Mathematics in Computer Science

, Volume 11, Issue 2, pp 197–208 | Cite as

Advanced Protein Alignments Based on Sequence, Structure and Hydropathy Profiles; The Paradigm of the Viral Polymerase Enzyme

  • Dimitrios Vlachakis
  • Alexandros Armaos
  • Sophia Kossida
Article

Abstract

One of the major drawbacks of modern bioinformatics is the fact that protein similarity and blast searches are still based on primary amino acid sequence rather than structural data. Primary sequence searches are inadequate, as they fail to provide a realistic fingerprint for the query protein. Protein function is much more related to protein structure rather than to its amino acid sequence. After all structure is much more conserved than sequence in nature. In this direction and in an effort to bridge this flaw, a novel platform has been developed, which is capable of performing fast similarity searches using protein primary and secondary structural information. The protein secondary structure profile (PSSP) tool is capable of performing conventional blast searches, based on protein sequences, as well as alignments based on a custom made hydropathy substitution matrix that takes into account the physicochemical profile of the amino acids that compose the query protein. Moreover, PSSP is capable of efficiently exploiting protein secondary structural information from the PDB database when available. If the query protein is not indexed in the RCSB PDB database, it will automatically determine the secondary elements of the given protein by performing an ‘on the fly’ secondary structure prediction. All query proteins are then blasted against the RCSB PDB secondary elements database. Hits are scored, ranked and returned to the user via a well-organized and user friendly graphical interface.

