstrate functional allosteric sensitivity to Ca2+. Together, this work provides an important foundation for future efforts in the development of DNAP-based biosensors.

Small-molecule binding and sensing with a designed protein family

Despite transformative advances in protein design with deep learning, the design of small-molecule-binding proteins and sensors for arbitrary ligands remains a grand challenge. Here we combine deep learning and physics-based methods to generate a family of proteins with diverse and designable pocket geometries, which we employ to computationally design binders for six chemically and structurally distinct small-molecule targets. Biophysical characterization of the designed binders revealed nanomolar to low micromolar binding affinities and atomic-level design accuracy. The bound ligands are exposed at one edge of the binding pocket, enabling the de novo design of chemically induced dimerization (CID) systems; we take advantage of this to create a biosensor with nanomolar sensitivity for cortisol. Our approach provides a general method to design proteins that bind and sense small molecules for a wide range of analytical, environmental, and biomedical applications.

Biosensors in microalgae: A roadmap for new opportunities in synthetic biology and biotechnology

Biosensors are powerful tools to investigate, phenotype, improve and prototype microbial strains, both in fundamental research and in industrial contexts. Genetic and biotechnological developments now allow the implementation of synthetic biology approaches to novel different classes of microbial hosts, for example photosynthetic microalgae, which offer unique opportunities. To date, biosensors have not yet been implemented in phototrophic eukaryotic microorganisms, leaving great potential for novel biological and technological advancements untapped. Here, starting from selected biosensor technologies that have successfully been implemented in heterotrophic organisms, we project and define a roadmap on how these could be applied to microalgae research. We highlight novel opportunities for the development of new biosensors, identify critical challenges, and finally provide a perspective on the impact of their eventual implementation to tackle research questions and bioengineering strategies. From studying metabolism at the single-cell level to genome-wide screen approaches, and assisted laboratory evolution experiments, biosensors will greatly impact the pace of progress in understanding and engineering microalgal metabolism. We envision how this could further advance the possibilities for unraveling their ecological role, evolutionary history and accelerate their domestication, to further drive them as resource-efficient production hosts.

Large-scale pathway reconstruction and colorimetric screening accelerate cellular metabolism engineering

The capability to manipulate and analyze hard-wired metabolic pathways sets the pace at which we can engineer cellular metabolism. Here, we present a framework to extensively rewrite the central metabolic pathway for malonyl-CoA biosynthesis in yeast and readily assess malonyl-CoA output based on pathway-scale DNA reconstruction in combination with colorimetric screening (Pracs). We applied Pracs to generate and test millions of enzyme variants by introducing genetic mutations into the whole set of genes encoding the malonyl-CoA biosynthetic pathway and identified hundreds of beneficial enzyme mutants with increased malonyl-CoA output. Furthermore, the synthetic pathways reconstructed by randomly integrating these beneficial enzyme variants generated vast phenotypic diversity, with some displaying higher production of malonyl-CoA as well as other metabolites, such as carotenoids and betaxanthin, thus demonstrating the generic utility of Pracs to efficiently orchestrate central metabolism to optimize the production of different chemicals in various metabolic pathways. Pracs will be broadly useful to advance our ability to understand and engineer cellular metabolism.

Rational design of small-molecule responsive protein switches

Small-molecule responsive protein switches are powerful tools for controlling cellular processes. These switches are designed to respond rapidly and specifically to their inducer. They have been used in numerous applications, including the regulation of gene expression, post-translational protein modification, and signal transduction. Typically, small-molecule responsive protein switches consist of two proteins that interact with each other in the presence or absence of a small molecule. Recent advances in computational protein design already contributed to the development of protein switches with an expanded range of small-molecule inducers and increasingly sophisticated switch mechanisms. Further progress in the engineering of small-molecule responsive switches is fueled by cutting-edge computational design approaches, which will enable more complex and precise control over cellular processes and advance synthetic biology applications in biotechnology and medicine. Here, we discuss recent milestones and how technological advances are impacting the development of chemical switches.

