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Amy B Guo1,2, Deniz Akpinaroglu1,2, Mark J S Kelly3

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Scientists developed a new deep learning method to design dynamic protein structures, enabling precise control over protein movements and signaling behavior for the first time.

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Area of Science:

  • * Computational biology and structural biology.
  • * Protein engineering and design.
  • * Molecular dynamics and biophysics.

Background:

  • * Deep learning has advanced static protein structure design.
  • * Controlled conformational dynamics in switch-like signaling proteins remain challenging for de novo design.

Purpose of the Study:

  • * To develop a general deep-learning-guided approach for de novo design of dynamic protein conformational changes.
  • * To achieve atom-level precision in designing switch-like protein mechanisms.
  • * To create tunable and controllable protein signaling behavior.

Main Methods:

  • * Utilized a deep-learning-guided strategy for de novo protein design.
  • * Solved four protein structures to validate designed conformations.
  • * Employed microsecond-scale molecular dynamics simulations.
  • * Investigated modulation of conformational landscape by ligands and mutations.
  • * Performed physics-based simulations to analyze residue interactions and predict mutations.

Main Results:

  • * Successfully designed and validated dynamic conformational changes in proteins.
  • * Demonstrated microsecond transitions between designed states.
  • * Showed that conformational landscapes can be modulated by orthosteric ligands and allosteric mutations.
  • * Physics-based simulations confirmed agreement with deep learning predictions and experimental data.
  • * Identified state-dependent residue interaction networks and predicted effective mutations.

Conclusions:

  • * A general deep-learning approach enables de novo design of dynamic protein conformational changes.
  • * Designed proteins exhibit tunable and controllable signaling behavior.
  • * This framework opens new possibilities for engineering novel protein functions and biological mechanisms.