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Analyzing the structure-activity relationship of raspberry polysaccharides using interpretable artificial neural

Jie Lu1, Yongjing Yang1, Eun-Kyung Hong2

  • 1School of Ecological and Environmental Engineering, Qinghai University, Xining 810016, China.

International Journal of Biological Macromolecules
|February 25, 2024
PubMed
Summary

Researchers explored the structure-activity relationship of raspberry polysaccharides using artificial neural networks (ANNs). Key structural features like molecular weight and specific sugar contents significantly influence immune-enhancing activity.

Keywords:
Interpretable artificial neural networkRaspberry polysaccharidesStructure-activity relationship

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Area of Science:

  • Carbohydrate Chemistry
  • Immunology
  • Computational Biology

Background:

  • The structure-activity relationship (SAR) of polysaccharides is crucial for understanding their biological functions.
  • Raspberry pulp is a source of bioactive polysaccharides with potential immune-enhancing properties.
  • Predictive modeling and interpretability methods can elucidate complex SARs.

Purpose of the Study:

  • To investigate the SAR of polysaccharides and their fragments from raspberry pulp.
  • To develop and validate an artificial neural network (ANN) model for predicting immune-enhancing activity based on polysaccharide structure.
  • To identify key structural determinants of immune-enhancing activity using Gradient-weighted class activation mapping (Grad-CAM).

Main Methods:

  • Extraction and purification of six polysaccharides and three polysaccharide fragments from raspberry pulp.
  • Development of an ANN model using structural information as input and immune activity as output.
  • Application of Grad-CAM for interpretability analysis to identify critical structural features.

Main Results:

  • The ANN model demonstrated robust convergence with low training and testing errors (MSE ~0.003-0.013, MAPE ~0.21%-0.98%).
  • Interpretability analysis revealed that molecular weight, arabinose and galactose content, galacturonic acid content, and specific glycosyl linkages (e.g., →3)-Arap-(1→, Araf-(1→, →4)-Galp-(1→) are major drivers of immune-enhancing activity.
  • The Grad-CAM algorithm successfully highlighted the structural features most influential for activity.

Conclusions:

  • Artificial neural networks effectively model the SAR of raspberry polysaccharides.
  • Specific structural attributes, including sugar composition and linkage patterns, are critical for the immune-enhancing effects of these polysaccharides.
  • This study offers a novel computational approach to understanding polysaccharide SAR, potentially guiding the development of new immunomodulatory agents.