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Related Concept Videos

Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
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Laminar Flow: Problem Solving

Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower indicates...
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Multi-pass Transmembrane Proteins and β-barrels01:09

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In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
α-Helix containing multi-pass transmembrane proteins
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Protein Diffusion in the Membrane

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TPS-Flow: Physics-Guided Flow-Based Generative Modeling of Protein Transition Paths.

Kai Xu1, Likun Zhao1, Yanan Tian1

  • 1Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.

Journal of Chemical Information and Modeling
|July 10, 2026
PubMed
Summary
This summary is machine-generated.

TPS-Flow is a new AI framework that efficiently generates protein transition paths. It accurately models protein dynamics and conformational changes, reducing computational costs for molecular dynamics simulations.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Machine Learning

Background:

  • Molecular dynamics (MD) simulations are crucial for understanding protein dynamics but are computationally expensive.
  • Sampling transition paths between protein states is particularly challenging for MD and machine learning methods.
  • Existing methods struggle to accurately capture the complex conformational landscape of proteins.

Purpose of the Study:

  • To introduce TPS-Flow, a novel physics-guided flow-based generative framework for protein conformational path sampling.
  • To enable efficient and accurate generation of transition paths between predefined protein states.
  • To bridge the gap between atomistic simulations and deep generative modeling for protein dynamics.

Main Methods:

  • TPS-Flow utilizes residue-level SE(3) transforms and a spatiotemporal gated attention encoder.
  • It learns an interpolation velocity field from MD trajectories using flow-matching.
  • The framework incorporates optional energy/structure constraints and physics-based relaxation.

Main Results:

  • TPS-Flow accurately preserves residue fluctuation patterns and conformational pathways across diverse protein systems.
  • It generates intermediate structures with docking scores comparable to reference MD.
  • The method demonstrates superior efficiency and reduced model size compared to MDGen.
  • TPS-Flow shows robust generalization to out-of-distribution mutants, maintaining fold continuity.

Conclusions:

  • TPS-Flow offers a powerful and efficient approach for sampling protein transition paths.
  • It successfully integrates physics-based principles with deep generative modeling.
  • This framework advances the study of protein dynamics and conformational changes.