Design of cell-type-specific hyperstable IL-4 mimetics via modular de novo scaffolds

The interleukin-4 (IL-4) cytokine plays a critical role in modulating immune homeostasis. Although there is great interest in harnessing this cytokine as a therapeutic in natural or engineered formats, the clinical potential of native IL-4 is limited by its instability and pleiotropic actions. Here, we design IL-4 cytokine mimetics (denoted Neo-4) based on a de novo engineered IL-2 mimetic scaffold and demonstrate that these cytokines can recapitulate physiological functions of IL-4 in cellular and animal models. In contrast with natural IL-4, Neo-4 is hyperstable and signals exclusively through the type I IL-4 receptor complex, providing previously inaccessible insights into differential IL-4 signaling through type I versus type II receptors. Because of their hyperstability, our computationally designed mimetics can directly incorporate into sophisticated biomaterials that require heat processing, such as three-dimensional-printed scaffolds. Neo-4 should be broadly useful for interrogating IL-4 biology, and the design workflow will inform targeted cytokine therapeutic development.

Exploration of novel αβ-protein folds through de novo design

A fundamental question in protein evolution is whether nature has exhaustively sampled nearly all possible protein folds throughout evolution, or whether a large fraction of the possible folds remains unexplored. To address this question, we defined a set of rules for β-sheet topology to predict novel αβ-folds and carried out a systematic de novo protein design exploration of the novel αβ-folds predicted by the rules. The designs for all eight of the predicted novel αβ-folds with a four-stranded β-sheet, including a knot-forming one, folded into structures close to the design models. Further, the rules predicted more than 10,000 novel αβ-folds with five- to eight-stranded β-sheets; this number far exceeds the number of αβ-folds observed in nature so far. This result suggests that a vast number of αβ-folds are possible, but have not emerged or have become extinct due to evolutionary bias.

Enabling technology and core theory of synthetic biology

Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.

ZetaDesign: an end-to-end deep learning method for protein sequence design and side-chain packing

Computational protein design has been demonstrated to be the most powerful tool in the last few years among protein designing and repacking tasks. In practice, these two tasks are strongly related but often treated separately. Besides, state-of-the-art deep-learning-based methods cannot provide interpretability from an energy perspective, affecting the accuracy of the design. Here we propose a new systematic approach, including both a posterior probability and a joint probability parts, to solve the two essential questions once for all. This approach takes the physicochemical property of amino acids into consideration and uses the joint probability model to ensure the convergence between structure and amino acid type. Our results demonstrated that this method could generate feasible, high-confidence sequences with low-energy side conformations. The designed sequences can fold into target structures with high confidence and maintain relatively stable biochemical properties. The side chain conformation has a significantly lower energy landscape without delegating to a rotamer library or performing the expensive conformational searches. Overall, we propose an end-to-end method that combines the advantages of both deep learning and energy-based methods. The design results of this model demonstrate high efficiency, and precision, as well as a low energy state and good interpretability.

Computational design of dynamic receptor-peptide signaling complexes applied to chemotaxis

Engineering protein biosensors that sensitively respond to specific biomolecules by triggering precise cellular responses is a major goal of diagnostics and synthetic cell biology. Previous biosensor designs have largely relied on binding structurally well-defined molecules. In contrast, approaches that couple the sensing of flexible compounds to intended cellular responses would greatly expand potential biosensor applications. Here, to address these challenges, we develop a computational strategy for designing signaling complexes between conformationally dynamic proteins and peptides. To demonstrate the power of the approach, we create ultrasensitive chemotactic receptor-peptide pairs capable of eliciting potent signaling responses and strong chemotaxis in primary human T cells. Unlike traditional approaches that engineer static binding complexes, our dynamic structure design strategy optimizes contacts with multiple binding and allosteric sites accessible through dynamic conformational ensembles to achieve strongly enhanced signaling efficacy and potency. Our study suggests that a conformationally adaptable binding interface coupled to a robust allosteric transmission region is a key evolutionary determinant of peptidergic GPCR signaling systems. The approach lays a foundation for designing peptide-sensing receptors and signaling peptide ligands for basic and therapeutic applications.

Alleviating the unwanted effects of oxidative stress on Aβ clearance: a review of related concepts and strategies for the development of computational modelling

