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Updated: May 23, 2025

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DeepPath: Overcoming data scarcity for protein transition pathway prediction using physics-based deep learning.

Yui Tik Pang1, Katie M Kuo1, Lixinhao Yang2

  • 1School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Biorxiv : the Preprint Server for Biology
|March 10, 2025
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This summary is machine-generated.

DeepPath rapidly generates realistic protein transition pathways using deep learning and active learning. This method offers an efficient alternative to computationally expensive molecular dynamics simulations for studying protein dynamics.

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Area of Science:

  • Biophysics
  • Computational Biology
  • Structural Biology

Background:

  • Protein structural dynamics are critical for function, but static models are common.
  • Molecular dynamics (MD) simulations offer atomistic detail but are computationally intensive.
  • Exploring large-scale protein motions requires efficient computational methods.

Purpose of the Study:

  • Introduce DeepPath, a deep-learning framework for rapid generation of protein transition pathways.
  • Enable efficient exploration of protein conformational changes.
  • Provide an alternative to traditional MD simulations.

Main Methods:

  • DeepPath utilizes a deep-learning framework with active learning.
  • Molecular mechanical force fields serve as an oracle to guide pathway generation.
  • Validated on SHP2 activation, CdiB H1 secretion, and BAM complex opening.

Main Results:

  • DeepPath accurately predicted transition pathways for all test cases.
  • Key intermediate structures and transient interactions were reproduced.
  • A novel intermediate for the BAM complex aligned with experimental data (TMscore = 0.91).

Conclusions:

  • DeepPath efficiently generates physically realistic protein transition pathways.
  • The framework provides a rapid and accurate alternative to MD simulations.
  • DeepPath accelerates the study of protein conformational dynamics.