ACS Central Science最新文献

Neutrophil extracellular traps (NETs) are DNA networks released by neutrophils, first described as a defense response against pathogens but have since been associated with numerous inflammatory diseases. Diverse physical material properties have been shown to promote NET formation. Herein, we report the discovery that the charge of self-assembled peptide hydrogels predictably modulates the formation of NETs in vivo within the implanted material. Positively charged gels induce rapid NET release, whereas negatively charged gels do not. This differential immune response to our self-assembled peptide gels enabled the development of a material platform that allows rheostat-like modulation over the degree of NET formation with anatomical and locoregional control.

Self-assembled peptide-based hydrogel strategy for controlling the formation of neutrophil extracellular traps (NETs) in vivo with anatomical and locoregional precision, and the ability to regulate the degree of the response.

Neutrophil extracellular traps (NETs) are DNA networks released by neutrophils, first described as a defense response against pathogens but have since been associated with numerous inflammatory diseases. Diverse physical material properties have been shown to promote NET formation. Herein, we report the discovery that the charge of self-assembled peptide hydrogels predictably modulates the formation of NETs in vivo within the implanted material. Positively charged gels induce rapid NET release, whereas negatively charged gels do not. This differential immune response to our self-assembled peptide gels enabled the development of a material platform that allows rheostat-like modulation over the degree of NET formation with anatomical and locoregional control.

Deep learning models for anticipating the products of organic reactions have found many use cases, including validating retrosynthetic pathways and constraining synthesis-based molecular design tools. Despite compelling performance on popular benchmark tasks, strange and erroneous predictions sometimes ensue when using these models in practice. The core issue is that common benchmarks test models in an in-distribution setting, whereas many real-world uses for these models are in out-of-distribution settings and require a greater degree of extrapolation. To better understand how current reaction predictors work in out-of-distribution domains, we report a series of more challenging evaluations of a prototypical SMILES-based deep learning model. First, we illustrate how performance on randomly sampled data sets is overly optimistic compared to performance when generalizing to new patents or new authors. Second, we conduct time splits that evaluate how models perform when tested on reactions published years after those in their training set, mimicking real-world deployment. Finally, we consider extrapolation across reaction classes to reflect what would be required for the discovery of novel reaction types. This panel of tasks can reveal the capabilities and limitations of today’s reaction predictors, acting as a crucial first step in the development of tomorrow’s next-generation models capable of reaction discovery.

Despite excellent benchmark performance, ML models for reaction prediction can struggle on real-world data─we evaluate these limitations by challenging a model on different out-of-distribution tasks.

Mitochondrial targeting has emerged as an attractive method for antitumor treatment. However, most of the mitochondria targeted drugs focused on inhibiting tumor cells, while their potential for activation of immune responses in the tumor microenvironment has rarely been described. In this study, we report a photosensitive iridium complex MitoIrL2, which enabled the simultaneous mitochondrial modulation of macrophages and tumor cells to achieve synergistic antitumor immunity. The adjustment of the mitochondrial respiratory chain, HIF-1α, and the NF-κB pathway in macrophages drove the metabolic reprogramming from oxidative phosphorylation (OXPHOS) to glycolysis, converting protumor M2 into the antitumor M1 phenotype. Downregulated expression of immunosuppressive checkpoint SIRPα has also been observed on macrophages. Meanwhile, the mitochondrial targeting MitoIrL2 enhanced the immunogenic cell death of tumor cells and reversed the immunosuppressive tumor microenvironment, which activated the systemic immune response and established long-term immune memory in vivo. This work illustrates a promising strategy to simultaneously regulate macrophages toward the antitumor phenotype and enhance immunogenic cell death in tumor cells for synergistic antitumor immunotherapy.

A mitochondrial targeting iridium photosensitizer could achieve direct immune activation of both macrophages and tumor cells, synergistically enhancing antitumor immunity via mitochondrial modulation.

Academia and industry see mixed results replacing fluorinated chemicals in refrigeration, textiles, and ion-exchange membranes.

Mitochondrial targeting has emerged as an attractive method for antitumor treatment. However, most of the mitochondria targeted drugs focused on inhibiting tumor cells, while their potential for activation of immune responses in the tumor microenvironment has rarely been described. In this study, we report a photosensitive iridium complex MitoIrL2, which enabled the simultaneous mitochondrial modulation of macrophages and tumor cells to achieve synergistic antitumor immunity. The adjustment of the mitochondrial respiratory chain, HIF-1α, and the NF-κB pathway in macrophages drove the metabolic reprogramming from oxidative phosphorylation (OXPHOS) to glycolysis, converting protumor M2 into the antitumor M1 phenotype. Downregulated expression of immunosuppressive checkpoint SIRPα has also been observed on macrophages. Meanwhile, the mitochondrial targeting MitoIrL2 enhanced the immunogenic cell death of tumor cells and reversed the immunosuppressive tumor microenvironment, which activated the systemic immune response and established long-term immune memory in vivo. This work illustrates a promising strategy to simultaneously regulate macrophages toward the antitumor phenotype and enhance immunogenic cell death in tumor cells for synergistic antitumor immunotherapy.

Thioesters, rather than oxo-esters, can be tolerated and processed during translation to incorporate unnatural monomers.

Macrocyclic peptides make up a unique class of modalities known for their high affinity, specificity, and ability to modulate protein-protein interactions, including receptor activation. Messenger RNA display, including the Random Nonstandard Peptides Integrated Discovery (RaPID) system, stands out in identifying target-specific macrocyclic peptides, producing potent binders with low to subnanomolar dissociation constants against diverse targets. It has often been discussed that this success is partly attributed to the vast library of over a trillion different peptide sequences expressed from the corresponding mRNA sequences. However, the impact of library scales on the identification of various binders has not been experimentally validated. Here, we report the RaPID selections against an ectodomain of a receptor tyrosine kinase MET using peptide libraries ranging from 10⁶ to 10¹⁴ unique members of mRNAs. We thoroughly analyzed the outcomes, including the binding kinetic properties, of the enriched peptide families. This study provides valuable guidelines for designing libraries with various numbers of sequences and selection conditions to enrich macrocyclic peptides with the desired characteristics.

Macrocyclic peptides make up a unique class of modalities known for their high affinity, specificity, and ability to modulate protein–protein interactions, including receptor activation. Messenger RNA display, including the Random Nonstandard Peptides Integrated Discovery (RaPID) system, stands out in identifying target-specific macrocyclic peptides, producing potent binders with low to subnanomolar dissociation constants against diverse targets. It has often been discussed that this success is partly attributed to the vast library of over a trillion different peptide sequences expressed from the corresponding mRNA sequences. However, the impact of library scales on the identification of various binders has not been experimentally validated. Here, we report the RaPID selections against an ectodomain of a receptor tyrosine kinase MET using peptide libraries ranging from 10⁶ to 10¹⁴ unique members of mRNAs. We thoroughly analyzed the outcomes, including the binding kinetic properties, of the enriched peptide families. This study provides valuable guidelines for designing libraries with various numbers of sequences and selection conditions to enrich macrocyclic peptides with the desired characteristics.

The initial sampling of the sequence space determines the evolution of the families by the RaPID selection pressure for slow dissociation rates.