2016  0,799
2015  0,662
2014  0,740
2013  0,739
2012  0,637
2011  0,658
2010  0,654
2009  0,570
2008  0,849
2007  0,805
2006  0,330
2005  0,435
2004  0,623
2003  0,567
2002  0,641
2001  0,490
2000  0,477
1999  0,762
1998  0,785
1997  0,507
1996  0,518
1995  0,502
Vol 52(2018) N 2 p. 279-284; DOI 10.1134/S0026893317050041 Full Text

A.O. Bragin1*, V.S. Sokolov1, P.S. Demenkov1, T.V. Ivanisenko1, E.Yu. Bragina2, Yu.G. Matushkin1, V.A. Ivanisenko1

Prediction of Bacterial and Archaeal Allergenicity with AllPred Program

1Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, 630090 Russia
2Research Institute of Medical Genetics, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050 Russia

Received - 2016-10-15; Accepted - 2016-12-03

Nowadays, allergic disorders have become one of the most important social problems in the world. This can be related to the advent of new allergenic agents in the environment, as well as an increasing density of human contact with known allergens, including various proteins. Thus, the development of computer programs designed for the prediction of allergenic properties of proteins becomes one of the urgent tasks of mo dern bioinformatics. Previously we developed a web accessible Allpred Program ( psd/cgi-bin/programs/Allpred/allpred.cgi) that allows users to assess the allergenicity of proteins by taking into account the characteristics of their spatial structure. In this paper, using AllPred, we predicted the allergenicity of proteins from 462 archaea and bacteria species for which a complete genome was available. The segregation of considered proteins on archaea and bacteria has shown that allergens are predicted more often among archaea than among bacteria. The division of these proteins into groups according to their intracellular localization has revealed that the majority of allergenic proteins were among the secreted proteins. The application of methods for predicting the level of gene expression of microorganisms based on DNA sequence analysis showed a statistically significant relationship between the expression level of the proteins and their allergenicity. This analysis has revealed that potentially allergenic proteins were more common among highly expressed proteins. Sorting microorganisms into the pathogenic and nonpathogenic groups has shown that pathogens can potentially be more allergenic because of a statistically significant greater number of allergens predicted among their proteins.

pathogenic microorganisms, allergenicity prediction, protein expression