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Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
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Unveiling hidden intermediate states in protein folding with AI-based conditional transition clustering.

Xuyang Liu1, Wensheng Cai1,2, Haohao Fu1,2

  • 1Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, College of Chemistry, Nankai University, Tianjin 300071, China.

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Summary

We developed AI-Based conditional transition clustering (CTC) to analyze protein folding dynamics from molecular dynamics (MD) simulations. This new method objectively identifies protein conformational states and folding pathways without prior assumptions.

Keywords:
AImachine learningmolecular dynamicsprotein-folding pathways

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Area of Science:

  • Computational biophysics
  • Biomolecular simulations
  • Artificial intelligence in science

Background:

  • Protein folding mechanisms and intermediate states are crucial but challenging to study.
  • Molecular dynamics (MD) simulations generate large datasets, but extracting kinetic models is difficult.
  • Conventional methods like Markov state models have limitations in identifying conformational states.

Purpose of the Study:

  • To present a novel AI-Based conditional transition clustering (CTC) framework for analyzing MD trajectories.
  • To overcome limitations of state-centric methods by adopting a dynamics-centric approach.
  • To enable objective identification of protein conformational states and folding pathways.

Main Methods:

  • AI-Based conditional transition clustering (CTC) framework.
  • Utilizing AI-based normalizing flows to estimate conditional transition probabilities.
  • Defining conformational states as kinetically trapped regions identified from system dynamics.

Main Results:

  • CTC successfully identifies critical intermediate and transition states in protein folding simulations.
  • The framework reveals protein folding pathways without prior assumptions on the number or properties of states.
  • Identified states as 'kinetic islands' with low escape probabilities.

Conclusions:

  • CTC offers a more objective and physically grounded method for analyzing complex biomolecular systems.
  • The dynamics-centric approach enhances the discovery of protein conformational mechanisms.
  • This AI-driven framework advances the study of protein folding dynamics.