Engineering Sensor Proteins

Development of epistatic YES and AND protein logic gates and their assembly into signalling cascades

The construction and assembly of artificial allosteric protein switches into information and energy processing networks connected to both biological and non-biological systems is a central goal of synthetic biology and bionanotechnology. However, designing protein switches with the desired input, output and performance parameters is challenging. Here we use a range of reporter proteins to demonstrate that their chimeras with duplicated receptor domains produce YES gate protein switches with large (up to 9,000-fold) dynamic ranges and fast (minutes) response rates. In such switches, the epistatic interactions between largely independent synthetic allosteric sites result in an OFF state with minimal background noise. We used YES gate protein switches based on β-lactamase to develop quantitative biosensors of therapeutic drugs and protein biomarkers. Furthermore, we demonstrated the reconfiguration of YES gate switches into AND gate switches controlled by two different inputs, and their assembly into signalling networks regulated at multiple nodes.

Versatile Protein and Its Subunit Biomolecules for Advanced Rechargeable Batteries

Rechargeable batteries are of great significance for alleviating the growing energy crisis by providing efficient and sustainable energy storage solutions. However, the multiple issues associated with the diverse components in a battery system as well as the interphase problems greatly hinder their applications. Proteins and their subunits, peptides, and amino acids, are versatile biomolecules. Functional groups in different amino acids endow these biomolecules with unique properties including self-assembly, ion-conducting, antioxidation, great affinity to exterior species, etc. Besides, protein and its subunit materials can not only work in solid forms but also in liquid forms when dissolved in solutions, making them more versatile to realize materials engineering via diverse approaches. In this review, it is aimed to offer a comprehensive understanding of the properties of proteins and their subunits, and research progress of using these versatile biomolecules to address the engineering issues of various rechargeable batteries, including alkali-ion batteries, lithium-sulfur batteries, metal-air batteries, and flow batteries. The state-of-the-art advances in electrode, electrolyte, separator, binder, catalyst, interphase modification, as well as recycling of rechargeable batteries are involved, and the impacts of biomolecules on electrochemical properties are particularly emphasized. Finally, perspectives on this interesting field are also provided.

Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments

Novel and improved biocatalysts are increasingly sourced from libraries via experimental screening. The success of such campaigns is crucially dependent on the number of candidates tested. Water-in-oil emulsion droplets can replace the classical test tube, to provide in vitro compartments as an alternative screening format, containing genotype and phenotype and enabling a readout of function. The scale-down to micrometer droplet diameters and picoliter volumes brings about a >10⁷-fold volume reduction compared to 96-well-plate screening. Droplets made in automated microfluidic devices can be integrated into modular workflows to set up multistep screening protocols involving various detection modes to sort >10⁷ variants a day with kHz frequencies. The repertoire of assays available for droplet screening covers all seven enzyme commission (EC) number classes, setting the stage for widespread use of droplet microfluidics in everyday biochemical experiments. We review the practicalities of adapting droplet screening for enzyme discovery and for detailed kinetic characterization. These new ways of working will not just accelerate discovery experiments currently limited by screening capacity but profoundly change the paradigms we can probe. By interfacing the results of ultrahigh-throughput droplet screening with next-generation sequencing and deep learning, strategies for directed evolution can be implemented, examined, and evaluated.

Recent advances in sensing the inter-biomolecular interactions at the nanoscale - A comprehensive review of AFM-based force spectroscopy

Biomolecular interactions underpin most processes inside the cell. Hence, a precise and quantitative understanding of molecular association and dissociation events is crucial, not only from a fundamental perspective, but also for the rational design of biomolecular platforms for state-of-the-art biomedical and industrial applications. In this context, atomic force microscopy (AFM) appears as an invaluable experimental technique, allowing the measurement of the mechanical strength of biomolecular complexes to provide a quantitative characterization of their interaction properties from a single molecule perspective. In the present review, the most recent methodological advances in this field are presented with special focus on bioconjugation, immobilization and AFM tip functionalization, dynamic force spectroscopy measurements, molecular recognition imaging and theoretical modeling. We expect this work to significantly aid in grasping the principles of AFM-based force spectroscopy (AFM-FS) technique and provide the necessary tools to acquaint the type of data that can be achieved from this type of experiments. Furthermore, a critical assessment is done with other nanotechnology techniques to better visualize the future prospects of AFM-FS.

Deep learning of protein sequence design of protein-protein interactions

MOTIVATION

As more data of experimentally determined protein structures are becoming available, data-driven models to describe protein sequence-structure relationships become more feasible. Within this space, the amino acid sequence design of protein-protein interactions is still a rather challenging subproblem with very low success rates-yet, it is central to most biological processes.

