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A method of predicting the secondary protein structure based on dictionaries

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The shape of a protein chain may be analyzed at different levels of details. The ultimate shape description contains three-dimensional coordinates of all atoms in the chain. In many cases, a description of the local shape, namely secondary structure, is enough to determine some properties of proteins. Although obtaining the full threedimensional (3D) information also defines the secondary structure, the problem of finding this precise 3D shape (tertiary structure) given only the amino acid sequence is very complex. However, the secondary structure may be found even without having the full 3D information. Many methods have been developed for this purpose. Most of them are based on similarities of the analyzed protein chain to other proteins that are already analyzed and have a known secondary structure. The presented paper proposes a method based on dictionaries of known structures for predicting the secondary structure from either the primary structure or the so-called structural code. Accuracies of up to 79% have been achieved.
Rocznik
Strony
163--170
Opis fizyczny
Bibliogr. 24 poz., tab.
Twórcy
  • Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Lazarza 16, Krakow, Poland
autor
  • Institute of Computer Science, Silesian Technical University, Akademicka 16, Gliwice, Poland
autor
  • Institute of Computer Science, Silesian Technical University, Akademicka 16, Gliwice, Poland
Bibliografia
  • 1. Tramontano A. Protein structure prediction: concepts and applications. Weinheim: Wiley-VCH, 2006.
  • 2. Kabsch W, Sander C. A database of secondary structure assignments (and much more) for all protein entries in the Protein Data Bank (PDB), 2012. Available at: http://swift.cmbi.ru.nl/gv/dssp/. Accessed on October, 2012.
  • 3. Chou PY, Fasman GD. Conformational parameters for amino acids in helical, β-sheet, and random coil regions calculated from proteins. Biochemistry 1974;13:211–22.
  • 4. Garnier J, Osguthorpe DJ, Robson B. Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. J Mol Biol 1978;120:97–120.
  • 5. Lim V. Algorithms for prediction of α-helical and β-structural regions in globular proteins. J Mol Biol 1974;88:873–94.
  • 6. Rost B, Sander C. Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 1993;232:584–99.
  • 7. Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 1999;292:195–202.
  • 8. Altschul S, Madden T, Schäffer A, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSIBLAST: a new generation of protein database search programs. Nucleic Acids Res 1997;25:3389–402.
  • 9. Przybylski D, Rost B. Alignments grow, secondary structure prediction improves. Proteins 2002;46:197–205.
  • 10. Pollastri G, McLysaght A. Porter: a new, accurate server for protein secondary structure prediction. Bioinformatics 2005;21:1719–20.
  • 11. Ward JJ, McGuffin LJ, Buxton BF, Jones DT. Secondary structure prediction with support vector machines. Bioinformatics 2003;19:1650–5.
  • 12. Nguyen MN, Rajapakse JC. Two-stage multi-class support vector machines to protein secondary structure prediction. Pac Symp Biocomput 2005:346–57.
  • 13. Stąpor K. Metody klasyfikacji obiektów w wizji komputerowej. Wydawnictwo Naukowe PWN, 2011.
  • 14. Błażewicz J, Łukasiak P, Wilk S. New machine learning methods for prediction of protein secondary structures. Control Cybernet 2007;36:183–201.
  • 15. Rost B. Rising accuracy of protein secondary structure prediction. New York: Dekker, 2003:207–49.
  • 16. Zemla A, Venclovas C, Fidelis K, Rost B. A modified definition of SOV, a segment-based measure for protein secondary structure prediction assessment. Proteins 1999;34:220–3.
  • 17. Yang W, Wang K-Q, Zuo W-M. Protein secondary structure prediction based on statistical dictionaries. In: 3rd International conference on bioinformatics and biomedical engineering, 2009:1–4.
  • 18. Lin H, Sung T, Ho S, Hsu W. Improving protein secondary structure prediction based on short subsequences with local structure similarity. BMC Genomics 2010;11:S4.
  • 19. Rost B, Sander C, Schneider R. Redefining the goals of protein secondary structure prediction. J Mol Biol 1994;235:13–26.
  • 20. Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983;22:2577–637.
  • 21. Joosten RP, te Beek TA, Krieger E, Hekkelman ML, Hooft RW, Schneider R, et al. A series of PDB related databases for everyday needs. Nucleic Acids Res 2011;39:D411–9.
  • 22. Brylinski M, Konieczny L, Roterman I. SPI – structure predictability index for protein sequences. In Silico Biol 2005;5: 227–37.
  • 23. Brylinski M, Konieczny L, Czerwonko P, Jurkowski W, Roterman I. Early-stage folding in proteins (in silico) sequenceto-structure relation. J Biomed Biotechnol 2005;2005: 65–80.
  • 24. Kalinowska B, Fabian P, Stąpor K, Roterman I. Statistical dictionaries for hypothetical in silico model of the early-stage intermediate in protein folding. J Comput-Aided Mol Des 2015;29:609–18.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4a83010d-ffc9-4814-8996-4b8149b963bb
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