Amino Acids

, Volume 40, Issue 4, pp 1169–1183 | Cite as

Novel amino acids indices based on quantum topological molecular similarity and their application to QSAR study of peptides

  • Bahram HemmateenejadEmail author
  • Saeed Yousefinejad
  • Ahmad Reza Mehdipour
Original Article


A new source of amino acid (AA) indices based on quantum topological molecular similarity (QTMS) descriptors has been proposed for use in QSAR study of peptides. For each bond of the chemical structure of AA, eight electronic properties were calculated using the approaches of bond critical point and theory of atom in molecule. Thus, for each molecule a data matrix of QTMS descriptors (having information from both topology and electronic features) were calculated. Using four different criterion based on principal component analysis of the QTMS data matrices, four different sets of AA indices were generated. The indices were used as the input variables for QSAR study (employing genetic algorithm-partial least squares) of three peptides’ data sets, namely, angiotensin-converting enzyme inhibitors, bactericidal peptides and the peptides binding to the HLA-A*0201 molecule. The obtained models had better prediction ability or a comparable one with respect to the previously reported models. In addition, by using the proposed indices and analysis of the variable important in projection, the active site of the peptides which plays a significant role in the biological activity of interest, was identified.


Amino acid indices QTMS QSAR Peptide 



Financial support of this project by Research councils of Shiraz University and Shiraz University of Medical Sciences is appreciated.

Supplementary material

726_2010_741_MOESM1_ESM.doc (469 kb)
Supplementary material 1 (DOC 469 kb)


