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A novel vector of topological and structural information for amino acids and its QSAR applications for peptides and analogues

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Abstract

A new descriptor, called vector of topological and structural information for coded and noncoded amino acids (VTSA), was derived by principal component analysis (PCA) from a matrix of 66 topological and structural variables of 134 amino acids. The VTSA vector was then applied into two sets of peptide quantitative structure-activity relationships or quantitative sequence-activity modelings (QSARs/QSAMs). Molded by genetic partial least squares (GPLS), support vector machine (SVM), and immune neural network (INN), good results were obtained. For the datasets of 58 angiotensin converting enzyme inhibitors (ACEI) and 89 elastase substrate catalyzed kinetics (ESCK), the R 2, cross-validation R 2, and root mean square error of estimation (RMSEE) were as follows: ACEI, R 2cu ⩾0.82, Q 2cu ⩾0.77, E rmse⩽0.44 (GPLS+SVM); ESCK, R 2cu ⩾0.84, Q 2cu ⩾0.82, E rmse⩽0.20 (GPLS+INN), respectively.

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References

  1. Kidera A, Konishi Y, Oka M. A statistical analysis of the physical properties of the 20 naturally occuring amino acids. J Protein Chem, 1985, 4: 23–55

    Article  CAS  Google Scholar 

  2. 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

    Article  CAS  Google Scholar 

  3. Liu S S, Cai S X, Yin C S. A novel MHDV descriptor for dipeptide QSAR studies. J Chin Chem Soc, 2001, 48: 253–260

    CAS  Google Scholar 

  4. Raychaudhury C, Banerjee A, Bag P. Topological shape and size of peptides: Identification of potential allele specific helper T cell antigenic sites. J Chem Inf Comput Sci, 1999, 39: 248–254

    CAS  Google Scholar 

  5. Anfinsen C B, Haber E, Sela M. The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain. Proc Natl Acad Sci USA, 1961, 47: 1309–1318

    Article  CAS  Google Scholar 

  6. Sneath P H. Relations between chemical structure and biological activity in peptides. J Theor Biol, 1966, 12: 157–195

    Article  CAS  Google Scholar 

  7. Hellberg S, Sjostrom M, Wold S. The prediction of bradykinin potentiating potency of pentapeptides. An example of a peptide quantitative structure-activity relationship. Acta Chem Scand, 1986, 40: 135–140

    Article  CAS  Google Scholar 

  8. Hellberg S, Sjostrom M, Skagerberg B. Peptide quantitative structure-activity relationships, a multivariate approach. J Med Chem, 1987, 30: 1126–1135

    Article  CAS  Google Scholar 

  9. 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

    Article  CAS  Google Scholar 

  10. Jonsson J, Eriksson L, Hellberg S. Multivariate parameterization of 55 coded and non-coded amino acids. Quant Strut-Act Relat, 1989, 8: 204–209

    Article  CAS  Google Scholar 

  11. Sandberg M, Eriksson L, Jonsson J. New chemical descriptors for the design of biologically active peptides. A multivariate characterization of 87 amino acids. J Med Chem, 1998, 41: 2481–2491

    Article  CAS  Google Scholar 

  12. Winer H. Structural determination of paraffin boiling point. J Am Chem Soc, 1947, 69: 2636–2641

    Article  Google Scholar 

  13. Hosoya H. Topological index. A new proposed quantity characterizing the topological nature of structural isomers of saturated hydrocarbons. Bull Chem Soc, 1971, 44: 2332–2339

    Article  CAS  Google Scholar 

  14. Randic M. On characterization of molecular branching. J Am Chem Soc, 1975, 97: 6609–6615

    Article  CAS  Google Scholar 

  15. Balaban A T. High discrimination distance-based topological index. Chem Phys Lett, 1982, 89: 399–404

    Article  CAS  Google Scholar 

  16. Kier L B, Hall L H. Molecular Connectivity in Structure-activity Analysis. New York: J Wiley & Sons, 1986

    Google Scholar 

  17. Bonchev D, Mekenjan O, Protic G. Application of topological indices to gas chromatographic data: Calculation of the retention indices of isomeric alkylbenzenes. J Chromatogr, 1979, 176: 149–156

    Article  CAS  Google Scholar 

  18. Buydens L, Massart D L, Geerlings P. Prediction of gas chromatographic retention indexes with topological, physicochemical, and quantum-chemical parameters. Anal Chem, 1983, 55: 738–744

    Article  CAS  Google Scholar 

  19. Call D J. Molecular connectivity in chemistry and drug research. Environ Toxicol Chem, 1995, 4: 10–19

    Google Scholar 

  20. Basak A C, Gute B D, Grunwald G D. Comparative study of topological and geometrical parameters in estimating normal boiling points and octanol/water partition coefficient. J Chem Inf Comput Sci, 1996, 36: 1054–1060

    Article  CAS  Google Scholar 

  21. Bock A, Forchhammer K, Heider J. Selenocysteine: The 21st amino acid. Mol Microbiol, 1991, 5: 515–520

    Article  CAS  Google Scholar 

  22. Atkins J F, Gesteland R. The 22nd amino acid. Science, 2002, 296: 1409–1410

    Article  CAS  Google Scholar 

  23. Yan A X, Tian G L, Ye Y H. Progress in modification of bioactive peptides with non-protein amino acids and their application in the studies of structure-activity relationship. Chin J Org Chem (in Chinese), 2000, 20(3): 299–305

    CAS  Google Scholar 

  24. Chambers I, Frampton J, Goldfarb P. The structure of the mouse glutathione peroxidase gene: The selenocysteine in the active site is encoded by the termination codon TGA. EMBO J, 1986, 5: 1221–1227

