Predicting the activity of ACE inhibitory peptides with a novel mode of pseudo amino acid composition

Protein and Peptide Letters

Volumn 18, Issue 12, 2011, Pages 1233-1243

  Shu, Mao ^a   Cheng, Xiaoming ^b   Zhang, Yunru ^a   Wang, Yuanqiang ^a   Lin, Yong ^a   Wang, Li ^a,b   Lin, Zhihua ^a  

^a CHONGQING UNIVERSITY OF TECHNOLOGY   (China)

^b THIRD MILITARY MEDICAL UNIVERSITY   (China)

Author keywords

Angiotensin I converting enzyme inhibitory peptides; Genetic algorithm; Partial least square; Physicochemical scale(P scale); Quantitative structure activity relationship

Indexed keywords

DIPEPTIDE; DIPEPTIDYL CARBOXYPEPTIDASE INHIBITOR; TETRAPEPTIDE; TRIPEPTIDE;

AMINO ACID COMPOSITION; ARTICLE; BIOLOGICAL ACTIVITY; CORRELATION COEFFICIENT; HYDROGEN BOND; HYDROPHOBICITY; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; PSEUDO AMINO ACID COMPOSITION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; STEREOSPECIFICITY;

ALGORITHMS; AMINO ACIDS; ANGIOTENSIN-CONVERTING ENZYME INHIBITORS; COMPUTATIONAL BIOLOGY; LEAST-SQUARES ANALYSIS; PEPTIDES; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 80054925990     PISSN: 09298665     EISSN: None     Source Type: Journal    
DOI: 10.2174/092986611797642706     Document Type: Article

References (89)

