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BioMed Research International
Volume 2013 (2013), Article ID 501310, 13 pages
http://dx.doi.org/10.1155/2013/501310
Research Article

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

1Biotechnology Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz 51664, Iran
2Hematology and Oncology Research Center, Tabriz University of Medical Sciences, Tabriz 51664, Iran
3Liver and Gastrointestinal Diseases Research Center, Students’ Research Committee, Tabriz University of Medical Sciences, Tabriz 51664, Iran
4Tuberculosis and Lung Disease Research Center, Tabriz University of Medical Sciences, Tabriz 51664, Iran
5Faculty of Chemistry, University of Tabriz, Tabriz 51664, Iran
6Drug Applied Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz 51664, Iran

Received 29 April 2013; Revised 16 September 2013; Accepted 25 September 2013

Academic Editor: Tatsuya Akutsu

Copyright © 2013 Somaieh Soltani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.