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Determining the main factors contributing to xylanase resistance to different pH by applying the asymptotic distribution of a transformation on the Pearson correlation coefficient
datasetposted on 2017-11-21, 00:29 authored by Ebrahimi, Esmaeil, Ebrahimi, Mansour, Ebrahimi, Mahdi, Zinati, Zahra, Delavari, Azar, Mohammadi-dehchesmah, M.
The main role of xylanolytic enzyme, β-endoxylanase (1,4-β-D-xylan xylanohydrolase, EC 18.104.22.168) is to convert the polymer xylan to xylosaccharides. Many xylanase and xylosidase genes from fungi and bacteria have been analyzed and their encoded enzymes have been isolated and characterized. It has already been shown that those enzymes are active at a limited range of pH (with maximum, minimum and mean ± SE, 8, 2 and 5.47 ± 0.286, respectively). In some industries need for active enzymes in both alkaline and acidic pH have been elaborated and understanding the main factors contributing in pH resistance of theses enzymes are very important. So we looked at more than seventy properties of 30 xylanase proteins (active in different pH) by applying a feature selection algorithm which assigned a p value to each attribute based on the asymptotic distribution of a transformation on the Pearson correlation coefficient. The attributes were then sorted in a descending order of their importance to xylanase pH resistance based on calculated p values. The results showed that the frequency of Arg, Ser, Pro, Tyr, the count of Arg, Pro, Trp, Gly, Leu, Gln, the frequency of positively charged residues, the count of hydrophobic residues, the count of positively charged residues, non-reduced cycteins extinctoin coefficient and reduced cycteins extinctoin coefficient were the most important features contributing to the resistance of xylanases at different pH, and thirteen other attributes were considered to have a marginal contribution to this function, while the other features were revealed to be unimportant. The significance of "important" and "marginal" properties in xylanase activity in both alkaline and acidic pH has been discussed in this paper. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Bioinformatics -- CongressesComputational biology -- CongressesComputer vision in medicine -- CongressesComputational biology -- Methods -- CongressesPattern recognition, automated -- Methods -- CongressespH resistanceXylanaseModelingBioinformatics2008conference paper1959.1/63742monash:7877Bioinformatics SoftwareBioinformaticsPattern Recognition and Data Mining