Engineering a Rigid Nucleic Acid Structure to Improve the Limit of Detection for Genetic Assays.

Detecting nucleic acids at ultralow concentrations is critical for research and clinical applications. Particle-based assays are commonly used to detect nucleic acids. However, DNA hybridization on particle surfaces is inefficient due to the instability of tethered sequences, which negatively influences the assay's detection sensitivity. Here, we report a method to stabilize sequences on particle surfaces using a double-stranded linker at the 5' end of the tethered sequence. We termed this method Rigid Double Stranded Genomic Linkers for Improved DNA Analysis (RIGID-DNA). Our method led to a 3- and 100-fold improvement of the assays' clinical and analytical sensitivity, respectively. Our approach can enhance the hybridization efficiency of particle-based assays without altering existing assay workflows. This approach can be adapted to other platforms and surfaces to enhance the detection sensitivity.

HelixDiff, a Score-Based Diffusion Model for Generating All-Atom α-Helical Structures.

Here, we present HelixDiff, a score-based diffusion model for generating all-atom helical structures. We developed a hot spot-specific generation algorithm for the conditional design of α-helices targeting critical hotspot residues in bioactive peptides. HelixDiff generates α-helices with near-native geometries for most test scenarios with root-mean-square deviations (RMSDs) less than 1 Å. Significantly, HelixDiff outperformed our prior GAN-based model with regard to sequence recovery and Rosetta scores for unconditional and conditional generations. As a proof of principle, we employed HelixDiff to design an acetylated GLP-1 D-peptide agonist that activated the glucagon-like peptide-1 receptor (GLP-1R) cAMP accumulation without stimulating the glucagon-like peptide-2 receptor (GLP-2R). We predicted that this D-peptide agonist has a similar orientation to GLP-1 and is substantially more stable in MD simulations than our earlier D-GLP-1 retro-inverse design. This D-peptide analogue is highly resistant to protease degradation and induces similar levels of AKT phosphorylation in HEK293 cells expressing GLP-1R compared to the native GLP-1. We then discovered that matching crucial hotspots for the GLP-1 function is more important than the sequence orientation of the generated D-peptides when constructing D-GLP-1 agonists.

Score-based generative modeling for de novo protein design.

The generation of de novo protein structures with predefined functions and properties remains a challenging problem in protein design. Diffusion models, also known as score-based generative models (SGMs), have recently exhibited astounding empirical performance in image synthesis. Here we use image-based representations of protein structure to develop ProteinSGM, a score-based generative model that produces realistic de novo proteins. Through unconditional generation, we show that ProteinSGM can generate native-like protein structures, surpassing the performance of previously reported generative models. We experimentally validate some de novo designs and observe secondary structure compositions consistent with generated backbones. Finally, we apply conditional generation to de novo protein design by formulating it as an image inpainting problem, allowing precise and modular design of protein structure.