- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Recently Accepted Articles ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
BioMed Research International
Volume 2013 (2013), Article ID 501310, 13 pages
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.
- A. H. Pripp and Y. Ardö, “Modelling relationship between angiotensin-(I)-converting enzyme inhibition and the bitter taste of peptides,” Food Chemistry, vol. 102, no. 3, pp. 880–888, 2007.
- A. H. Pripp, T. Isaksson, L. Stepaniak, T. Sørhaug, and Y. Ardö, “Quantitative structure activity relationship modelling of peptides and proteins as a tool in food science,” Trends in Food Science and Technology, vol. 16, no. 11, pp. 484–494, 2005.
- H. O. Kim and E. C. Y. Li-Chan, “Quantitative structure-activity relationship study of bitter peptides,” Journal of Agricultural and Food Chemistry, vol. 54, no. 26, pp. 10102–10111, 2006.
- K. Maehashi and L. Huang, “Bitter peptides and bitter taste receptors,” Cellular and Molecular Life Sciences, vol. 66, no. 10, pp. 1661–1671, 2009.
- E. G. Walsh, B. E. Adamczyk, K. B. Chalasani et al., “Oral delivery of macromolecules: rationale underpinning gastrointestinal permeation enhancement technology (GIPET),” Therapeutic Delivery, vol. 2, no. 12, pp. 1595–1610, 2011.
- R. C. Pinto, C. Silva, N. Martinho, et al., “Drug carriers for oral delivery of peptides and proteins: accomplishments and future perspectives,” Therapeutic Delivery, vol. 4, pp. 251–265, 2013.
- J. Renukuntla, A. D. Vadlapudi, A. Patel, et al., “Approaches for enhancing oral bioavailability of peptides and proteins,” International Journal of Pharmaceutics, vol. 447, pp. 75–93, 2013.
- S. Swain, “Stabilization and delivery approaches for protein and peptide pharmaceuticals: an extensive review of patents,” Recent Patents on Biotechnology. In press.
- K. Maehashi, M. Matano, H. Wang, L. A. Vo, Y. Yamamoto, and L. Huang, “Bitter peptides activate hTAS2Rs, the human bitter receptors,” Biochemical and Biophysical Research Communications, vol. 365, no. 4, pp. 851–855, 2008.
- K. H. Ney, “Prediction of bitterness of peptides from their amino acid composition,” Zeitschrift für Lebensmittel-Untersuchung und -Forschung, vol. 147, no. 2, pp. 64–68, 1971.
- F. Roudot-Algaron, “The taste of amino acids, peptides and proteins: examples of tasty peptides in casein hydrolysates,” Lait, vol. 76, no. 4, pp. 313–348, 1996.
- T. Matoba and T. Hata, “Relationship between bitterness of peptides and their chemical structures,” Agricultural and Biological Chemistry, vol. 36, pp. 1423–1431, 1972.
- J. Wu and R. E. Aluko, “Quantitative structure-activity relationship study of bitter di- and tri-peptides including relationship with angiotensin I-converting enzyme inhibitory activity,” Journal of Peptide Science, vol. 13, no. 1, pp. 63–69, 2007.
- G. Liang, L. Yang, L. Kang, H. Mei, and Z. Li, “Using multidimensional patterns of amino acid attributes for QSAR analysis of peptides,” Amino Acids, vol. 37, no. 4, pp. 583–591, 2009.
- Z. H. Lin, H. X. Long, Z. Bo, Y. Q. Wang, and Y. Z. Wu, “New descriptors of amino acids and their application to peptide QSAR study,” Peptides, vol. 29, no. 10, pp. 1798–1805, 2008.
- J. Tong, S. Liu, P. Zhou, B. Wu, and Z. Li, “A novel descriptor of amino acids and its application in peptide QSAR,” Journal of Theoretical Biology, vol. 253, no. 1, pp. 90–97, 2008.
- J. Yin, Y. Diao, Z. Wen, Z. Wang, and M. Li, “Studying peptides biological activities based on multidimensional descriptors (E) using support vector regression,” International Journal of Peptide Research and Therapeutics, vol. 16, no. 2, pp. 111–121, 2010.
- A. Zaliani and E. Gancia, “MS-WHIM scores for amino acids: a new 3D-description for peptide QSAR and QSPR studies,” Journal of Chemical Information and Computer Sciences, vol. 39, no. 3, pp. 525–533, 1999.
- E. R. Collantes and W. J. Dunn III, “Amino acid side chain descriptors for quantitative structure-activity relationship studies of peptide analogues,” Journal of Medicinal Chemistry, vol. 38, no. 14, pp. 2705–2713, 1995.
- S. Hellberg, L. Eriksson, J. Jonsson et al., “Minimum analogue peptide sets (MAPS) for quantitative structure-activity relationships,” International Journal of Peptide and Protein Research, vol. 37, no. 5, pp. 414–424, 1991.
