.png){kind=icon}
.png){kind=icon}
Return
HelixDiff, a Score-Based Diffusion Model for Generating All-Atom α-Helical Structures
.png){kind=icon}
OA
AI
DOI：10.1021/acscentsci.3c01488.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 alpha-helices targeting critical hotspot residues in bioactive peptides. HelixDiff generates alpha-helices with near-native geometries for most test scenarios with root-mean-square deviations (RMSDs) less than 1 angstrom. 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.
Keywords:
D-AMINO-ACID
PROTEIN
INTERFACES
RECEPTOR
PEPTIDE
ANALOGS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.