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- MakaleAçık Erişim2026Cloning and biochemical characterization of a novel GH1 β-glucosidase from thermophilic bacterium, Anoxybacillus flavithermus DSM 2641T
Akgeyik, Aytul Uzun; Akyildiz, Esma; Karaoglu, Hakan; Beris, Fatih Saban
β-glucosidases belong to the class of glycoside hydrolases (GHs) and can mediate the cleavage of β-1-4 glycosidic bonds in alkyl or aryl-β-D-glucosides, amino, cyanogenic glucosides, and oligo-or disaccharides. The gene encoding β-glucosidase of Anoxybacillus flavithermus DSM2641T was subcloned and expressed in E. coli BL21 Star(DE3) cells with pET100 Directional TOPO Expression Kit. The recombinant AFBGL was purified by a Ni-NTA affinity chromatography. The enzyme was 53 kDa and had 5.13 × 105 µmol/min/mg protein of Vmax and 422.3 µM of Km towards p-nitrophenyl β-D-glucoside as a main substrate. The optimum temperature and pH of the purified enzyme were 65°C and 6.8, respectively. We also determined kinetic parameters of AFBGL against the other artificial substrats, pNPC, pNPX, and oNPG. The enzyme had the Vmax and Km for 5.13 × 105 µmol/min/mg protein and 0.42 mM for pNPG, 0.007 µmol/min/mg protein and 0.19 mM for pNPC, 0.01 µmol/min/mg protein and 11.97 mM for pNPX, 0.06 µmol/min/mg protein and 0.134 mM for oNPG, respectively. AFBGL's substrate affinity was found to be more directed towards oNPG. Although 0.5 mM Mg2+ increases the enzyme's activity 1.6-fold, higher concentrations of Mg2+ and the presence of K+, Fe2+, Mn2+, Zn2+, Cu2+, and Al3+ decreases its activity. AFBGL retained 50% of the initial activity towards pNPG for 25 min and the activity was not detected after 225 min. According to amino acid motifs, AFBGL belongs to family 1 of glycosylhydrolase and 4/7 super family. So, AFBGL is a member of GH1, as the amino acid sequence contained a GH1 specific domain.
- MakaleYalnız künye2012Artificial neural networks for estimation of temporal rate coefficient of equilibrium bar volume
Kankal, Murat; Kömürcü, Murat Ihsan; Yüksek, Ömer; Akpinar, Adem
Present study consists the growth of a bar profile caused by cross-shore sediment transport. This is especially on growth of bar volume (V) toward equilibrium bar volume (Veq). Three analysis methods being a power and linear regression analysis (PRA and LRA) and an Artificial Neural Network (ANN) analysis were performed to determine empirical temporal rate coefficient (α). Forty-two experimental data were used for training set and the rest of the experimental data were used for testing set in the ANN analysis. As the results of analyses, the smallest average relative and root mean square error (RMSE) computed for the ANN methods are 7.578% and 0.029, respectively. It has been obtained that the ANN analysis, which is used for determination of α coefficient, gives reasonable results. Finally, bar volumes were calculated by means of computed α values and compared with the results of experimental data.
- DergiIndian Journal of Geo-Marine Sciences
- DergiIndian Journal of Biochemistry and Biophysics