- M. Cocchi and E. Johansson, “Amino acids characterization by GRID and multivariate data analysis,” Quantitative Structure-Activity Relationships, vol. 12, no. 1, pp. 1–8, 1993.
- S. Liu, C. Yin, S. Cai, and Z. Li, “A novel MHDV descriptor for dipeptide QSAR studies,” Journal of the Chinese Chemical Society, vol. 48, no. 2, pp. 253–260, 2001.
- S. Z. Li, B. Fu, Y. Wang, and S. Liu, “On structural parameterization and molecular modeling of peptide analogues by molecular electronegativity edge vector (VMEE): estimation and prediction for biological activity of dipeptides,” Journal of the Chinese Chemical Society, vol. 48, no. 5, pp. 937–944, 2001.
- P. Zhou, Y. Zhou, S. Wu, B. Li, F. Tian, and Z. Li, “A new descriptor of amino acids based on the three-dimensional vector of atomic interaction field,” Chinese Science Bulletin, vol. 51, no. 5, pp. 524–529, 2006.
- R. Ramos de Armas, H. González Díaz, R. Molina, M. Pérez González, and E. Uriarte, “Stochastic-based descriptors studying peptides biological properties: modeling the bitter tasting threshold of dipeptides,” Bioorganic & Medicinal Chemistry, vol. 12, no. 18, pp. 4815–4822, 2004.
- G. Z. Liang, P. Zhou, Y. Zhou, Q. X. Zhang, and Z. L. Li, “New descriptors of aminoacids and their applications to peptide quantitative structure-activity relationship,” Acta Chimica Sinica, vol. 64, no. 5, pp. 393–396, 2006.
- J. J. Yin, “Study of peptides QSAR based on multidimensional attributes (E) using multiple linear regression,” Advanced Materials Research, vol. 345, pp. 263–269, 2011.
- R. Todeschini and V. Consonni, Methods and Principles in Medicinal Chemistry: Molecular Descriptors for Chemoinformatics, Wiley-VCH, Weinheim, Germany, 2009.
- Organisation for economic co-operation development, “Guidance document on the validation of (quantitative) structure activity,” ENV/JM/MONO, 2, 2007.
- A. Golbraikh and A. Tropsha, “Beware of q2!,” Journal of Molecular Graphics and Modelling, vol. 20, no. 4, pp. 269–276, 2002.
- A. Tropsha, “Best practices for QSAR model development, validation, and exploitation,” Molecular Informatics, vol. 29, no. 6-7, pp. 476–488, 2010.
- P. P. Roy and K. Roy, “On some aspects of variable selection for partial least squares regression models,” QSAR and Combinatorial Science, vol. 27, no. 3, pp. 302–313, 2008.
- T. Scior, A. Bender, G. Tresadern, et al., “Recognizing pitfalls in virtual screening: a critical review,” Journal of Chemical Information and Modeling, vol. 52, pp. 867–881, 2012.
- D. S. Cao, Y. Z. Liang, Q. S. Xu, H. D. Li, and X. Chen, “A new strategy of outlier detection for QSAR/QSPR,” Journal of Computational Chemistry, vol. 31, no. 3, pp. 592–602, 2009.
- C. Nantasenamat, C. Isarankura-Na-Ayudhya, T. Naenna, and V. Prachayasittikul, “A practical overview of quantitative structure-activity relationship,” Experimental and Clinical Sciences International Journal, vol. 8, pp. 74–88, 2009.
- R. Leardi, “Application of genetic algorithm-PLS for feature selection in spectral data sets,” Journal of Chemometrics, vol. 14, pp. 643–655, 2000.
- S. Soltani, H. Abolhasani, A. Zarghi, and A. Jouyban, “QSAR analysis of diaryl COX-2 inhibitors: comparison of feature selection and train-test data selection methods,” European Journal of Medicinal Chemistry, vol. 45, no. 7, pp. 2753–2760, 2010.
- M. Shahlaei, “Descriptor selection methods in quantitative structure-activity relationship studies: a review study,” Chemical Reviews, vol. 113, no. 10, pp. 8093–8103, 2013.
- M. Jalali-Heravi, M. Asadollahi-Baboli, and P. Shahbazikhah, “QSAR study of heparanase inhibitors activity using artificial neural networks and Levenberg-Marquardt algorithm,” European Journal of Medicinal Chemistry, vol. 43, no. 3, pp. 548–556, 2008.
- J. C. Dearden, M. T. D. Cronin, and K. L. E. Kaiser, “How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR),” SAR and QSAR in Environmental Research, vol. 20, no. 3-4, pp. 241–266, 2009.
- R. Todeschini, V. Consonni, R. Mannhold, et al., Methods and Principles in Medicinal Chemistry: Handbook of Molecular Descriptors, Wiley-VCH, Weinheim, Germany, 2003.