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Related Experiment Video

Updated: Jun 25, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

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

Xuezhi Xie^1,2, Pedro A Valiente¹, Jisun Kim¹

¹Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.

ACS Central Science

|May 27, 2024

Summary

This summary is machine-generated.

HelixDiff, a new AI model, generates accurate helical protein structures. It successfully designed a novel peptide drug targeting the GLP-1 receptor with enhanced stability and protease resistance.

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Last Updated: Jun 25, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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Published on: March 1, 2022

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Published on: November 3, 2011

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

Computational Biology
Structural Bioinformatics
Drug Design

Background:

Generating accurate protein structures is crucial for understanding biological function and designing new therapeutics.
Existing methods for de novo protein structure generation have limitations in accuracy and conditional design.

Purpose of the Study:

To introduce HelixDiff, a novel score-based diffusion model for generating all-atom helical structures.
To develop a hot spot-specific algorithm for conditional de novo design of alpha-helices.
To demonstrate the utility of HelixDiff in designing a functional peptide therapeutic.

Main Methods:

Utilized a score-based diffusion model (HelixDiff) for generating protein structures.
Implemented a hot spot-specific generation algorithm for targeted peptide design.
Validated generated structures using root-mean-square deviations (RMSDs) and compared performance against a GAN-based model.
Designed and tested a D-peptide agonist for the glucagon-like peptide-1 receptor (GLP-1R).

Main Results:

HelixDiff generates alpha-helices with near-native geometries (RMSD < 1 Å).
HelixDiff outperforms previous GAN-based models in sequence recovery and Rosetta scores.
Successfully designed a GLP-1 receptor agonist D-peptide with high stability and protease resistance.
The designed D-peptide activated GLP-1R cAMP accumulation without activating GLP-2R and induced AKT phosphorylation.

Conclusions:

HelixDiff is a powerful tool for generating accurate helical protein structures and enabling conditional de novo design.
Matching functional hotspots is critical for designing effective D-peptide agonists.
The developed D-peptide agonist shows promise as a stable and potent therapeutic for GLP-1R related conditions.