    CAS  Google Scholar 

  25. Srinivasan G, James C M, Krzycki J K. Pyrrolysine encoded by UGA in archaea: Charging of a UAG-decoding specialized tRNA. Science, 2002, 296: 1459–1462

    Article  CAS  Google Scholar 

  26. Cho S J, Zheng W, Tropsha A. Rational combinatorial library design. 2. Rational design of targeted combinatorialn peptide libraries using chemical similarity probe and the inverse QSAR approaches. J Chem Inf Comput Sci, 1998, 38: 259–268

    Article  CAS  Google Scholar 

  27. He Y Y, Zhu F L, Zhong B L. Artificial neural networks design and implementation. Based on evolutionary computation. Control and Decision (in Chinese), 2001, 16(3): 257–262

    Google Scholar 

  28. Montana D J, Davis L. Training feedforward neural networks using genetic algorithms. In: Proceedings of the International Joint Conference on Neural Network (IJCNN), 1989

  29. De Weijer A P, Lucasius C B, Buydens L M C. Using genetic algorithms for an artificial neural network model inversion. Chemom Intell Lab Syst, 1993, 20: 45–55

    Article  Google Scholar 

  30. van den Bergh F, Engelbrecht A P. Training product unit networks using cooperative particle swarm optimizers. In: Proceedings of the Third Genetic and Evolutionary Computation Conference, 2001

  31. Wang L, Pan J, Jiao L C. Immune algorithm. J Electron (in Chinese), 2000, 28(7): 74–78

    Google Scholar 

  32. De Castro L N, Von Zuben F J. The clonal selection algorithm with engineering applications. Genetic and Evolutionary Computation Conference, Las Vegas, USA, 2000. 36–37

  33. Toma N, Endo S, Yamada K. The immune distributed competitive problem solver with major histocompatibility complex and immune network. IEEE International Conference on Systems, Man, and Cybernetics, Nashville TN USA, 2000, 3: 1865–1870

    Google Scholar 

  34. Rogers D, Hopfinger A J. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J Chem Inf Comput Sci, 1994, 34: 854–866

    Article  CAS  Google Scholar 

  35. Zhang X G. Introduction to statistical learning theory and support vector machines. Acta Automatica Sinica (in Chinese), 2000, 26(1): 32–42

    Google Scholar 

  36. Hassell C H. The Design and synthesis of new triazolo-, pyrazolo-, and pyridazo-pyridazine derivatives as inhibitors of angiotensin converting enzyme. J Chem Soc Perkin Trans I, 1984, 23: 155–162

    Article  Google Scholar 

  37. Hellberg S, Eriksson L, Jonsson J, Lindgren F, Sjoström 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

    CAS  Google Scholar 

  38. Cocchi M, Johansson E. Amino acids characterization by GRID and multivariate data analysis. Quant Struct Act Relat, 1993, 12: 1–8

    Article  CAS  Google Scholar 

  39. Li S Z, Fu B H, Wang Y Q. 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

    CAS  Google Scholar 

  40. Zhou P, Zhou Y, Wu S R, Tian F F, Li Z L. A new descriptor of amino acids based on the three-dimensional vector of atomic interaction field. Chin Sci Bull, 2006, 51(1): 34–39; 2006, 51(5): 524–52

    Google Scholar 

  41. Mei H, Zhou Y, Li, S Z. A new descriptor of amino acids and its application in peptide QSARs. Peptide Science, 2005, 80: 775–786

    CAS  Google Scholar 

  42. Mei H, Zhou Y, Sun L L, Li Z L. A New descriptor of amino acids and its application in peptide QSAR. Acta Phys Chim Sinica (in Chinese), 2004, 20(8): 821–825

    CAS  Google Scholar 

  43. Snider G L. Animal models of emphysema. Am Rew Respit Dis, 1986, 133: 149–150

    CAS  Google Scholar 

  44. Nomizu M, Iwaki T, Yamashita T, Inagaki Y, Asano K, Aka matsu M, Fujita T. Quantitative structure-activity relationship (QSAR) study of elastase substrates and inhibitors. Int J Pept Protein Res, 1993, 42: 216–226

    CAS  Google Scholar 

  45. Kimura T, Miyashita Y, Funatsu K. Quantitative structure-activity relationships of the synthetic substrates for elastase enzyme using nonlinear partial least squares regression. J Chem Inf Comput Sci, 1996, 36: 185–189

    Article  CAS  Google Scholar 

  46. Zhang H X, Guo J L, Zhu J Y. Multivariate Data Analysis Methods and Applications with Few Observations (in Chinese). Xi’an: Northwest Polytechnical University Press, 2002. 30–32

    Google Scholar 

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Correspondence to Hu Mei.

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Supported by the Foundations of National High Technology (863) Programme (Grant No. 2006AA02Z312), State New Drug Project (Grant No. 1996ND1035A01), Fok-Yingtung Educational Foundation (Grant No. 980706), State Key Laboratory of Chemo/Biosensing and Chemometrics Foundation (Grant No. KLCB005-0012), Chongqing University Innovation Fund (Grant No. CUIF030506), Chongqing Municipality Applied Science Fund (Grant No. CASF01-3-6) and Momentous Juche Innovation Fund for Tackle Key Problem Items (Grant No. MJIF 06-9-9)

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Li, Z., Li, G., Shu, M. et al. A novel vector of topological and structural information for amino acids and its QSAR applications for peptides and analogues. Sci. China Ser. B-Chem. 51, 946–957 (2008). https://doi.org/10.1007/s11426-008-0040-5

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  • DOI: https://doi.org/10.1007/s11426-008-0040-5

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