QSBR Study of Bitter Taste of Peptides: Application of GA-PLS in Combination with MLR, SVM, and ANN Approaches

Somaieh Soltani,Karim Asadpour Zeynali,Ali Shayanfar,Hossein Haghaei,Abolghasem Jouyban,Javad Vallipour

doi:10.1155/2013/501310

Somaieh Soltani, Karim Asadpour Zeynali + Show 4 more

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https://doi.org/10.1155/2013/501310

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Abstract

Detailed information about the relationships between structures and properties/activities of peptides as drugs and nutrients is useful in the development of drugs and functional foods containing peptides as active compounds. The bitterness of the peptides is an undesirable property which should be reduced during drug/nutrient production, and quantitative structure bitter taste relationship (QSBR) studies can help researchers to design less bitter peptides with higher target efficiency. Calculated structural parameters were used to develop three different QSBR models (i.e., multiple linear regression, support vector machine, and artificial neural network) to predict the bitterness of 229 peptides (containing 2–12 amino acids, obtained from the literature). The developed models were validated using internal and external validation methods, and the prediction errors were checked using mean percentage deviation and absolute average error values. All developed models predicted the activities successfully (with prediction errors less than experimental error values), whereas the prediction errors for nonlinear methods were less than those for linear methods. The selected structural descriptors successfully differentiated between bitter and nonbitter peptides.

Highlights

Proteins are made from peptide fragments that are well known for their nutrient, biological, and physiological roles in the human body
Findings have shown that hydrophobicity is correlated with bitterness, and a hydrophobic interaction is needed for the bitter receptors (T2Rs) to sense bitterness, whereas the amino acid sequence has no effect on bitterness [9, 10]
The principal component analysis (PCA) map of scores showed that most of the data points fell into the acceptable data space, and there is no outlier in the studied data set

Summary

Introduction

Proteins are made from peptide fragments that are well known for their nutrient, biological, and physiological roles in the human body. It allows researchers to compile data sets on their structures and properties/activities. The results of these studies are useful in the development of functional foods containing peptides as active compounds and drugs [2]. Bitter taste properties in relation with the structure of the peptides in fermented food and protein hydrolyzates have been studied. Introducing amino acids into the hydrophobic chain intensifies bitterness, and blocking both C and N terminals of peptides by acetylating increases bitterness about ten times [4]. The hydrophobic group of the side chain offers a binding site for the bitter taste receptor. Another binding site is a bulky basic group, including an αamino group. Peptides composed of eight or more amino acids do not differ in bitter potency, and they form a spherical shape rather than a helix conformation [4, 9]

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