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Quantitative Structure Activity Relationship Models for the Antioxidant Activity of Polysaccharides
Zhiming Li1, Kaiying Nie1, Zhaojing Wang1
1Department of Bioengineering and Biotechnology, Huaqiao University, Fujian Xiamen, 361021, China.
Plos One
|September 30, 2016
Summary
Quantitative structure activity relationship (QSAR) models were developed to predict polysaccharide antioxidant activity. Artificial neural networks (ANN) proved most accurate for predicting DPPH-scavenging and hydroxyl radical scavenging activities, identifying key molecular descriptors.
Area of Science:
- Biochemistry
- Computational Chemistry
- Pharmacology
Background:
- Polysaccharides exhibit significant antioxidant properties, crucial for various biological applications.
- Understanding the relationship between polysaccharide structure and antioxidant activity is essential for drug discovery and development.
- Quantitative Structure-Activity Relationship (QSAR) modeling offers a powerful approach to predict and optimize these activities.
Purpose of the Study:
- To develop and optimize Quantitative Structure-Activity Relationship (QSAR) models for predicting the antioxidant activity of polysaccharides.
- To identify key molecular descriptors influencing the 50% effective concentration (EC50) for DPPH-scavenging and hydroxyl radical scavenging activities.
- To compare the predictive performance of Multiple Linear Regression (MLR), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) models.
Main Methods:
- Development of QSAR models using MLR, SVM, and ANN algorithms.
- Selection of 11 molecular descriptors relevant to polysaccharide structure and activity.
- Training and testing of models using datasets for DPPH-scavenging and hydroxyl radical scavenging activities.
Main Results:
- ANN models demonstrated superior predictive accuracy for both DPPH-scavenging (R=0.96 training, R=0.933 test) and hydroxyl radical scavenging (R=0.944) activities compared to MLR and SVM.
- Key descriptors identified for DPPH-scavenging activity included Ara, GalA, GlcA, and PC.
- Fuc, Rha, uronic acid, and protein content were found to significantly impact hydroxyl radical scavenging activity.
Conclusions:
- Artificial Neural Networks (ANN) provide the most accurate QSAR models for predicting polysaccharide antioxidant activity.
- Specific monosaccharide residues (Ara, GalA, Fuc, Rha) and uronic acid content are critical determinants of antioxidant efficacy.
- QSAR modeling is a valuable tool for rational design and optimization of polysaccharides with enhanced antioxidant properties.
