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Exploring the Mutarotation Mechanism of Glucose in Solution Using Deep Learning Potential.

Hongqiang Cui1,2, Da Zheng1,2, Huiying Chu1,3

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Glucose mutarotation, the conversion between α- and β-anomers, primarily occurs via a ring-opening pathway. Deep learning potential molecular dynamics simulations reveal this mechanism has a lower barrier, favoring β-anomer formation.

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Area of Science:

  • Carbohydrate Chemistry
  • Computational Chemistry
  • Biophysical Chemistry

Background:

  • Glucose mutarotation interconverts α- and β-anomers, affecting glucose properties.
  • Two mechanisms exist, but the predominant pathway in aqueous solution is unclear.
  • Distinguishing evidence between pathways is lacking.

Purpose of the Study:

  • To clarify the predominant mechanism of glucose mutarotation in aqueous solution.
  • To investigate the free energy profiles and reaction pathways using advanced simulations.

Main Methods:

  • Deep Learning Potential Molecular Dynamics (DLPMD) simulations were employed.
  • Simulations provided a statistically converged description of the reaction landscape.
  • Compared DLPMD results with previous ab initio molecular dynamics.

Main Results:

  • Mutarotation preferentially proceeds via the ring-opening pathway.
  • The ring-opening pathway has a lower activation barrier and avoids high-energy intermediates.
  • The formation of the β-anomer is kinetically favored within the ring-opening mechanism.

Conclusions:

  • DLPMD simulations accurately capture glucose mutarotation pathways and conformational preferences.
  • The ring-opening pathway is the dominant mechanism for glucose mutarotation.
  • DLPMD offers an efficient computational alternative to traditional DFT methods for studying such reactions.