1. Sewald, N.; Jakubke, H.D. Peptides: chemistry and biology. Wiley-Vch Verlag GmbH & Co. KGaA, Weinheim, 2002.
2. Yang, H.Y.T.; Erdos, E.G.; Levin, Y. A dipeptidyl carboxypepti-dase that converts angiotensin II and inactivates bradykinin. Bio-chim. Biophys. Actam., 1970, 214, 374-376.
3. Wyvratt, M,J.; Patchet, A.A. Recent developments in the design of angiotensin-converting enzyme inhibitors. Med. Res. Rev., 1985, 5, 485-531.
4. Wu, J.; Aluko, R.E.; Nakai, S. Structural Requirements of Angio-tensin I-Converting EnzymeInhibitory Peptides: Quantitative Structure-Activity Relationship Study of Di-and Tripeptides. J. Agric. Food. Chem., 2006, 54, 732-738. (Pubitemid 43274109)
5. Seppo, L.; Jauhiainen, T.; Poussa, T.; Korpela, R. A fermented milk, high in bioactive peptides, has a blood pressure lowering effect in hypertensive subjects. Am. J. Clin. Nutr., 2003, 77, 326-330. (Pubitemid 36133373)
6. FitzGerald, R.J.; Murray, B.A.; Walsh, D.J. Hypotensive peptides from milk proteins. J. Nutr., 2004, 134, 980S-988S. (Pubitemid 38456275)
7. Dea-Ayuela, M. A.; Perez-Castillo, Y.; Meneses-Marcel, A.; Ubeira, F. M.; Bolas-Fernandez, F.; Chou, K. C.; Gonzalez-Diaz, H. HP-Lattice QSAR for dynein proteins: Experimental proteomics (2D-electrophoresis, mass spectrometry) and theoretic study of a Leishmania infantum sequence. Bioorg. Med. Chem., 2008, 16, 7770-7776.
8. Du, Q. S.; Huang, R. B.; Chou, K. C. Review: Recent advances in QSAR and their applications in predicting the activities of chemical molecules, peptides and proteins for drug design. Curr. Protein Pept.Sci., 2008, 9, 248-259.
9. Du, Q. S.; Huang, R. B.; Wei, Y. T.; Du, L. Q.; Chou, K. C. Multi-ple Field Three Dimensional Quantitative Structure-Activity Relationship (MF-3D-QSAR). J. Comput. Chem., 2008, 29, 211-219.
10. Du, Q. S.; Huang, R. B.; Wei, Y. T.; Pang, Z. W.; Du, L. Q.; Chou, K. C. Fragment-Based Quantitative Structure-Activity Relationship (FB-QSAR) for Fragment-Based Drug Design. J. Comput. Chem., 2009, 30, 295-304.
11. Du, Q. S.; Mezey, P. G.; Chou, K. C. Heuristic Molecular Lipophil-icity Potential (HMLP): A 2D-QSAR Study to LADH of Molecular Family Pyrazole and Derivatives. J. Comput. Chem., 2005, 26, 461-470. (Pubitemid 40449458)
12. Prado-Prado, F. J.; de la Vega, O. M.; Uriarte, E.; Ubeira, F. M.; Chou, K. C.; Gonzalez-Diaz, H. Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug-drug complex networks. Bioorg. Med. Chem., 2009, in press.
13. Prado-Prado, F. J.; Gonzalez-Diaz, H.; de la Vega, O. M.; Ubeira, F. M.; Chou, K. C. Unified QSAR approach to antimicrobials. Part 3: First multi-tasking QSAR model for Input-Coded prediction, structural back-projection, and complex networks clustering of an-tiprotozoal compounds. Bioorganic & Medicinal Chemistry, 2008, 16, 5871-5880. (Pubitemid 351766720)
14. Prado-Prado, F. J.; Martinez de la Vega, O.; Uriarte, E.; Ubeira, F. M.; Chou, K. C.; Gonzalez-Diaz, H. Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug-drug complex networks. Bioorg. Med. Chem., 2009, 17, 569-575.
15. Wei, H.; Wang, C. H.; Du, Q. S.; Meng, J.; Chou, K. C. Investigation into adamantane-based M2 inhibitors with FB-QSAR. Medicinal Chemistry, 2009, 5, 305-317.
16. Sneath, P.H. Relations between chemical structure and biological activity in peptides. J. Theor. Biol.,1966,12, 157-195
17. Hellberg, S.; Eriksson, L.; Jonsson, J.; Lindgren, F.; Sjöström, M.; Skagerberg, B.; Wold, S.; Andrews, P. Minimum analogue peptide sets (MAPS) for quantitative structure-activity relationships. Int. J. Pept. Protein. Res., 1991, 37, 414-424.
18. Cocchi, M.; Johansson, E. Amino acids characterization by GRID and multivariate data analysis. Quant. Struct-Act. Relat., 1993, 12, 1-8. (Pubitemid 23128715)
19. Collantes, E.R.; Dunn, W.J. Amino acid side chain descriptors for quantitative structure activity relationship studies of peptide analogues. J. Med. Chem., 1995, 38, 2705-2713.