Treatment for Alzheimer's disease (AD) can be more effective in the early stages. Although we do not completely understand the aetiology of the early stages of AD, potential pathological factors (amyloid beta [Aβ] and tau) and other co-factors have been identified as causes of AD, which may indicate some of the mechanism at work in the early stages of AD. Today, one of the primary techniques used to help delay or prevent AD in the early stages involves alleviating the unwanted effects of oxidative stress on Aβ clearance. 4-Hydroxynonenal (HNE), a product of lipid peroxidation caused by oxidative stress, plays a key role in the adduction of the degrading proteases. This HNE employs a mechanism which decreases catalytic activity. This process ultimately impairs Aβ clearance. The degradation of HNE-modified proteins helps to alleviate the unwanted effects of oxidative stress. Having a clear understanding of the mechanisms associated with the degradation of the HNE-modified proteins is essential for the development of strategies and for alleviating the unwanted effects of oxidative stress. The strategies which could be employed to decrease the effects of oxidative stress include enhancing antioxidant activity, as well as the use of nanozymes and/or specific inhibitors. One area which shows promise in reducing oxidative stress is protein design. However, more research is needed to improve the effectiveness and accuracy of this technique. This paper discusses the interplay of potential pathological factors and AD. In particular, it focuses on the effect of oxidative stress on the expression of the Aβ-degrading proteases through adduction of the degrading proteases caused by HNE. The paper also elucidates other strategies that can be used to alleviate the unwanted effects of oxidative stress on Aβ clearance. To improve the effectiveness and accuracy of protein design, we explain the application of quantum mechanical/molecular mechanical approach.

A split ribozyme that links detection of a native RNA to orthogonal protein outputs

Individual RNA remains a challenging signal to synthetically transduce into different types of cellular information. Here, we describe Ribozyme-ENabled Detection of RNA (RENDR), a plug-and-play strategy that uses cellular transcripts to template the assembly of split ribozymes, triggering splicing reactions that generate orthogonal protein outputs. To identify split ribozymes that require templating for splicing, we use laboratory evolution to evaluate the activities of different split variants of the Tetrahymena thermophila ribozyme. The best design delivers a 93-fold dynamic range of splicing with RENDR controlling fluorescent protein production in response to an RNA input. We further resolve a thermodynamic model to guide RENDR design, show how input signals can be transduced into diverse outputs, demonstrate portability across different bacteria, and use RENDR to detect antibiotic-resistant bacteria. This work shows how transcriptional signals can be monitored in situ and converted into different types of biochemical information using RNA synthetic biology.

Differential sensing with arrays of de novo designed peptide assemblies

Differential sensing attempts to mimic the mammalian senses of smell and taste to identify analytes and complex mixtures. In place of hundreds of complex, membrane-bound G-protein coupled receptors, differential sensors employ arrays of small molecules. Here we show that arrays of computationally designed de novo peptides provide alternative synthetic receptors for differential sensing. We use self-assembling α-helical barrels (αHBs) with central channels that can be altered predictably to vary their sizes, shapes and chemistries. The channels accommodate environment-sensitive dyes that fluoresce upon binding. Challenging arrays of dye-loaded barrels with analytes causes differential fluorophore displacement. The resulting fluorimetric fingerprints are used to train machine-learning models that relate the patterns to the analytes. We show that this system discriminates between a range of biomolecules, drink, and diagnostically relevant biological samples. As αHBs are robust and chemically diverse, the system has potential to sense many analytes in various settings.

Chemically inducible split protein regulators for mammalian cells

Chemically inducible systems represent valuable synthetic biology tools that enable the external control of biological processes. However, their translation to therapeutic applications has been limited because of unfavorable ligand characteristics or the immunogenicity of xenogeneic protein domains. To address these issues, we present a strategy for engineering inducible split protein regulators (INSPIRE) in which ligand-binding proteins of human origin are split into two fragments that reassemble in the presence of a cognate physiological ligand or clinically approved drug. We show that the INSPIRE platform can be used for dynamic, orthogonal and multiplex control of gene expression in mammalian cells. Furthermore, we demonstrate the functionality of a glucocorticoid-responsive INSPIRE platform in vivo and apply it for perturbing an endogenous regulatory network. INSPIRE presents a generalizable approach toward designing small-molecule responsive systems that can be implemented for the construction of new sensors, regulatory networks and therapeutic applications.

Precision engineering of biological function with large-scale measurements and machine learning

As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor's sensitivity (EC50) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC50. Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.

Genetically encoded biosensors for microbial synthetic biology: From conceptual frameworks to practical applications

Genetically encoded biosensors are the vital components of synthetic biology and metabolic engineering, as they are regarded as powerful devices for the dynamic control of genotype metabolism and evolution/screening of desirable phenotypes. This review summarized the recent advances in the construction and applications of different genetically encoded biosensors, including fluorescent protein-based biosensors, nucleic acid-based biosensors, allosteric transcription factor-based biosensors and two-component system-based biosensors. First, the construction frameworks of these biosensors were outlined. Then, the recent progress of biosensor applications in creating versatile microbial cell factories for the bioproduction of high-value chemicals was summarized. Finally, the challenges and prospects for constructing robust and sophisticated biosensors were discussed. This review provided theoretical guidance for constructing genetically encoded biosensors to create desirable microbial cell factories for sustainable bioproduction.

