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

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Iterative Modeling via Structural Diffusion (IMSD): Exploring Fold-Switching Pathways in Metamorphic Proteins Using

Dmitrii A Luzik¹, Nikolai R Skrynnikov^1,2

¹Laboratory of Biomolecular NMR, St. Petersburg State University, St. Petersburg, Russia.

|September 24, 2025

Summary

This summary is machine-generated.

Researchers developed a new computational method, iterative modeling via structural diffusion (IMSD), to model how metamorphic proteins switch between structures. This deep learning approach accurately maps protein fold-switching pathways, aiding in understanding protein dynamics.

Keywords:

folding/unfolding intermediates generative diffusion model metamorphic proteins modeling of fold‐switching pathways parallel cascade selection molecular dynamics

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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:

Structural biology
Computational biology
Protein dynamics

Background:

Metamorphic proteins (MPs) exhibit multiple distinct spatial structures, making them crucial for various biological functions.
Predicting protein fold-switching potential and modeling refolding pathways are significant challenges in structural biology.

Purpose of the Study:

To develop a novel computational algorithm for modeling metamorphic protein fold-switching pathways.
To leverage deep learning, specifically the UFConf predictor based on AlphaFold2, for modeling protein conformational changes.

Main Methods:

Developed the iterative modeling via structural diffusion (IMSD) algorithm, driven by the UFConf generative diffusion predictor.
Utilized a noising and denoising process to iteratively model the transition between conformational states (A to B) of MPs.
Applied the IMSD protocol to metamorphic proteins GA98, SA1 V90T, and the RfaH C-terminal domain.

Main Results:

Successfully mapped the complete fold-switching pathways for selected metamorphic proteins.
The modeled pathways align with the dual-funnel energy landscape model for MPs.
The results show good agreement with existing experimental data for metamorphic protein structures.

Conclusions:

The UFConf-based IMSD protocol provides an effective method for modeling metamorphic protein fold-switching.
This work contributes to the advancement of deep learning-based tools for simulating protein dynamics.
The developed protocol aids in understanding the complex conformational flexibility of metamorphic proteins.