RMH

Click chemistry is a powerful concept that refers to a set of covalent bond-forming reactions with highly favorable properties. In this Perspective, I outline the analogous concept of click biology as a set of reactions derived from the regular building blocks of living cells, rapidly forming covalent bonds to specific partners under cell-friendly conditions. Click biology using protein components employs canonical amino acids and may react close to the diffusion limit, with selectivity in living cells amid thousands of components generated from the same building blocks. I discuss how the criteria for click chemistry can be applied or modified to fit the extra constraints of click biology and achieve favorable characteristics for biological research. Existing reactions that may be described as click biology include split intein reconstitution, spontaneous isopeptide bond formation by SpyTag and SpyCatcher and suicide enzyme reaction with small-molecule ligands (HaloTag and SNAP-tag). I also describe how click biology has created new possibilities in fields including molecular imaging, mechanobiology, vaccines and engineering cellular intelligence. This Perspective discusses click biology as an analogy to click chemistry and examines reactions carried out using building blocks present in every living cell, enabling rapid selective covalent bond formation under biologically friendly conditions. Desirable criteria for robust cellular performance are defined, along with new opportunities arising from click biology for fundamental research and synthetic biology.

Insect pest control, which is essential for food and crop production, typically relies on chemical insecticides. At Yngvi Bio, we repurpose bacterial contractile injection systems as biodegradable insecticides, offering ecological safety, target specificity and a scalable path to market.

Quenchbodies, antibodies labelled with fluorophores that increase in intensity upon antigen binding, offer great promise for biosensor development. Nanobody-based quenchbodies are particularly attractive due to their small size, ease of expression, high stability, rapid evolvability, and amenability to protein engineering. However, existing designs for protein detection show limited dynamic range, with fluorescence increases of only 1.1–1.4 fold. Here we identify the tryptophan residues in the nanobody complementarity-determining regions (CDRs) that are critical to quenchbody performance. Using a combination of rational design and molecular dynamics simulations, we developed an optimised nanobody scaffold with tryptophans introduced at key positions. We used this scaffold in an in vitro directed-evolution screen against human inflammatory cytokine interleukin-6 (IL-6). This yielded quenchbodies with 1.5–2.4-fold fluorescence increases, enabling IL-6 detection down to 1–2 nM. Our scaffold provides a valuable platform for developing biosensors for diverse protein targets, with applications in research, diagnostics, and environmental monitoring. Optimised nanobody-based quenchbodies are developed through rational design and in vitro evolution, enabling improved protein detection with up to 2.4-fold fluorescence increase and 2 nM sensitivity for IL-6.

Until now, computationally designed enzymes exhibited low catalytic rates and required intensive experimental optimization to reach activity levels observed in comparable natural enzymes. These results exposed limitations in design methodology and suggested critical gaps in our understanding of the fundamentals of biocatalysis. We present a fully computational workflow for designing efficient enzymes in TIM-barrel folds using backbone fragments from natural proteins and without requiring optimization by mutant-library screening. Three Kemp eliminase designs exhibit efficiencies greater than 2,000 M−1 s−1. The most efficient shows more than 140 mutations from any natural protein, including a novel active site. It exhibits high stability (greater than 85 °C) and remarkable catalytic efficiency (12,700 M−1 s−1) and rate (2.8 s−1), surpassing previous computational designs by two orders of magnitude. Furthermore, designing a residue considered essential in all previous Kemp eliminase designs increases efficiency to more than 105 M−1 s−1 and rate to 30 s−1, achieving catalytic parameters comparable to natural enzymes and challenging fundamental biocatalytic assumptions. By overcoming limitations in design methodology, our strategy enables programming stable, high-efficiency, new-to-nature enzymes through a minimal experimental effort. We present a computational approach to the design of high-efficiency enzymes with catalytic parameters comparable to natural enzymes, enabling programming of stable, high-efficiency, new-to-nature Kemp elimination enzymes through minimal experimental effort.

