Branched chemically modified poly(A) tails enhance the translation capacity of mRNA.

Although messenger RNA (mRNA) has proved effective as a vaccine, its potential as a general therapeutic modality is limited by its instability and low translation capacity. To increase the duration and level of protein expression from mRNA, we designed and synthesized topologically and chemically modified mRNAs with multiple synthetic poly(A) tails. Here we demonstrate that the optimized multitailed mRNA yielded ~4.7-19.5-fold higher luminescence signals than the control mRNA from 24 to 72 h post transfection in cellulo and 14 days detectable signal versus <7 days signal from the control in vivo. We further achieve efficient multiplexed genome editing of the clinically relevant genes Pcsk9 and Angptl3 in mouse liver at a minimal mRNA dosage. Taken together, these results provide a generalizable approach to synthesize capped branched mRNA with markedly enhanced translation capacity.

Enhancing luciferase activity and stability through generative modeling of natural enzyme sequences.

The availability of natural protein sequences synergized with generative AI provides new paradigms to engineer enzymes. Although active enzyme variants with numerous mutations have been designed using generative models, their performance often falls short of their wild type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing beneficial single mutations continues to be a formidable task. In this study, using the generative maximum entropy model to analyze Renilla luciferase (RLuc) homologs, and in conjunction with biochemistry experiments, we demonstrated that natural evolutionary information could be used to predictively improve enzyme activity and stability by engineering the active center and protein scaffold, respectively. The success rate to improve either luciferase activity or stability of designed single mutants is ~50%. This finding highlights nature's ingenious approach to evolving proficient enzymes, wherein diverse evolutionary pressures are preferentially applied to distinct regions of the enzyme, ultimately culminating in an overall high performance. We also reveal an evolutionary preference in RLuc toward emitting blue light that holds advantages in terms of water penetration compared to other light spectra. Taken together, our approach facilitates navigation through enzyme sequence space and offers effective strategies for computer-aided rational enzyme engineering.

Enhancing Luciferase Activity and Stability through Generative Modeling of Natural Enzyme Sequences.

The availability of natural protein sequences synergized with generative artificial intelligence (AI) provides new paradigms to create enzymes. Although active enzyme variants with numerous mutations have been produced using generative models, their performance often falls short compared to their wild-type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing beneficial single mutations continues to be a formidable task. In this study, using the generative maximum entropy model to analyze Renilla luciferase homologs, and in conjunction with biochemistry experiments, we demonstrated that natural evolutionary information could be used to predictively improve enzyme activity and stability by engineering the active center and protein scaffold, respectively. The success rate of designed single mutants is ~50% to improve either luciferase activity or stability. These finding highlights nature's ingenious approach to evolving proficient enzymes, wherein diverse evolutionary pressures are preferentially applied to distinct regions of the enzyme, ultimately culminating in an overall high performance. We also reveal an evolutionary preference in Renilla luciferase towards emitting blue light that holds advantages in terms of water penetration compared to other light spectrum. Taken together, our approach facilitates navigation through enzyme sequence space and offers effective strategies for computer-aided rational enzyme engineering.

Fine Tuning the Properties of Stapled Peptides by Stereogenic α-Amino Acid Bridges.

Peptide stapling represents a versatile strategy to generate peptide derivatives with stable helical structures. While a wide range of skeletons have been investigated for cyclizing the side chains of peptides, the stereochemical outcomes from the linkers remain to be better understood. In this study, we incorporated α-amino acids (α-AAs) as bridges to construct side chain-stapled analogs of an interleukin-17A-binding peptide (HAP) and evaluated the impacts of the staples on the peptide's properties. While all AA-derived peptidyl staples drastically increase the enzymatic stability of HAP, our results indicate that compared to the D-amino acid bridges, the L-AA-based staples may generate more significant impacts in increasing the helicity and enhancing the interleukin-17A(IL-17A)-binding affinity of the modified peptide. Using Rosetta modelling and molecular dynamics (MD) simulations, we demonstrate that the chirality (L/D) possessed within the AAs substantially influences the conformation of stapled HAP peptides, providing either stabilizing or destabilizing effects. Based on the computational model, a modification of the stapled HAP leads to the discovery of a peptide with further enhanced helicity, enzymatic stability and IL-17A-inhibiting ability. This systematic study reveals that chiral AAs can serve as modulatory linkers for optimizing the structures and properties of stapled peptides.

O-Glycosylation Induces Amyloid-ß To Form New Fibril Polymorphs Vulnerable for Degradation.

Brain accumulation of amyloid-ß (Aß) peptides (resulting from a disrupted balance between biosynthesis and clearance) occurs during the progression of Alzheimer's disease (AD). Aß peptides have diverse posttranslational modifications (PTMs) that variously modulate Aß aggregation into fibrils, but understanding the mechanistic roles of PTMs in these processes remains a challenge. Here, we chemically synthesized three homogeneously modified isoforms of Aß (1-42) peptides bearing Tyr10 O-glycosylation, an unusual PTM initially identified from the cerebrospinal fluid samples of AD patients. We discovered that O-glycans significantly affect both the aggregation and degradation of Aß42. By combining cryo-EM and various biochemical assays, we demonstrate that a Galß1-3GalNAc modification redirects Aß42 to form a new fibril polymorphic structure that is less stable and more vulnerable to Aß-degrading enzymes (e.g., insulin-degrading enzyme). Thus, beyond showing how particular O-glycosylation modifications affect Aß42 aggregation at the molecular level, our study provides powerful experimental tools to support further investigations about how PTMs affect Aß42 fibril aggregation and AD-related neurotoxicity.

Glycopeptide Self-Assembly Modulated by Glycan Stereochemistry through Glycan-Aromatic Interactions.

Carbohydrates are often utilized to provide hydrophilicity and hydroxyl-based hydrogen bonds in self-assembling glycopeptides, affording versatile scaffolds with wide applicability in biomedical research. However, how stereochemistry of carbohydrates impacts the self-assembly process remains unclear. Here we have established a dimeric tyrosine-rich glycopeptide system for probing the corresponding hydrogelating behavior under the influence of site- and stereospecific glycosylations. Comparison of 18 glycoforms bearing monosaccharides at Tyr4 and Tyr4' shows that the glycopeptides with either α- or ß-anomers exhibit contrary gelating abilities, when the glycan moieties contain axial hydroxyl groups. A high-resolution X-ray crystallographic structure of the ß-galactose-containing gelator, along with other results from spectroscopic, microscopic, and rheological experiments, indicate an unusual carbohydrate-aromatic CH-π bonding that promotes glycopeptide self-assembly. These mechanistic findings, particularly evidence obtained at the angstrom scale, illuminate an unconventional role that carbohydrates can play in building supramolecules. Potential biomaterials exploiting the CH-π bond-based stabilization, as exemplified by an enzyme-resistant hydrogel, may thus be developed.