In vivo protein-based biosensors: seeing metabolism in real time

Biological homeostasis is a dynamic and elastic equilibrium of countless interlinked biochemical reactions. A key goal of life sciences is to understand these dynamics; bioengineers seek to reconfigure such networks. Both goals require the ability to monitor the concentration of individual intracellular metabolites with sufficient spatiotemporal resolution. To achieve this, a range of protein or protein/DNA signalling circuits with optical readouts have been constructed. Protein biosensors can provide quantitative information at subsecond temporal and suborganelle spatial resolution. However, their construction is fraught with difficulties related to integrating the affinity- and selectivity-endowing components with the signal reporters. We argue that development of efficient approaches for construction of chemically induced dimerisation systems and reporter domains with large dynamic ranges will solve these problems.

Design and engineering of genetically encoded protein biosensors for small molecules

Genetically encoded protein biosensors controlled by small organic molecules are valuable tools for many biotechnology applications, including control of cellular decisions in living cells. Here, we review recent advances in protein biosensor design and engineering for binding to novel ligands. We categorize sensor architecture as either integrated or portable, where portable biosensors uncouple molecular recognition from signal transduction. Proposed advances to improve portable biosensor development include standardizing a limited set of protein scaffolds, and automating ligand-compatibility screening and ligand-protein-interface design.

Rapid biosensor development using plant hormone receptors as reprogrammable scaffolds

A general method to generate biosensors for user-defined molecules could provide detection tools for a wide range of biological applications. Here, we describe an approach for the rapid engineering of biosensors using PYR1 (Pyrabactin Resistance 1), a plant abscisic acid (ABA) receptor with a malleable ligand-binding pocket and a requirement for ligand-induced heterodimerization, which facilitates the construction of sense-response functions. We applied this platform to evolve 21 sensors with nanomolar to micromolar sensitivities for a range of small molecules, including structurally diverse natural and synthetic cannabinoids and several organophosphates. X-ray crystallography analysis revealed the mechanistic basis for new ligand recognition by an evolved cannabinoid receptor. We demonstrate that PYR1-derived receptors are readily ported to various ligand-responsive outputs, including enzyme-linked immunosorbent assay (ELISA)-like assays, luminescence by protein-fragment complementation and transcriptional circuits, all with picomolar to nanomolar sensitivity. PYR1 provides a scaffold for rapidly evolving new biosensors for diverse sense-response applications.

A Single-Component Luminescent Biosensor for the SARS-CoV-2 Spike Protein

Many existing protein detection strategies depend on highly functionalized antibody reagents. A simpler and easier to produce class of detection reagent is highly desirable. We designed a single-component, recombinant, luminescent biosensor that can be expressed in laboratory strains of Escherichia coli and Saccharomyces cerevisiae. This biosensor is deployed in multiple homogeneous and immobilized assay formats to detect recombinant SARS-CoV-2 spike antigen and cultured virus. The chemiluminescent signal generated facilitates detection by an unaugmented cell phone camera. Binding-activated tandem split-enzyme (BAT) biosensors may serve as a useful template for diagnostics and reagents that detect SARS-CoV-2 antigens and other proteins of interest.

The present and the future of protein biosensor engineering

Protein biosensors play increasingly important roles in cell and neurobiology and have the potential to revolutionise the way clinical and industrial analytics are performed. The gradual transition from multicomponent biosensors to fully integrated single chain allosteric biosensors has brought the field closer to commercial applications. We evaluate various approaches for converting constitutively active protein reporter domains into analyte operated switches. We discuss the paucity of the natural receptors that undergo conformational changes sufficiently large to control the activity of allosteric reporter domains. This problem can be overcome by constructing artificial versions of such receptors. The design path to such receptors involves the construction of Chemically Induced Dimerisation systems (CIDs) that can be configured to operate single and two-component biosensors.

A single-component luminescent biosensor for the SARS-CoV-2 spike protein

Many existing protein detection strategies depend on highly functionalized antibody reagents. A simpler and easier to produce class of detection reagent is highly desirable. We designed a single-component, recombinant, luminescent biosensor that can be expressed in laboratory strains of E. coli and S. cerevisiae . This biosensor is deployed in multiple homogenous and immobilized assay formats to detect recombinant SARS-CoV-2 spike antigen and cultured virus. The chemiluminescent signal generated facilitates detection by an un-augmented cell phone camera. B inding A ctivated T andem split-enzyme (BAT) biosensors may serve as a useful template for diagnostics and reagents that detect SARS-CoV-2 antigens and other proteins of interest.

