Measurement of hydrophobicity distribution in proteins – complete redundant Protein Data Bank

• Autorzy: Sałapa, K. , Kalinowska, B. , Jadczyk, T. , Roterman, I.
• Języki publikacji: EN

Abstrakty

EN

The divergence entropy: O/T and O/R measuring the distance between observed/theoretical and observed/random distributions was applied to identify the category of protein structures in respect to the hydrophobic core in protein molecules. The naive interpretation was applied treating the proteins of O/T < O/R as the molecules of hydrophobic core accordant with the theoretically assumed. The proteins of O/T > O/R are treated as representing the hydrophobic core not accordant with the assumed one. The large scale computing was performed (PDB data set) to reveal whether other than simple inequality relation should be used for this identification. The cluster analysis was applied to identify the relation O/T versus O/R as the discrimination factor to classify the category of proteins in respect to their structural form of hydrophobic core.

Słowa kluczowe

EN

cluster analysis; divergence entropy; protein structure; hydrophobic core

• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2012
• Tom: Vol. 8, no. 2
• Strony: 195--206
• Opis fizyczny: Bibliogr. 8 poz., rys., tab., wykr.

Twórcy

autor

Sałapa, K.

• Department of Bioinformatics and Telemedicine – Jagiellonian University – Medical College – Kraków, Poland

autor

Kalinowska, B.

• Department of Bioinformatics and Telemedicine – Jagiellonian University – Medical College – Kraków, Poland
• Faculty of Physics, Astronomy and Applied Computer Science – Jagiellonian University, Kraków, Poland

autor

Jadczyk, T.

• ACK – Cyfronet – Academic Computer Center – University of Science and Technology – Kraków, Poland

autor

Roterman, I.

• Department of Bioinformatics and Telemedicine – Jagiellonian University – Medical College – Kraków, Poland

Bibliografia

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• 3. Levitt M. (1976) A simplified representation of protein conformations for rapid simulation of protein folding. J. Mol. Biol. 104, 59-107.
• 4. Kullback, S.; Leibler, R.A. (1951). "On Information and Sufficiency". Annals of Mathematical Statistics 22 (1), 79–86.
• 5. Marchewka D, Banach M, Roterman I. (2011) Internal force field in proteins seen by divergence entropy. Bioinformation. 6(8):300-2.
• 6. (www.statsoft.com, 2011). Downloaded 11 20, 2011
• 7. Tuffery, S. (2011). Data Mining and Statistics for Decision Making. Wiley.
• 8. Du, H. (2010). Data Mining Techniques and Applications. An introduction. Cengage Learning EMEA.