RESULTS

We developed an attention-based deep learning model inspired by algorithms used for image-caption assignments to design peptides or protein fragment sequences. Our trained model can be applied for the redesign of natural protein interfaces or the designed protein interaction fragments. Here, we validate the potential by recapitulating naturally occurring protein-protein interactions including antibody-antigen complexes. The designed interfaces accurately capture essential native interactions and have comparable native-like binding affinities in silico. Furthermore, our model does not need a precise backbone location, making it an attractive tool for working with de novo design of protein-protein interactions.

AVAILABILITY AND IMPLEMENTATION

The source code of the method is available at https://github.com/strauchlab/iNNterfaceDesign.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evolution of Supramolecular Systems Towards Next-Generation Biosensors

Supramolecular materials, which rely on dynamic non-covalent interactions, present a promising approach to advance the capabilities of currently available biosensors. The weak interactions between supramolecular monomers allow for adaptivity and responsiveness of supramolecular or self-assembling systems to external stimuli. In many cases, these characteristics improve the performance of recognition units, reporters, or signal transducers of biosensors. The facile methods for preparing supramolecular materials also allow for straightforward ways to combine them with other functional materials and create multicomponent sensors. To date, biosensors with supramolecular components are capable of not only detecting target analytes based on known ligand affinity or specific host-guest interactions, but can also be used for more complex structural detection such as chiral sensing. In this Review, we discuss the advancements in the area of biosensors, with a particular highlight on the designs of supramolecular materials employed in analytical applications over the years. We will first describe how different types of supramolecular components are currently used as recognition or reporter units for biosensors. The working mechanisms of detection and signal transduction by supramolecular systems will be presented, as well as the important hierarchical characteristics from the monomers to assemblies that contribute to selectivity and sensitivity. We will then examine how supramolecular materials are currently integrated in different types of biosensing platforms. Emerging trends and perspectives will be outlined, specifically for exploring new design and platforms that may bring supramolecular sensors a step closer towards practical use for multiplexed or differential sensing, higher throughput operations, real-time monitoring, reporting of biological function, as well as for environmental studies.

Structural Analysis of a Genetically Encoded FRET Biosensor by SAXS and MD Simulations

Inspired by the modular architecture of natural signaling proteins, ligand binding proteins are equipped with two fluorescent proteins (FPs) in order to obtain Förster resonance energy transfer (FRET)-based biosensors. Here, we investigated a glucose sensor where the donor and acceptor FPs were attached to a glucose binding protein using a variety of different linker sequences. For three resulting sensor constructs the corresponding glucose induced conformational changes were measured by small angle X-ray scattering (SAXS) and compared to recently published single molecule FRET results (Höfig et al., ACS Sensors, 2018). For one construct which exhibits a high change in energy transfer and a large change of the radius of gyration upon ligand binding, we performed coarse-grained molecular dynamics simulations for the ligand-free and the ligand-bound state. Our analysis indicates that a carefully designed attachment of the donor FP is crucial for the proper transfer of the glucose induced conformational change of the glucose binding protein into a well pronounced FRET signal change as measured in this sensor construct. Since the other FP (acceptor) does not experience such a glucose induced alteration, it becomes apparent that only one of the FPs needs to have a well-adjusted attachment to the glucose binding protein.

Development of Gold Nanoparticle Micropatterns for the Electrical Detection of Proteins

Protein analysis can be used to efficiently detect the early stages of various diseases. However, conventional protein detection platforms require expensive or complex equipment, which has been a major obstacle to their widespread application. In addition, uncertain signals from non-specific adhesion interfere with the precise interpretation of the results. To overcome these problems, the development of a technique that can detect the proteins in a simple method is needed. In this study, a platform composed of gold nanoparticles (GNPs) was fabricated through a simple imprinting method for protein detection. The corrugated surface naturally formed by the nanoparticle assemblies simultaneously increases the efficiency of adhesion and binding with analytes and reduces undesired interactions. After forming the GNP micropatterns, post-functionalization with both cationic and neutral ligands was performed on the surface to manipulate their electrostatic interaction with proteins. Upon protein binding, the change in the electrical values of the micropatterns was recorded by using a resistance meter. The resistance of the positively charged micropatterns was found to increase due to the electrostatic interaction with proteins, while no significant change in resistance was observed for the neutral micropatterns after immersion in a protein solution. Additionally, the selective adsorption of fluorescent proteins onto the micropatterns was captured using confocal microscopy. These simply imprinted GNP micropatterns are sensitive platforms that can detect various analytes by measuring the electrical resistance with portable equipment.