20. Zaliani, A.; Gancia, E. MS-WHIM scores for amino acids: a new 3D-description for peptide QSAR and QSPR studies. J. Chem. Inf. Comput. Sci., 1999, 39, 525-533. (Pubitemid 129539208)
21. Li, S.; Fu, B.; Wang, Y. On structural parameterization and molecular modeling of peptide analogues by molecular electronegativity-edge vector (VMEE): Estimation and prediction for biological activity of pentapeptides. J. Chin. Chem. Soc., 2001, 48, 937-944. (Pubitemid 33724449)
22. Consonni, V.; Todeschini, R.; Pavan, M. Structure/response cor-rela-tions and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors. J. Chem. Inf. Comput. Sci., 2002, 42, 682-692. (Pubitemid 35355277)
23. Mei, H.; Liao, Z.H.; Zhou, Y.; Li, S.S. A New Set of Descriptors of Amino Acids and its Application in Peptide QSARs. Biopolymers, 2005, 80,775-786. (Pubitemid 43266584)
24. Lin, Z.; Long, H.; Zhu, B.; Wang, Y.; Wu, Y. New descriptors of amino acids and their application to peptide QSAR study. peptides, 2008,29, 178-1805.
25. Shu, M,; Mei, H.; Yang, S.; Liao, L.; Li, Z. Structural Parameter Characterization and Bioactivity Simulation Based on Peptide Sequence. QSAR Comb. Sci., 2009 28,27-35.
26. Yang, L.; Shu, M.; Jiang, Y.; Mei, H.; Li, Z. ST-scale as a new set of amino acid descriptors and its application in QSAM of peptides and analogues. Amino Acids., 2010, 38, 805-816
27. Chou, K. C. Review: Structural bioinformatics and its impact to biomedical science. Curr. Med. Chem., 2004, 11, 2105-2134.
28. Chou, K. C. Modelling extracellular domains of GABA-A receptors: subtypes 1, 2, 3, and 5. Biochem. Biophys. Res. Commun., 2004, 316, 636-642.
29. Chou, K. C. Molecular therapeutic target for type-2 diabetes. J. Proteome Res., 2004, 3, 1284-1288.
30. Chou, K. C. Insights from modelling the 3D structure of the extracellular domain of alpha7 nicotinic acetylcholine receptor. Bio-chem. Biophys. Res. Commun., 2004, 319, 433-438.
31. Chou, K. C. Coupling interaction between thromboxane A2 receptor and alpha-13 subunit of guanine nucleotide-binding protein. J. Proteome Res., 2005, 4, 1681-1686.
32. Chou, K. C.; Wei, D. Q.; Zhong, W. Z. Binding mechanism of coronavirus main proteinase with ligands and its implication to drug design against SARS. (Erratum: ibid., 2003, Vol.310, 675).
33. Biochem. Biophys. Res. Commun., 2003, 308, 148-151.
34. Chou, K. C. Energy-optimized structure of antifreeze protein and its binding mechanism. J. Mol. Biol., 1992, 223, 509-517.
35. Chou, K. C.; Zhou, G. P. Role of the protein outside active site on the diffusion-controlled reaction of enzyme. J. Am. Chem. Soci., 1982, 104, 1409-1413.
36. Chou, K. C. Graphic rules in steady and non-steady enzyme kinetics. J. Biol. Chem., 1989, 264, 12074-12079
37. Chou, K. C. Review: Applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady state systems. Biophysical Chemistry, 1990, 35, 1-24.
38. Chou, K. C.; Kezdy, F. J.; Reusser, F. Review: Steady-state inhibition kinetics of processive nucleic acid polymerases and nucleases. Anal. Biochem., 1994, 221, 217-230. (Pubitemid 24279327)
39. Chou, K. C. Review: Low-frequency collective motion in biomac-romolecules and its biological functions. Biophysical Chemistry, 1988, 30, 3-48.
40. Chou, K. C. A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. J. Biol. Chem., 1993, 268, 16938-16948
41. Chou, K. C. Review: Prediction of HIV protease cleavage sites in proteins. Anal. Biochem., 1996, 233, 1-14.
42. Chou, K. C.; Shen, H. B. Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. Biochem. Bio-phys. Res. Comm., 2007, 357, 633-640.
43. He, Z.; Zhang, J.; Shi, X. H.; Hu, L. L.; Kong, X.; Cai, Y. D.; Chou, K. C. Predicting drug-target interaction networks based on functional groups and biological features. PLoS one, 2010, 5, e9603
44. Chou, K. C. Review: Prediction of tight turns and their types in proteins. Anal. Biochem., 2000, 286, 1-16.
45. Huang, T.; Shi, X. H.; Wang, P.; He, Z.; Feng, K. Y.; Hu, L.; Kong, X.; Li, Y. X.; Cai, Y. D.; Chou, K. C. Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks. PLoS one, 2010, 5, e10972.