  1. Alsberg BK, Marchand-Geneste N, King RD (2000) A new 3D molecular structure representation using quantum topology with application to structure–property relationships. Chemom Intell Lab Syst 54:75–91CrossRefGoogle Scholar
  2. Alsberg BK, Marchand-Geneste N, King RD (2001) Modeling quantitative structure–property relationships in calculated reaction pathways using a new 3D quantum topological representation. Anal Chim Acta 446:3–13CrossRefGoogle Scholar
  3. Bader RFW (1990) Atoms in molecules: a quantum theory. Oxford University Press, OxfordGoogle Scholar
  4. Bader RFW, Preston HJT (1969) The kinetic energy of molecular charge distributions and molecular stability. Int J Quantum Chem 3:327–347CrossRefGoogle Scholar
  5. Bader RFW, Nguyen-Dang TT, Tal Y (1981) A topological theory of molecular structure. Rep Prog Phys 44:893–948CrossRefGoogle Scholar
  6. Baumann K (2003) Cross-validation as the objective function for variable-selection techniques. Trends Anal Chem 22:395–406CrossRefGoogle Scholar
  7. Baumann K (2005) Chance correlation in variable subset regression: influence of the objective function, the selection mechanism, and ensemble averaging. QSAR Comb Sci 24:1033–1046CrossRefGoogle Scholar
  8. Bytheway I, Popelier PLA, Gillespie RJ (1996) Topological studies of the charge density of some group 2 metallocenes M(η5–C5H5)2 (M = Mg or Ca). Can J Chem 74:1059–1071CrossRefGoogle Scholar
  9. Chaudry UA, Popelier PLA (2003) Ester hydrolysis rate constant prediction from quantum topological molecular similarity (QTMS) descriptors. J Phys Chem A 107:4578–4582CrossRefGoogle Scholar
  10. Doytchinova IA, Walshe V, Borrow P, Flower DR (2005) Towards the chemometric dissection of peptide–HLA-A*0201 binding affinity: comparison of local and global QSAR models. J Comput-Aided Mol Des 19:203–212PubMedCrossRefGoogle Scholar
  11. Du QS, Huang RB, Chou KC (2008) Recent advances in QSAR and their applications in predicting the activities of chemical molecules, peptides and proteins for drug design. Curr Protein Pept Sci 9:248–260PubMedCrossRefGoogle Scholar
  12. Erikson L, Johansson E, Kettaneh-Wold N, Wold S (2001) Multi- and mega-variate data analysis. Principle and applications. Umetrics Academy, UmeaGoogle Scholar
  13. Hansch C, Hoekman D, Leo A, Weininger D, Selassie CD (2002) Chem-bioinformatics: comparative QSAR at the interface between chemistry and biology. Chem Rev 102:783–812PubMedCrossRefGoogle Scholar
  14. Harding AP, Wedge DC, Popelier PLA (2009) pKa prediction from “Quantum Chemical Topology” descriptors. J. Chem Inf Model 49:1914–1924PubMedCrossRefGoogle Scholar
  15. Hemmateenejad B (2005) Correlation ranking procedure for factor selection in PC-ANN modeling and application to ADMETox evaluation. Chemom Intell Lab Syst 75:231–245CrossRefGoogle Scholar
  16. Hemmateenejad B, Mohajeri A (2007) Application of quantum topological molecular similarity descriptors in QSPR study of the O-methylation of substituted phenols. J Comput Chem 29:266–274CrossRefGoogle Scholar
  17. Hemmateenejad B, Akhond M, Miri R, Shamsipur M (2003) Genetic algorithm applied to the selection of factors in principal component-artificial neural networks: application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyridines (nifedipine analogous). J Chem Inf Comput Sci 43:1328–1334PubMedGoogle Scholar
  18. Hemmateenejad B, Mehdipour AR, Popelier PLA (2008) Quantum topological QSAR models based on the MOLMAP approach. Chem Biol Drug Des 72:551–563PubMedCrossRefGoogle Scholar
  19. Jenssen H, Gutteberg TJ, Rekdal Ø, Lejon T (2006) Prediction of activity, synthesis and biological testing of anti-HSV active peptides. Chem Biol Drug Des 68:58–66PubMedCrossRefGoogle Scholar
  20. Lin ZH, Long HX, Bo Z, Wang YQ, Wu YZ (2008) New descriptors of amino acids and their application to peptide QSAR study. Peptides 29:1798–1805PubMedCrossRefGoogle Scholar
  21. Mauri A, Ballabio D, Consonni V, Managanaro A, Todeschini R (2008) Peptides multivariate characterization using a molecular based approach. MATCH Commun Math Comput Chem 60:671–690Google Scholar
  22. Mehdipour AR, Hemmateenejad B, Miri R (2007) QSAR studies on the anesthetic action of some polyhalogenated ethers. Chem Biol Drug Des 69:362–368PubMedCrossRefGoogle Scholar
  23. Mei H, Zhou Y, Sun LL, Li ZL (2004) A new descriptor of amino acid and its application in peptide QSAR. Acta Phys Chim Sin 20:821–825Google Scholar
  24. Mohajeri A, Hemmateenejad B, Mehdipour A, Miri R (2008) Modeling calcium channel antagonistic activity of dihydropyridine derivatives using QTMS indices analyzed by GA-PLS and PC-GA-PLS. J Mol Graph Model 26:1057–1065PubMedCrossRefGoogle Scholar
  25. O’Brien SE, Popelier PLA (2001) Quantum molecular similarity 3. QTMS descriptors. J Chem Inf Comput Sci 41:764–775PubMedGoogle Scholar
  26. O’Brien SE, Popelier PLA (2002) Quantum topological molecular similarity. Part 4. A QSAR study of cell growth inhibitory properties of substituted (E)-1-phenylbut-1-en-3-ones. J Chem Soc Perkin Trans 2:478–483Google Scholar
  27. Padron-Garcia JA, Alonso-Tarajano M, Alonso-Becerra E, Winterburn TJ, Yasser R, Kay J, Berry C (2009) Quantitative structure activity relationship of IA3-like peptides as aspartic proteinase inhibitors. Proteins 75:859–869PubMedCrossRefGoogle Scholar
  28. Popelier PLA, Smith PJ (2006) QSAR models based on quantum topological molecular similarity. Eur J Med Chem 41:862–873PubMedCrossRefGoogle Scholar
  29. Popelier PLA, Chaudry UA, Smith PJ (2002) Quantum topological molecular similarity. Part 5: further development with an application to the toxicity of polychlorinated dibenzo-p-dioxins (PCDDs). J Chem Soc Perkin II:1231–1237Google Scholar
  30. Popelier PLA, Chaudry UA, Smith PJ (2004) Quantitative structure–activity relationships of mutagenic activity from quantum topological descriptors: triazenes and halogenated hydroxyfuranones (mutagen-X) derivatives. J Comput-Aided Mol Des 18:709–718PubMedCrossRefGoogle Scholar
  31. Rafat M, Shaik M, Popelier PLA (2006) Transferability of quantum topological atoms in terms of electrostatic interaction energy. J Phys Chem A 110:13578–13583PubMedCrossRefGoogle Scholar
  32. Ramos de Armas R, González Díaz H, Molina R, Uriarte E (2005) Stochastic-based descriptors studying biopolymers biological properties: extended MARCH-INSIDE methodology describing antibacterial activity of lactoferricin derivatives. Biopolymers 77:247–256CrossRefGoogle Scholar
  33. Roy K, Popelier PLA (2008a) Exploring predictive QSAR models using quantum topological molecular similarity (QTMS) descriptors for toxicity of nitroaromatics to Saccharomyces cerevisiae. QSAR Comb Sci 27:1006–1012CrossRefGoogle Scholar
  34. Roy K, Popelier PLA (2008b) Exploring predictive QSAR models for hepatocyte toxicity of phenols using QTMS descriptors. Bioorg Med Chem Lett 18:2604–2609PubMedCrossRefGoogle Scholar
  35. Selassie CD, Mekapati SB, Verma RP (2002) QSAR: then and now. Curr Top Med Chem 2:1357–1379PubMedCrossRefGoogle Scholar
  36. Tian F, Yang L, Lv F, Yang Q, Zhou P (2009) In silico quantitative prediction of peptides binding affinity to human MHC molecule: an intuitive quantitative structure–activity relationship approach. Amino Acids 36:535–554PubMedCrossRefGoogle Scholar
  37. Tong J, Liu S, Zhou P, Wu B, Li Z (2008) A novel descriptor of amino acids and its application in peptide QSAR. J Theor Biol 253:90–97PubMedCrossRefGoogle Scholar
  38. Wold S, Sjostrom M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58:109–130CrossRefGoogle Scholar
  39. Zhou P, Chen X, Wu Y, Shang Z (2010) Gaussian process: an alternative approach for QSAM modeling of peptides. Amino Acids 38:199–212PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Bahram Hemmateenejad
    • 1
    • 2
    Email author
  • Saeed Yousefinejad
    • 1
    • 2
  • Ahmad Reza Mehdipour
    • 2
  1. 1.Department of ChemistryShiraz UniversityShirazIran
  2. 2.Medicinal & Natural Products Chemistry Research CenterShiraz University of Medical SciencesShirazIran

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