Concentrations of RNAs and proteins provide important determinants of cell fate. Robust gene circuit design requires an understanding of how the combined actions of individual genetic components influence both messenger RNA (mRNA) and protein levels. Here, we simultaneously measure mRNA and protein levels in single cells using hybridization chain reaction Flow-FISH (HCR Flow-FISH) for a set of commonly used synthetic promoters. We find that promoters generate differences in both the mRNA abundance and the effective translation rate of these transcripts. Stronger promoters not only transcribe more RNA but also show higher effective translation rates. While the strength of the promoter is largely preserved upon genome integration with identical elements, the choice of polyadenylation signal and coding sequence can generate large differences in the profiles of the mRNAs and proteins. We used long-read direct RNA sequencing to define the transcription start and splice sites of common synthetic promoters and independently vary the defined promoter and 5′ UTR sequences in HCR Flow-FISH. Together, our high-resolution profiling of transgenic mRNAs and proteins offers insight into the impact of common synthetic genetic components on transcriptional and translational mechanisms. By developing a novel framework for quantifying expression profiles of transgenes, we have established a system for building more robust transgenic systems.

Short open reading frames (sORFs) and their encoded peptides (SEPs) have confounded functional geneticists, as these genes do not fit historical definitions of protein-coding genes. Evading traditional prediction and detection techniques, plant SEP genes have long been neglected in functional studies, but those that have been identified have proven to play numerous critical biological roles. Recent advances in transcriptomics and proteomics have led to the identification of hundreds of putative sORFs and SEPs in plants, some positioned within genes traditionally thought to be non-coding, highlighting a portion of the proteome that has gone unnoticed thus far. In this review, we examine the historical approaches to answering questions on gene function, how they have impacted and continue to impact sORF and SEP identification, and how they have evolved with technological advancements and developments in the field. Additionally, we emphasize the need for functional validation of putative SEPs in an era of high throughput and -omics based approaches, and discuss potential options for such pursuits. The definition, identification, and characterization of SEPs will ultimately allow for more accurate genomic resources and improved tools with which to develop them, pushing towards a more complete understanding of the functional genome.

HiBiT is an engineered luciferase’s 11-amino-acid component that can be introduced as a tag at either terminus of a protein of interest. When the LgBiT component and a substrate are present, HiBiT and LgBiT dimerize forming a functional luciferase. The HiBiT technology has been extensively used for high-throughput protein turnover studies in cells. Here, we have adapted the use of the HiBiT technology to quantify messenger RNA (mRNA) translation temporally in vitro in the rabbit reticulocyte system and in cellulo in HEK293 cells constitutively expressing LgBiT. The assay system can uniquely detect differences in cap, 5′UTR, modified nucleotide composition, coding sequence optimization and poly(A) length, and their effects on mRNA translation over time. Importantly, using these assays we established the optimal mRNA composition varied depending on the encoded protein of interest, highlighting the importance of screening methods tailored to the protein of interest, and not reliant on reporter proteins. Our findings demonstrated that HiBiT can be easily and readily adapted to monitor real-time mRNA translation in live cells and offers a novel and highly favourable method for the development of mRNA-based therapeutics.

Analyzing RNA secondary structures plays a crucial role in elucidating the functional mechanisms of RNA. Despite advances in RNA structure determination, these methods are low throughout and resource-intensive. While machine learning-based models have achieved remarkable performance in terms of prediction accuracy, challenges such as data scarcity and overfitting remain common. Here, we introduce a phased learning strategy that integrates RNA sequence and structural context information to mitigate the risk of overfitting and employs pairing constraints to train the model on folding scores. This approach effectively addresses both local and long-range nucleotide interactions, substantially improving the robustness of RNA secondary structure predictions. Our comprehensive analysis across multiple benchmarking datasets demonstrated that the performance of our model (DSRNAFold) was superior to that of existing methods, especially in pseudoknot recognition and chemical mapping activity prediction, where our approach showed positive performance.