The S-S bridge mutation between the A2 and A4 loops (T416C-I432C) of Cel7A of Aspergillus fumigatus enhances catalytic activity and thermostability.

Disulfide bonds are important for maintaining the structural conformation and stability of the protein. The introduction of the disulfide bond is a promising strategy to increase the thermostability of the protein. In this report, cysteine residues are introduced to form disulfide bonds in the Glycoside Hydrolase family GH 7 cellobiohydrolase (GH7 CBHs) or Cel7A of Aspergillus fumigatus. Disulfide by Design 2.0 (DbD2), an online tool is used for the detection of the mutation sites. Mutations are created (D276C-G279C; DSB1, D322C-G327C; DSB2, T416C-I432C; DSB3, G460C-S465C; DSB4) inside and outside of the peripheral loops but, not in the catalytic region. The introduction of cysteine in the A2 and A4 loop of DSB3 mutant showed higher thermostability (70% activity at 70°C), higher substrate affinity (Km = 0.081 mM) and higher catalytic activity (Kcat = 9.75 min-1; Kcat/Km = 120.37 mM min-1) compared to wild-type AfCel7A (50% activity at 70°C; Km = 0.128 mM; Kcat = 4.833 min-1; Kcat/Km = 37.75 mM min-1). The other three mutants with high B factor showed loss of thermostability and catalytic activity. Molecular dynamic simulations revealed that the mutation T416C-I432C makes the tunnel wider (DSB3: 13.6 Å; Wt: 5.3 Å) at the product exit site, giving flexibility in the entrance region or mobility of the substrate in the exit region. It may facilitate substrate entry into the catalytic tunnel and release the product faster than the wild type, whereas in other mutants, the tunnel is not prominent (DSB4), the exit is lost (DSB1), and the ligand binding site is absent (DSB2). This is the first report of the gain of function of both thermostability and enzyme activity of cellobiohydrolase Cel7A by disulfide bond engineering in the loop.IMPORTANCEBioethanol is one of the cleanest renewable energy and alternatives to fossil fuels. Cost efficient bioethanol production can be achieved through simultaneous saccharification and co-fermentation that needs active polysaccharide degrading enzymes. Cellulase enzyme complex is a crucial enzyme for second-generation bioethanol production from lignocellulosic biomass. Cellobiohydrolase (Cel7A) is an important member of this complex. In this work, we engineered (disulfide bond engineering) the Cel7A to increase its thermostability and catalytic activity which is required for its industrial application.

Computational approach for identification, characterization, three-dimensional structure modelling and machine learning-based thermostability prediction of xylanases from the genome of Aspergillus fumigatus.

Identification of thermostable and alkaline xylanases from different fungal and bacterial species have gained an interest for the researchers because of its biotechnological relevance in many industries, such as pulp, paper, and bioethanol. In this study, we have identified and characterized xylanases from the genome of the thermophilic fungus of Aspergillus fumigatus by in silico analysis. Genome data mining revealed that the A fumigatus genome has six xylanase genes that belong to GH10, GH11, GH43 glycoside hydrolase families. In general, most of the bacterial and fungal GH11 xylanases are alkaline, and GH10 xylanases are acidic; however, we found that one identified xylanase from A fumigatus that belongs to the GH10 family is alkaline while the rest are acidic. Moreover, physicochemical properties also stated that most of the xylanases identified have lower molecular weight except one that belongs to the GH43 family. Structure prediction by homology modelling gave optimized structures of the xylanases. It suggests that GH10 family structure models adapt (ß∕α) 8 barrel type, GH11 homology models adapt ß-jelly type, and the GH43 family has a fivefold ß-propeller type structure. Molecular docking of identified xylanases with xylan revealed that GH11 xylanases have strong interaction (-9.6 kcal/mol) with xylan than the GH10 (-8.5 and -9.3 kcal/mol) and GH43 (-8.8 kcal/mol). We used the machine learning approach based TAXyl server to predict the thermostability of the xylanases. It revealed that two GH10 xylanases and one GH11 xylanase are thermo-active up to 75ᵒC. We have explored the physiochemical properties responsible for maintaining thermostability for bacterial and fungal GH10 and GH11 xylanases by comparing crystal structures. All the analyzed parameters specified that GH10 xylanases from both the fungi and bacteria are more thermostable due to higher hydrogen bonds, salt bridges, and helical content.