Protein design via deep learning

Proteins with desired functions and properties are important in fields like nanotechnology and biomedicine. De novo protein design enables the production of previously unseen proteins from the ground up and is believed as a key point for handling real social challenges. Recent introduction of deep learning into design methods exhibits a transformative influence and is expected to represent a promising and exciting future direction. In this review, we retrospect the major aspects of current advances in deep-learning-based design procedures and illustrate their novelty in comparison with conventional knowledge-based approaches through noticeable cases. We not only describe deep learning developments in structure-based protein design and direct sequence design, but also highlight recent applications of deep reinforcement learning in protein design. The future perspectives on design goals, challenges and opportunities are also comprehensively discussed.

Advances in the Computational Design of Small-Molecule-Controlled Protein-Based Circuits for Synthetic Biology

Synthetic biology approaches living systems with an engineering perspective and promises to deliver solutions to global challenges in healthcare and sustainability. A critical component is the design of biomolecular circuits with programmable input-output behaviors. Such circuits typically rely on a sensor module that recognizes molecular inputs, which is coupled to a functional output via protein-level circuits or regulating the expression of a target gene. While gene expression outputs can be customized relatively easily by exchanging the target genes, sensing new inputs is a major limitation. There is a limited repertoire of sensors found in nature, and there are often difficulties with interfacing them with engineered circuits. Computational protein design could be a key enabling technology to address these challenges, as it allows for the engineering of modular and tunable sensors that can be tailored to the circuit's application. In this article, we review recent computational approaches to design protein-based sensors for small-molecule inputs with particular focus on those based on the widely used Rosetta software suite. Furthermore, we review mechanisms that have been harnessed to couple ligand inputs to functional outputs. Based on recent literature, we illustrate how the combination of protein design and synthetic biology enables new sensors for diverse applications ranging from biomedicine to metabolic engineering. We conclude with a perspective on how strategies to address frontiers in protein design and cellular circuit design may enable the next generation of sense-response networks, which may increasingly be assembled from de novo components to display diverse and engineerable input-output behaviors.

An In Silico Methodology That Facilitates Decision Making in the Engineering of Nanoscale Protein Materials

Under the need for new functional and biocompatible materials for biomedical applications, protein engineering allows the design of assemblable polypeptides, which, as convenient building blocks of supramolecular complexes, can be produced in recombinant cells by simple and scalable methodologies. However, the stability of such materials is often overlooked or disregarded, becoming a potential bottleneck in the development and viability of novel products. In this context, we propose a design strategy based on in silico tools to detect instability areas in protein materials and to facilitate the decision making in the rational mutagenesis aimed to increase their stability and solubility. As a case study, we demonstrate the potential of this methodology to improve the stability of a humanized scaffold protein (a domain of the human nidogen), with the ability to oligomerize into regular nanoparticles usable to deliver payload drugs to tumor cells. Several nidogen mutants suggested by the method showed important and measurable improvements in their structural stability while retaining the functionalities and production yields of the original protein. Then, we propose the procedure developed here as a cost-effective routine tool in the design and optimization of multimeric protein materials prior to any experimental testing.

Engineering protein activity into off-the-shelf DNA devices

DNA-based devices are straightforward to design by virtue of their predictable folding, but they lack complex biological activity such as catalysis. Conversely, protein-based devices offer a myriad of functions but are much more difficult to design due to their complex folding. This study combines DNA and protein engineering to generate an enzyme that is activated by a DNA sequence of choice. A single protein switch, engineered from nanoluciferase using the alternate-frame-folding mechanism and herein called nLuc-AFF, is paired with different DNA technologies to create a biosensor for specific nucleic acid sequences, sensors for serotonin and ATP, and a two-input logic gate. nLuc-AFF is a genetically encoded, ratiometric, blue/green-luminescent biosensor whose output can be quantified by a phone camera. nLuc-AFF retains ratiometric readout in 100% serum, making it suitable for analyzing crude samples in low-resource settings. This approach can be applied to other proteins and enzymes to convert them into DNA-activated switches.

