ArticleACS central science2024
HelixDiff, a Score-Based Diffusion Model for Generating All-Atom α-Helical Structures.
Article in ACS central science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Mirror-Peptidizer: In Silico Mirror-Image Screening Enables De Novo Design of D-Peptide Binders without D-Protein Synthesis.Research (Washington, D.C.) · 2026Article
- Accurate de novo design of high-affinity protein-binding macrocycles using deep learning.Nature chemical biology · 2025Article
- State-Specific Peptide Design Targeting G Protein-Coupled Receptors.Journal of chemical information and modeling · 2025Article
- AI-Driven De Novo Design of Ultra Long-Acting GLP-1 Receptor Agonists.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Conformational Dynamics and Binding Interactions of SARS-CoV-2 Spike Protein Variants: Omicron, XBB.1.9.2, and EG.5.Journal of chemical information and modeling · 2025Article
- Ligand-Induced Biased Activation of GPCRs: Recent Advances and New Directions from In Silico Approaches.Molecules (Basel, Switzerland) · 2025Review
- An outlook on structural biology after AlphaFold: tools, limits and perspectives.FEBS open bio · 2025Review
- AccuratebioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.