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Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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Updated: May 9, 2026

Determination of Protein-ligand Interactions Using Differential Scanning Fluorimetry
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Published on: September 13, 2014

Distance-Restraint-Guided Diffusion Models for Sampling Protein Conformational Changes and Ligand Dissociation

Tatsuki Hori1, Yoshitaka Moriwaki1,2, Ryuichiro Ishitani1,3

  • 1Department of Computational Drug Discovery and Design, Medical Research Laboratory, Institute of Integrated Research, Institute of Science Tokyo, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.

Journal of Chemical Theory and Computation
|May 7, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using distance restraints with AI models to predict protein structures in specific states. This approach efficiently characterizes biomolecular dynamics and binding thermodynamics.

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Biophysics

Background:

  • Protein conformational dynamics and ligand binding are crucial for biological functions but challenging to study systematically.
  • Current methods for sampling protein structures and characterizing thermodynamics have limitations.

Purpose of the Study:

  • To develop a novel method for predicting protein structures in specific conformational states.
  • To enable systematic sampling of biomolecular dynamics and thermodynamic characterization without retraining models.

Main Methods:

  • A distance-restraint-guided inference method extending AlphaFold3-like diffusion models.
  • Restraining intergroup distances during the reverse diffusion process for systematic sampling along reaction coordinates.
  • Implementation in Boltz-2 and validation on proteins with conformational transitions and protein-peptide dissociation.

Main Results:

  • Achieved uniform coverage of conformational space with high structural quality, outperforming conventional methods.
  • Demonstrated effectiveness on model proteins and a protein-peptide dissociation pathway.
  • Successfully combined deep learning predictions with physics-based simulations to construct free energy landscapes.

Conclusions:

  • The developed method efficiently characterizes biomolecular dynamics and ligand binding.
  • It bridges deep learning-based structure prediction with physics-based simulations.
  • Provides a powerful strategy for studying protein conformational changes and binding thermodynamics.