title: QSBR study of bitter taste of peptides: application of GA-PLS in combination with MLR, SVM, and ANN approaches.
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.
authors.affiliation: Biotechnology Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz 51664, Iran.
authors.forename: Somaieh
authors.initials: S
authors.lastname: Soltani
authors.affiliation: Hematology and Oncology Research Center, Tabriz University of Medical Sciences, Tabriz 51664, Iran.
authors.forename: Hossein
authors.initials: H
authors.lastname: Haghaei
authors.affiliation: Liver and Gastrointestinal Diseases Research Center, Students' Research Committee, Tabriz University of Medical Sciences, Tabriz 51664, Iran.
authors.forename: Ali
authors.initials: A
authors.lastname: Shayanfar
authors.affiliation: Tuberculosis and Lung Disease Research Center, Tabriz University of Medical Sciences, Tabriz 51664, Iran.
authors.forename: Javad
authors.initials: J
authors.lastname: Vallipour
authors.affiliation: Faculty of Chemistry, University of Tabriz, Tabriz 51664, Iran.
authors.forename: Karim
authors.initials: K
authors.lastname: Asadpour Zeynali
authors.affiliation: Drug Applied Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz 51664, Iran.
authors.forename: Abolghasem
authors.initials: A
authors.lastname: Jouyban
mesh_terms: D000596:Amino Acids / Q000737:chemistry / Q000502:physiology; D002626:Chemistry, Pharmaceutical; D005504:Food Analysis; D006801:Humans; D016014:Linear Models; D016571:Neural Networks, Computer; D010455:Peptides / Q000008:administration & dosage* / Q000737:chemistry / Q000502:physiology; D013329:Structure-Activity Relationship; D060388:Support Vector Machine; D013649:Taste / Q000502:physiology*
chemical_list: D000596:Amino Acids; D010455:Peptides