46. Chen, L.; Huang, T.; Shi, X. H.; Cai, Y. D.; Chou, K. C. Analysis of protein pathway networks using hybrid properties. Molecules, 2010, 15, 8177-8192 (Openly accessible at www.mdpi.com/journal/molecules).
47. Chen, L.; Feng, K. Y.; Cai, Y. D.; Chou, K. C.; Li, H. P. Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition. BMC Bioinformatics, 2010, 11, 293.
48. Chou, K. C.; Shen, H. B. Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization. PLoS one, 2010, 5, e11335.
49. Chou, K. C.; Shen, H. B. Cell-PLoc 2.0: An improved package of web-servers for predicting subcellular localization of proteins in various organisms. Natural Science, 2010, 2, 1090-1103 (openly accessible at http://www.scirp.org/journal/NS/).
50. Chou, K. C.; Shen, H. B. ProtIdent: A web server for identifying proteases and their types by fusing functional domain and sequential evolution information. Biochem. Biophys. Res. Comm., 2008, 376, 321-325.
51. Chou, K. C.; Shen, H. B. MemType-2L: A Web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochem. Biophys. Res. Commun, 2007, 360, 339-345.
52. Xiao, X.; Wang, P.; Chou, K. C. GPCR-2L: Predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions. Molecular Biosystems, 2011, 7, 911-919.
53. Chou, K. C.; Shen, H. B. FoldRate: A web-server for predicting protein folding rates from primary sequence. Open Bioinforma. J., 2009, 3, 31-50 (openly accessible at http://www.bentham.org/open/tobioij/).
54. Guo, J.; Rao, N.; Liu, G.; Yang, Y.; Wang, G. Predicting protein folding rates using the concept of Chou's pseudo amino acid composition. J. Comput. Chem., 2011, 32, 1612-1617.
55. Chou, K. C. Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review). J. Theor. Biol., 2011, 273, 236-247
56. Chou, K. C. Prediction of protein cellular attributes using pseudo amino acid composition. PROTEINS: Structure, Function, and Genetics (Erratum: ibid., 2001, Vol.44, 60), 2001, 43, 246-255.
57. Mohabatkar, H. Prediction of cyclin proteins using Chou's pseudo amino acid composition. Protein Pept. Lett., 2010, 17, 1207-1214.
58. Esmaeili, M.; Mohabatkar, H.; Mohsenzadeh, S. Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses. J. Theor. Biol., 2010, 263, 203-209.
59. Lin, H. The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition. J. Theor. Biol., 2008, 252, 350-356.
60. Zeng, Y. H.; Guo, Y. Z.; Xiao, R. Q.; Yang, L.; Yu, L. Z.; Li, M. L. Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covari-ance approach. J. Theor. Biol., 2009, 259, 366-372.
61. Chen, C.; Chen, L.; Zou, X.; Cai, P. Prediction of protein secondary structure content by using the concept of Chou's pseudo amino acid composition and support vector machine. Protein Pept. Lett., 2009, 16, 27-31.
62. Ding, H.; Luo, L.; Lin, H. Prediction of cell wall lytic enzymes using Chou's amphiphilic pseudo amino acid composition. Protein Pept. Lett., 2009, 16, 351-355.
63. Zhou, X. B.; Chen, C.; Li, Z. C.; Zou, X. Y. Using Chou's am-phiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes. J. Theor. Biol., 2007, 248, 546-551. (Pubitemid 47368372)
64. Hasegawa, K.; Miyashita, Y.; Funatsu, K. GA strategy forvariable selection in QSAR studies: GA based PLS analysis of calcium channel antagonists. J. Chem. Inf. Comput. Sci., 1997, 37,306-310. (Pubitemid 127603530)
65. Hasegawa, K.; Kimura, T.; Funatsu, K. GA strategy for variable selection in QSAR studies: Enhancement of comparative molecular binding energy analysis by GA-based PLS method. Quant. Struct-Act. Relat., 1999, 18, 262-272. (Pubitemid 29364073)
66. Sjöström, M.; Rännar, S.; Wieslander, Å. Polypeptide sequence property relationships in Escherichia coli based on auto cross co-variances. Chemomet. Intell. Lab. Syst., 1995, 29,295-305.
67. Wold, S.; Trygg, J.; Berglund, A.; Antti, H. Some recent developments in PLS modeling. Chemomet. Intell. Lab. Syst., 2001, 58, 131-150. (Pubitemid 33033284)
68. Tropsha, A.; Gramatica, P.; Gombar, V.K. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb. Sci., 2003, 22, 69-77. (Pubitemid 36717473)
