350 rub
Journal Biomedical Radioelectronics №5 for 2011 г.
Article in number:
The Investigation of Statistic Properties of Protein Factors of Pathogenicity
Authors:
A.R. Kayumov, M.I. Bogachev
Abstract:
Pathogenic bacteria have different polypeptide instruments, that (i) allow to penetrate the host organism, and (ii) support the protection of the microorganism from the host immune system. These functions are normally per-formed by proteins (that may be also coupled to carbohydrates) that are called pathogenicity factors. Each protein is responsible for particular functions of the microorganism in the infectionous process. They include factors of adhesion and colonisation; factors of invasion; factors preventing fagocytose, ferments of bacterial «protection and agression». The localization of key amino acids involved in the substrate binding and recognition and participating in cathalytic reaction in the protein primary structure of the biopolymer, can be treated as the biological system signal. In this paper, the primary protein structure is analyzed by a highly specific approach based on interval statistics. The efficiency of this approach for biological sequence analysis has been recently demonstrated by the authors. The original data for the analysis has been obtained from the pathogenicity factors database. Analyzed proteins has been originally classified according to their functional role in the cell. The analysis of amino acid sequences revealed significant statistical differences in the primary structure of proteins related to the cell wall. In these proteins several amino acids characterized by a broad distribution of intervals have been detected. Currently the functional and the evolutionary role of the broad distribution in some amino acids remains unknown. It is possible that these components are more essential for the secondary and triple protein globule formation than for the functional activity of the particular protein.
Pages: 24-27
References
  1. Kayumov A., Heinrich A., Sharipova M., Iljinskaya O., Forchhammer K. Inactivation of the general transcription factor TnrA in Bacillus subtilis by proteolysis // Microbiology. 2008. V.154. P.2348-2355.
  2. Peng C.-K., Buldyrev S. V., Goldberger A. L. et al. Long-range correlations in nucleotide sequences // Nature. 1992. V. 356. P. 168-170.
  3. Chatzdimitriou-Dreismann C. A., Lahrammar D. Long-range correlations in DNA // Nature. 1993. V. 361. P. 212-213.
  4. Yu Z.-G., Ahn V., Lau K.-S. Multifractal and correlation analyses of protein sequences from complete genomes // Phys. Rev. E. 2003. V. 68. P.021913 (1-10).
  5. Yang J.-Y., Yu Z.-G., Ahn V. Clustering structures of large proteins using multifractal analyses based on a 6-letter model and hydrophobicity scale of amino acids // Chaos, Solitons and Fractals. 2009. V. 40. P. 607-620.
  6. Bogachev M. I., Eichner J. F., Bunde A. Effect of nonlinear correlations on the statistics of return intervals in multifractal data sets // Phys. Rev. Lett. 2007. V. 99. P. 240601(1-4).
  7. Bogachev M. I., Eichner J. F., Bunde A. The effect of multifractality on the statistics of return intervals // Eur. Phys. J. Spec. topics. 2008. V. 181. P. 181-193.
  8. Bogachev M. I., Bunde A. On the occurrence and predictability of overloads in telecommunication networks // Europhys. Lett. 2009. V. 86. P. 66002(1-6).
  9. Богачев М. И., Каюмов А. Р., Михайлова Е. О. Анализ структуры сигналов и функциональной организации биокаталитических систем с использованием математического аппарата интервальных статистик // Изв. вузов России. Сер. Радиоэлектроника. 2010. Вып. 3. С. 8 - 16.
  10. Богачев М. И., Каюмов А. Р. Сравнительный анализ первичной структуры белков патогенных и непатогенных микроорганизмов при помощи математического аппарата интервальных статистик // Биомедицинские технологии и радиоэлектроника. 2010. № 11. С. 4 - 9.