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Keram: a novel stand-alone application for correlated mutations identification and analysis

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Keram is a stand-alone Windows 2000/XP/Vista application designed for the detection and analysis of the correlated mutations. Study of this phenomenon provides important information about protein structure stability factors as well as the formation of protein complexes. It is generally assumed that the mechanism of compensation explains the mutations that occur simultaneously. Keram is designed to detect the mutational correlations by comparative analysis of multiple sequence alignments. Additionally a three dimensional structure can be applied to calculate the distance between correlated positions in the protein molecule. Keram has been succesfully applied for the analysis of kinase subfamilies. The obtained data suggest that the mechanism of compensation does not explain utterly this phenomenon which seems to be much more complex and diverse. The residues that are detected as correlated are often placed at very distant positions of the protein structure, therefore the direct mutual interaction between them is impossible. We have detected not only correlated pairs, but also clusters of positions (even more than 10) that reveal correlated changeability.
Rocznik
Strony
71--76
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
autor
  • Faculty of Biology, University of Warsaw, Poland
  • CoE BioExploratorium, University of Warsaw, Poland
autor
  • Faculty of Biological Sciences, University of Zielona Góra, Poland
  • CoE BioExploratorium, University of Warsaw, Poland
autor
  • Faculty of Physics, University of Warsaw, Poland
  • CoE BioExploratorium, University of Warsaw, Poland
Bibliografia
  • 1. Apweiler R., Bairoch A., Wu C.H., Barker W.C., Boeckmann B., Ferro S., Gasteiger E., Huang H., Lopez R., Magrane M., Martin M.J., Natale D.A., O'Donovan C., Redaschi N., Yeh L.S.: UniProt: the Universal Protein knowledgebase. Nucleic Acids Res. 32:D115-119, 2004.
  • 2. Apweiler R., Bairoch A., Wu C. H.: Protein sequence databases. Current Opinion in Chemical Biology 8: 76-80, 2004.
  • 3. Bairoch A., Apweiler R., Wu C.H., Barker W.C., Boeckmann B., Ferro S., Gasteiger E., Huang H., Lopez R., Magrane M., Martin M.J., Natale D.A., O'Donovan C., Redaschi N., Yeh L.S.: The Universal Protein Resource (UniProt). Nucleic Acids Res. 33: D154-159, 2004.
  • 4. Berman H.M., Westbrook J., Feng Z., Gilliland G., Bhat T.N., Weissig H., Shindyalov I.N., Bourne P.E.: The Protein Data Bank. Nucleic Acids Research 28: 235-242, 2000.
  • 5. Finn R.D., Mistry J., Schuster-Böckler B., Griffiths-Jones S., Hollich V., Lassmann T., Moxon S., Marshall M., Khanna A., Durbin R., Eddy S.R., Sonnhammer E.L.L., Bateman A.: Pfam: clans, web tools and services, Nucleic Acids Research, Database Issue 34:D247-D251, 2006.
  • 6. Górecki A., Leluk J., Lesyng B.: Identification and free energy simulations of correlated mutations in proteins, RECOMB2005, Cambridge MA, USA, Abstracts, 2005.
  • 7. Kass I., Horovitz A.: Mapping pathways of allosteric communication in GroEL by analysis of correlated mutations Proteins: Struct. Funct. & Genet. 48: 611-617, 2002.
  • 8. Leluk, J.: A new algorithm for analysis of the homology in protein primary structure. Computers & Chemistry 22: 123-131, 1998.
  • 9. Leluk, J.: A non-statistical approach to protein mutational variability. BioSystems 56: 83-93, 2000a.
  • 10. Leluk, J.: Regularities in mutational variability in selected protein families and the Markovian model of amino acid replacement. Computers & Chemistry 24: 659-672, 2000b.
  • 11. Leluk, J., Hanus-Lorenz, B. Sikorski, A.F.: Application of genetic semihomology algorithm to theoretical studies on various protein families. Acta Biochim. Polon. 48: 21-33, 2001.
  • 12. Leluk J., Konieczny L., Roterman I.: Search for structural similarity in proteins, Bioinformatics: 19(1): 117-124, 2003.
  • 13. Neher E.: How frequent are correlated changes in families of protein sequences, Proc. Natl. Acad. Sci. USA. 91: 98-102, 1993.
  • 14. Oliveira L., Pavia A. C. M., Vriend G.: Correlated Mutation Analyses on Very Large Sequence Families. Chembiochem. 3(10):1010-7, 2002.
  • 15. Pieper U., Eswar N., Braberg H., Madhusudhan M.S., Davis F., Stuart A.C., Mirkovic N., Rossi A., Marti-Renom M.A., Fiser A., Webb B., Greenblatt D., Huang C., Ferrin T., Sali A.: MODBASE, a database of annotated comparative protein structure models, and associated resources. Nucleic Acids Research 32: D217-D222, 2004.
  • 16. Pieper U., Eswar N., Davis F.P., Braberg H., Madhusudhan M.S., Rossi A., Marti-Renom M., Karchin R,, Webb B.M., Eramian D., Shen M.Y., Kelly L., Melo F., Sali A.: MODBASE, a database of annotated comparative protein structure models and associated resources. Nucleic Acids Research 34: D291-D295, 2006.
  • 17. Thompson, J.D., Gibson, T.J., Plewniak, F., Jeanmougin, F. and Higgins, D.G.: The ClustalX windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Research, 24:4876-4882, 1997.
  • 18. Thompson, J.D., Higgins, D.G. and Gibson, T.J.: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, positions-specific gap penalties and weight matrix choice. Nucleic Acids Research 22: 4673-4680, 1994.
  • 19. Valencia A., Pazos F.: Computational methods for the prediction of protein interactions, Current Opinion in Biology 12: 368-373, 2002.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-76eb35f4-a117-4f12-a671-05930fd015c5
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