69. Chou, K. C.; Zhang, C. T. Review: Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol., 1995, 30, 275-349.
70. Chou, K. C.; Shen, H. B. Cell-PLoc: A package of Web servers for predicting subcellular localization of proteins in various organisms. Nature Protocols, 2008, 3, 153-162. (Pubitemid 351266500)
71. Jiang, X.; Wei, R.; Zhang, T. L.; Gu, Q. Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy. Protein Pept. Lett., 2008, 15, 392-396. (Pubitemid 351679342)
72. Li, F. M.; Li, Q. Z. Predicting protein subcellular location using Chou's pseudo amino acid composition and improved hybrid approach. Protein Pept. Lett., 2008, 15, 612-616.
73. Lin, H.; Ding, H.; Feng-Biao Guo, F. B.; Zhang, A. Y.; Huang, J. Predicting subcellular localization of mycobacterial proteins by using Chou's pseudo amino acid composition. Protein Pept. Lett., 2008, 15, 739-744.
74. Lin, H.; Ding, H. Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition. J. Theor. Biol., 2011, 269, 64-69.
75. Liu, T.; Jia, C. A high-accuracy protein structural class prediction algorithm using predicted secondary structural information. J. Theor. Biol., 2010, 267, 272-275.
76. Masso, M.; Vaisman, I. I. Knowledge-based computational mutagenesis for predicting the disease potential of human non-synonymous single nucleotide polymorphisms. J. Theor. Biol., 2010, 266, 560-568.
77. Kandaswamy, K. K.; Chou, K. C.; Martinetz, T.; Moller, S.; Sugan-than, P. N.; Sridharan, S.; Pugalenthi, G. AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. J. Theor. Biol., 2011, 270, 56-62.
78. Chou, K. C.; Wu, Z. C.; Xiao, X. iLoc-Euk: A Multi-Label Classifier for Predicting the Subcellular Localization of Singleplex and Multiplex Eukaryotic Proteins. PLoS one, 2011, 6, e18258.
79. Golbraikh, A.; Tropsha, A. Beware of q2! J. Mol. Graphics. Mod., 2002 20,269-276.
80. Gramatica, P.; Pilutti, P.; Papa, E.; Validated QSAR prediction of OH tropospheric degradation of VOCs: Splitting into training-test sets and consensus modeling. J. Chem. Inf. Comput. Sci., 2004, 44, 1794-1802.
81. Eriksson, L.; Johansson, E.; Miiller, M,; Wold, S. Cluster-based design in environmental QSAR. Quant. Struct. Act. Relat., 1997,16, 383-390. (Pubitemid 27520617)
82. Wold, S.; Sjostrom, M.; Ericksson, L. PLS-regression: A basic tool of chemometrics. Chemom. Intell. Lab. Syst., 2001, 58, 109-130. (Pubitemid 33033283)
83. Cheung, H.S.; Wang, F.L.; Ondetti, M.A.; Sabo, E F.; Cushman, D.W. Binding of peptide substrates and inhibitors of angiotensin-cnverting enzyme: importance of the COOH terminal dipeptide sequence. J. Biol. Chem. 1980, 255, 401-407.
84. Wu, J.; Aluko, R. Quantitative structure-activity relationship study of bitter di-and tri-peptides including relationship with angiotensin I-converting enzyme inhibitory activity. J. Pept. Sci., 2007, 13, 63-69. (Pubitemid 46190035)
85. Redelinghuys, P.; Nchinda, A.T.; Sturrock, E.D. Development of Domain-Selective Enzyme Inhibitors. New York Academy of Science, 2005, 1056: 160-175.
86. Almquist, R.G.; Chao, W.R.; Ellis, M.E.; Johnson, H.L. Synthesis and Biological Activity of a Ketomethylene Analogue of a Tripep-tide Inhibitor of Angiotensin-Converting Enzyme. J. Med.Chem., 1980, 23, 1392-1398. (Pubitemid 11164109)
87. Clive, D.L.J.; Yang, H.; Lewanczuk, E.Z. Synthesis and in vitro activity of a non-epimerizable analog of the angiotensin-converting enzyme inhibitor A58365A. Chemistry, 2001, 4, 505-512. (Pubitemid 33376684)
88. Wu, J.; Aluko, R.E.; Nakaib, S. Structural Requirements of Angio-tensin I-Converting Enzyme Inhibitory Peptides: QuantitativeStruc-ture-Activity Relationship Modeling of Peptides Containing 4-10 Amino Acid Residues. QSAR Comb. Sci., 2006, 25, 873-880 (Pubitemid 44621664)
89. Chou, K. C.; Shen, H. B. Review: recent advances in developing web-servers for predicting protein attributes. Natural Science, 2009, 2, 63-92 (openly accessible at http://www.scirp.org/journal/NS/).