Engineering ligand-specific biosensors for aromatic amino acids and neurochemicals

Microbial biosensors have diverse applications in metabolic engineering and medicine. Specific and accurate quantification of chemical concentrations allows for adaptive regulation of enzymatic pathways and temporally precise expression of diagnostic reporters. Although biosensors should differentiate structurally similar ligands with distinct biological functions, such specific sensors are rarely found in nature and challenging to create. Using E. coli Nissle 1917, a generally regarded as safe microbe, we characterized two biosensor systems that promiscuously recognize aromatic amino acids or neurochemicals. To improve the sensors' selectivity and sensitivity, we applied rational protein engineering by identifying and mutagenizing amino acid residues and successfully demonstrated the ligand-specific biosensors for phenylalanine, tyrosine, phenylethylamine, and tyramine. Additionally, our approach revealed insights into the uncharacterized structure of the FeaR regulator, including critical residues in ligand binding. These results lay the groundwork for developing kinetically adaptive microbes for diverse applications. A record of this paper's transparent peer review process is included in the supplemental information.

Accurate positioning of functional residues with robotics-inspired computational protein design

SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geometries, which remains a major challenge. Here, we develop computational methods that design and predict local protein geometries with greater accuracy than existing methods. Then, as a proof of concept, we leverage these methods to design new protein conformations in the enzyme ketosteroid isomerase that change the protein's preference for a key functional residue. Our computational methods are openly accessible and can be applied to the design of other intricate geometries customized for new user-defined protein functions.

Functional network motifs defined through integration of protein-protein and genetic interactions

Cells are enticingly complex systems. The identification of feedback regulation is critically important for understanding this complexity. Network motifs defined as small graphlets that occur more frequently than expected by chance have revolutionized our understanding of feedback circuits in cellular networks. However, with their definition solely based on statistical over-representation, network motifs often lack biological context, which limits their usefulness. Here, we define functional network motifs (FNMs) through the systematic integration of genetic interaction data that directly inform on functional relationships between genes and encoded proteins. Occurring two orders of magnitude less frequently than conventional network motifs, we found FNMs significantly enriched in genes known to be functionally related. Moreover, our comprehensive analyses of FNMs in yeast showed that they are powerful at capturing both known and putative novel regulatory interactions, thus suggesting a promising strategy towards the systematic identification of feedback regulation in biological networks. Many FNMs appeared as excellent candidates for the prioritization of follow-up biochemical characterization, which is a recurring bottleneck in the targeting of complex diseases. More generally, our work highlights a fruitful avenue for integrating and harnessing genomic network data.

DEPACT and PACMatch: A Workflow of Designing De Novo Protein Pockets to Bind Small Molecules

Engineering of new functional proteins such as enzymes and biosensors involves the design of new protein pockets for the specific binding of small molecules. Here, we report a workflow composed of two new computational methods to execute this task. The DEPACT (Design Pocket as a Cluster based on Templates) method is a data-driven approach to design and evaluate small-molecule-binding pockets as isolated clusters, while the PACMatch method is a computational approach to match pocket residues in a cluster model to positions on given protein scaffolds. Using DEPACT and its scoring function, pocket clusters of natural-pocket-like chemical compositions and protein-ligand interaction strength can be designed. DEPACT can design pocket clusters containing water- or metal-ion-mediated protein-ligand interactions. While being able to efficiently treat relatively large pocket cluster models (e.g., of around 10 pocket residues), PACMatch outperforms previous methods in test cases of recovering the native positions of pocket residues in natural enzyme-substrate complexes.

A backbone-centred energy function of neural networks for protein design

A protein backbone structure is designable if a substantial number of amino acid sequences exist that autonomously fold into it^1,2. It has been suggested that the designability of backbones is governed mainly by side chain-independent or side chain type-insensitive molecular interactions^3-5, indicating an approach for designing new backbones (ready for amino acid selection) based on continuous sampling and optimization of the backbone-centred energy surface. However, a sufficiently comprehensive and precise energy function has yet to be established for this purpose. Here we show that this goal is met by a statistical model named SCUBA (for Side Chain-Unknown Backbone Arrangement) that uses neural network-form energy terms. These terms are learned with a two-step approach that comprises kernel density estimation followed by neural network training and can analytically represent multidimensional, high-order correlations in known protein structures. We report the crystal structures of nine de novo proteins whose backbones were designed to high precision using SCUBA, four of which have novel, non-natural overall architectures. By eschewing use of fragments from existing protein structures, SCUBA-driven structure design facilitates far-reaching exploration of the designable backbone space, thus extending the novelty and diversity of the proteins amenable to de novo design.