A Simple, Yet Rapid and Effective Method for LogP Prediction of Dipeptides Based on Theoretical Descriptors (HMLP)
Автор: Jiajian Yin, Yong Liu
Журнал: International Journal of Engineering and Manufacturing(IJEM) @ijem
Статья в выпуске: 1 vol.1, 2011 года.
Бесплатный доступ
The hydrophobicity of peptide is an important factor that affects the dissolution behavior of proteins and peptides, also affect the physical and chemical properties. In this study, each amino acid side chain was characterized using three structure parameters (heuristic molecular lipophilicity potential, HMLP). The HMLP parameters, total surface area(S), lipophilic indices (L), and hydrophilic indices (H) of amino acid side chains are derived from theoretical computation. Based on HMLP descriptors, QSAR models of the logP were constructed for blocked and unblocked dipeptides by multiple linear regression (MLR) and support vector regression (SVR). All the results showed that the logP relates to the total surface area(S) and hydrophilic indices (H), and the prediction results of SVR are better than that of MLR. The prediction results are in agreement with the experimental values. The result shows HMLP parameters (S,L,H) could preferably describe the structure features of the peptides responsible for their octanol to water partition behavior.
HMLP descriptors, peptides, logP, QSAR, support vector regression
Короткий адрес: https://sciup.org/15014108
IDR: 15014108
Список литературы A Simple, Yet Rapid and Effective Method for LogP Prediction of Dipeptides Based on Theoretical Descriptors (HMLP)
- M.Akamatsu, Y.Yoshida, H.Nakamura, et al., “Hydrophobicity of di- and tripeptides having unionizable side chains and correlation with substituent and structural parameters”, Quant. Struct.-Act. Relat., 1989, 8, pp.195-203.
- M.Akamatsu, S.Okutani, K.Nakao, et al., “Hydrophobicity of N-acetyl-di- and tripeptide amides having unionizable side chains and correlation with substituent and structural parameters”, Quant. Struct.-Act. Relat., 1990,9, pp.189-194.
- M.Akamatsu, and T.Fujita, “Quantitative analyses of hydrophobicity of di- to pentapeptides having un-ionizable side chains with substituent and structural parameters”, J.Pharm. Sci., 1992, 81(2), pp.164-174.
- M.Akamatsu, T.Katayama, D.Kishimoto, et al., “Quantitative analyses of the structure-hydrophobicity relationship for N-acetyl di- and tripeptide amides”, J.Pharm. Sci., 1994, 83(7), pp.1026-1033.
- S.N.Tomoko, O.Akio, “Evaluation of the hydrophobic parameters of the amino acid side chains of peptides and their application in QSAR and conformational studies”, J.Mol. Struct. -Theochem, 1997, 392, pp.43-54.
- P.Tao, R.X.Wang, L.H.Lai, “Calculating Partition Coefficients of Peptides by the Addition method”, J.Mol. Model., 1999,5(10), pp.189-195.
- N.Gulyaeva, A.Zaslavsky, A.Chait, et al., “Relative hydrophobicity of di- to hexapeptides as measured by aqueous two-phase partitioning”, J. Pept. Res., 2003, 61(3), pp.129-139.
- J. T.Sarah, K. H.Channa, D. H.John, et al., “On the hydrophobicity of peptides: Comparing empirical predictions of peptide log P values”, Bioinformation, 2006, 1(7), pp. 237-241.
- K. H.Channa and R. F.Darren, “Empirical prediction of peptide octanol-water partition coefficients”, Bioinformation, 2006, 1(7), pp. 257-259.
- Q.S., Du, D.P. Li, W.Z. He, et al., “Heuristic molecular lipophilicity potential (HMLP): Lipophilicity and hydrophilicity of amino acid side chains”, J. Comput. Chem., 2006, 27(6), pp. 685-692.
- Q.S. Du, R.B. Huang, Y.T.Wei, et al., “Peptide reagent design based on physical and chemical properties of amino acid residues”, J. Comput. Chem., 2007, 28(12), pp. 2043-2050.
- Huang, R.B., Du, Q.S., Wei, Y.T., et al., Physics and chemistry-driven artificial neural network for predicting bioactivity of peptides and proteins and their design.. Journal of Theoretical Biology, 2009, 256(3):428-435
- J.S.Alex, S.Bernhrd, ”A tutorial on support vector regression”, Stat. Comput., 2004, 14, pp.199-222.
- N.V. Vapnik, “Statistical learning theory”, Beijing: Publishing House of Electronics Industry, 2004.
- L.Eriksson, E.Johansson, N.Kettaneh-Wold, et al., “Multi- and megavariate data analysis: principle and application ’, Umea, Sweden: Umetrics AB. 2001.
- R.E.Fan, P.H.Chen, C.J.Lin, “Working set selection using the second order information for training SVM”, J. Mach. Learn. Res., 2005, 6, pp.1889-1918 or http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