Keywords

Similarity Algorithms Structure Physicochemistry Evolution Genetics 

Mathematics Subject Classification

28 40 62 68 92 

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References

  1. 1.
    Allen, M.P., Tildesley, D.J.: Computer Simulation of Liquids, reprint edn. Oxford University Press, Oxford (1989)MATHGoogle Scholar
  2. 2.
    Bond, S.D., Leimkuhler, B.J., Laird, B.B.: The NoséPoincaré method for constant temperature molecular dynamics. J. Comput. Phys. 151(1), 114–134 (1999)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Bowie, J.U., Reidhaar-Olson, J.F., Lim, W.A., Sauer, R.T.: Deciphering the message in protein sequences: tolerance to amino acid substitutions. Science 247(4948), 1306–1310 (1990)CrossRefGoogle Scholar
  4. 4.
    Carvalho, C.S., Vlachakis, D., Tsiliki, G., Megalooikonomou, V., Kossida, S.: Protein signatures using electrostatic molecular surfaces in harmonic space. PeerJ 1, e185 (2013)CrossRefGoogle Scholar
  5. 5.
    Chothia, C.: The nature of the accessible and buried surfaces in proteins. J. Mol. Biol. 105(1), 1–12 (1976)CrossRefGoogle Scholar
  6. 6.
    Gill, P.E., Murray, W., Wright, M.H.: Practical Optimization. Academic Press, London (1982)MATHGoogle Scholar
  7. 7.
    Gille, C.: STRAP: Structure based sequences alignment program. http://www.bioinformatics.org/strap/index2.html
  8. 8.
    Gille, C.: Molecular Operating Environment (MOE) (2013). http://www.chemcomp.com
  9. 9.
    Hekkelman, M.L., Te Beek, T.A., Pettifer, S.R., Thorne, D., Attwood, T.K., Vriend, G.: WIWS: a protein structure bioinformatics web service collection. Nucleic Acids Res. 38(Web Server issue), 719–723 (2010)CrossRefGoogle Scholar
  10. 10.
    Hess, B., Kutzner, C., van der Spoel, D., Lindahl, E.: GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J. Chem. Theory Comput. 4(3), 435–447 (2008)CrossRefGoogle Scholar
  11. 11.
    Hooft, R., Sander, C., Scharf, M., Vriend, G.: The pdbfinder database: a summary of pdb, dssp and hssp information with added value. Comput. Appl. Biosci. CABIOS 12(6), 525–529 (1996)Google Scholar
  12. 12.
    Kabsch, W., Sander, C.: Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12), 2577–2637 (1983)CrossRefGoogle Scholar
  13. 13.
    Kamtekar, S., Schiffer, J.M., Xiong, H., Babik, J.M., Hecht, M.H.: Protein design by binary patterning of polar and nonpolar amino acids. Science 262(5140), 1680–1685 (1993)CrossRefGoogle Scholar
  14. 14.
    Krissinel, E.: Enhanced fold recognition using efficient short fragment clustering. J. Mol. Biochem. 1(2), 76–85 (2012)Google Scholar
  15. 15.
    Kyte, J., Doolittle, R.F.: A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157(1), 105–132 (1982)CrossRefGoogle Scholar
  16. 16.
    Lampe, E., de Oliveira, J.M., Pereira, J.L., Saback, F.L., Yoshida, C.F., Niel, C.: Hepatitis G virus (GBV-C) infection among Brazilian patients with chronic liver disease and blood donors. Clin. Diagn. Virol. 9(1), 1–7 (1998)CrossRefGoogle Scholar
  17. 17.
    Lefranc, M.P., Giudicelli, V., Ginestoux, C., Bodmer, J., Muller, W., Bontrop, R., Lemaitre, M., Malik, A., Barbie, V., Chaume, D.: IMGT, the international ImMunoGeneTics database. Nucleic Acids Res. 27(1), 209–212 (1999)CrossRefGoogle Scholar
  18. 18.
    Pánek, J., Eidhammer, I., Aasland, R.: A new method for identification of protein (sub)families in a set of proteins based on hydropathy distribution in proteins. Proteins 58(4), 923–934 (2005). 03CrossRefGoogle Scholar
  19. 19.
    Stewart, J.J.: MOPAC: a semiempirical molecular orbital program. J. Comput. Aided Mol. Des. 4(1), 1–105 (1990)CrossRefGoogle Scholar
  20. 20.
    Sturgeon, J., Laird, B.: Symplectic algorithm for constant-pressure molecular dynamics using a Nosé–Poincaré thermostat. J. Chem. Phys. 112, 3474–3482 (2000)CrossRefGoogle Scholar
  21. 21.
    Verlet, L.: Computer “Experiments” on classical fluids. I. Thermodynamical properties of Lennard–Jones molecules. Phys. Rev. Online Arch. (Prola) 159(1), 98–103 (1967)Google Scholar
  22. 22.
    Vlachakis, D., Tsagkrasoulis, D., Megalooikonomou, V., Kossida, S.: Introducing drugster: a comprehensive and fully integrated drug design, lead and structure optimization toolkit. Bioinformatics 29(1), 126–128 (2013)CrossRefGoogle Scholar
  23. 23.
    Vlachakis, D., Tsagkrasoulis, D., Tsiliki, G., Kossida, S.: The future of structural bioinformatics in the post-genomic era. EMBnet.journal 18(1), 3–5 (2012)Google Scholar
  24. 24.
    Vlachakis, D., Tsaniras, S.C., Feidakis, C., Kossida, S.: An in silico 3D study of the biglycan core protein, using homology modelling techniques. J. Mol. Biochem. 2(2), 85–93 (2013)Google Scholar
  25. 25.
    Vlachakis, D., Tsiliki, G., Tsagkrasoulis, D., Carvalho, C.S., Megalooikonomou, V., Kossida, S.: Speeding up the drug discovery process: structural similarity searches using molecular surfaces. EMBnet.journal 18(1), 6–9 (2012)Google Scholar
  26. 26.
    Vriend, G.: WHAT IF: a molecular modeling and drug design program. J. Mol. Graph. 8(1), 52–56 (1990)CrossRefGoogle Scholar
  27. 27.
    Xiong, H., Buckwalter, B.L., Shieh, H.M., Hecht, M.H.: Periodicity of polar and nonpolar amino acids is the major determinant of secondary structure in self-assembling oligomeric peptides. Proc. Natl. Acad. Sci. 92(14), 6349–6353 (1995)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing 2017

Authors and Affiliations

  • Dimitrios Vlachakis
    • 1
  • Alexandros Armaos
    • 1
  • Sophia Kossida
    • 2
  1. 1.Bioinformatics and Medical Informatics TeamBiomedical Research Foundation of the Academy of AthensAthensGreece
  2. 2.University of Montpellier, IMGT®, The International ImMunoGeneTics Information System®, LIGM, IGH, UPR CNRS 1142Montpellier Cedex 5France

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