Modern vaccines have largely shifted from using whole, killed or attenuated pathogens to being based on subunit components. Since this diminishes immunogenicity, vaccine adjuvants that enhance the immune response to purified antigens are critically needed. Further advantages of adjuvants include dose sparing, increased vaccine efficacy in immunocompromised individuals and the potential to protect against highly variable pathogens by broadening the immune response. Due to their ability to link the innate with the adaptive immune response, Toll-like receptor (TLR) agonists are highly promising as adjuvants in vaccines against life-threatening and complex diseases such as cancer, AIDS and malaria. TLRs are transmembrane receptors, which are predominantly expressed by innate immune cells. They can be classified into cell surface (TLR1, TLR2, TLR4, TLR5, TLR6) and intracellular TLRs (TLR3, TLR7, TLR8, TLR9), expressed on endosomal membranes. Besides a transmembrane domain, each TLR possesses a leucine-rich repeat (LRR) segment that mediates PAMP/DAMP recognition and a TIR domain that delivers the downstream signal transduction and initiates an inflammatory response. Thus, TLRs are excellent targets for adjuvants to provide a “danger” signal to induce an effective immune response that leads to long-lasting protection. The present review will elaborate on applications of TLR ligands as vaccine adjuvants and immunotherapeutic agents, with a focus on clinically relevant adjuvants.

Evolution guides biological systems to populate ecological niches, with viruses among the most successful examples of this principle. Viruses evolved over billions of years to efficiently transfer genetic information. Although viruses are highly diverse, most have converged towards remarkable similarity in the size and shape of their capsids ^1,2 . By contrast, generative models for protein design enable the creation of protein architectures that are absent from nature ^3–5 . Here we investigate whether protein assemblies designed by artificial intelligence can be functionalized to construct nucleic acid transport vehicles that are independent of evolutionary trajectories. By combining natural protein domains with synthetic protein assemblies, we create more than 100 bottom-up RNA transfer vehicles with unique sizes and shapes. These vehicles surpass the RNA transfer efficiency of widely used delivery vehicles by several orders of magnitude. In addition, we demonstrate that their tropism can be programmed by incorporation of computationally designed peptide binders and use them to deliver therapeutically relevant cargo RNAs into a wide range of cellular models. We show the in vivo biodistribution of one of these vehicles in a mouse at near-single-cell resolution, confirm its safety, and use it to perform a gene-editing treatment strategy for Duchenne muscular dystrophy in patient-derived cells and a pig. Our work demonstrates how proteins created by generative artificial intelligence can be harnessed for the rational engineering of RNA transport systems with the desired properties by overcoming the limitations of natural protein diversity.

Directed evolution enables the discovery of protein mutants with improved fitness through iterative rounds of selection and has been widely applied to adeno-associated virus (AAV) capsid engineering. Machine learning (ML) can augment this process by prioritising mutants for experimental validation, but whether its benefits outweigh the added cost of ML-designed library construction remains unclear. Here, we address this question by considering improvements in manufacturing efficiency of AAV capsids in the context of exosomal encapsulation, which is a technique for improving AAV immunogenicity. We generated a directed evolution dataset comprising 53,974 mutants across three rounds of selection and an independent assessment dataset of 472 ML-designed mutants with detailed profiling. Directed evolution alone yielded a 3-fold improvement, while ML-assisted directed evolution yielded a 41-fold improvement in manufacturing efficiency, demonstrating that ML-assisted directed evolution outperforms directed evolution alone. We show that data quality – specifically the number of selection rounds and the statistical power of mutant counts – has a greater impact on model performance than model architecture. Finally, we provide practical guidance on experimental design and implementation of ML tuning strategies that can augment model performance in protein engineering applications.

Gene tagging enables functional analysis of proteins in vivo . Currently existing technologies in Drosophila suffer from drawbacks including limited flexibility, copy number variation, and/or imprecise expression. To address this, we have developed the Exchangeable Tagging System (ExTaSy), a CRISPR/Cas9-based platform which introduces a 3XHA tag into the endogenous gene locus. Importantly, the 3XHA can be subsequently exchanged for other tags using fly crosses, making the platform highly versatile and accessible. Simultaneously, an excisable transgenic marker allows virtually scarless locus modification. Here, we report successful tagging of 55 different loci, which shows that ExTaSy can be used to tag genes across the Drosophila genome and demonstrate its versatility for functional studies. We also developed a software that automates guide RNA and homology arm design, aiding efficient synthesis of transgenesis constructs. This novel technology will significantly improve our ability to visualize and manipulate proteins using various applications in vivo while maintaining endogenous expression levels and genetic background.