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Atomic partial charge predictions for furanoses by random forest regression with atom type symmetry function.

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Summary

We developed a new descriptor, atom type symmetry function (ATSF), to improve machine learning predictions of conformational adaptive (CA) charges for furanoses. ATSF enhances molecular mechanics (MM) calculations by better capturing electrostatic variations due to conformational changes.

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Area of Science:

  • Computational chemistry
  • Biomolecular modeling
  • Machine learning

Background:

  • Furanoses, key biomolecule components, possess complex conformational spaces due to flexible rings and exo-cyclic groups.
  • Accurate electrostatic potential representation is crucial for molecular mechanics (MM) simulations, especially capturing conformational adaptive (CA) charges.

Purpose of the Study:

  • To introduce and evaluate a novel descriptor, atom type symmetry function (ATSF), for describing furanose conformations.
  • To improve the prediction accuracy of CA charges and dipole moments for furanoses using machine learning.

Main Methods:

  • Developed ATSF based on atom-centered symmetry functions (ACSF), categorizing atoms by MM force field properties.
  • Employed random forest regression (RFR) models with ATSF, ACSF, and atom name symmetry functions for prediction.
  • Compared RFR model performance using different descriptors for predicting CA charges and dipole moments.

Main Results:

  • RFR models utilizing ATSF demonstrated improved prediction of CA charges and dipole moments for furanoses compared to ACSF and atom name symmetry functions.
  • ATSF-predicted CA charges more accurately reproduced carbohydrate-water and carbohydrate-protein interactions than ensemble-averaged charges.
  • ATSF's atom type categorization introduced chemical perception and optimized coordinate size for capturing furanose structural features.

Conclusions:

  • ATSF is a superior descriptor for furanose conformation, enhancing machine learning-based CA charge prediction.
  • The improved CA charges enable more accurate molecular mechanics simulations by accounting for dynamic electrostatic variations.
  • ATSF's design facilitates its application to other biomolecules due to widespread MM force field implementations.