Molecular docking of Bacillus pumilus xylanase and xylan substrate using computer modeling / 生物工程学报

Bacillus pumilus xylanase was cloned and sequenced. Based on the tertiary structure that originated from homology modeling, the potential active pocket was searched and ligand-protein docking was performed using relative softwares. The information extracted from the molecular docking is analyzed; several amino acid residues might play a vital role in the xylanase catalytic reaction are obtained to instruct the further modification of xylanase directed-evolution.

Cloning and sequence analysis of the gene encoding NiFe-hydrogenase from Klebsiella pneumoniae / 生物工程学报

Degenerate PCR primers were designed by multiple alignment of the protein sequences of known structural genes encoding the catalytic subunits of NiFe-hydrogenases obtained from Swiss-Prot Protein Sequence Database through CLUSTAL-W software and compared for conserved sequence motifs. An amplified PCR product 1 kb in size was obtained from the genomic DNA of Klebsiella pneumoniae using a set of degenerate primers, and then inverse PCR technique was used to obtain the full hydrogenase coding region. A predicted secondary structure and 3D structural model were constructed by homology modeling and docking. On the basis of these results, it was inferred that NiFe-hydrogenase from Klebsiella pneumoniae belongs to the membrane-bound H2 evolving hydrogenase group (Ech hydrogenase group).

A study on the discrimination of thermophilic and mesophilic proteins based on dipeptide composition / 生物工程学报

In this work, the dipeptide composition of 3216 thermophilic and 4007 mesophilic protein sequences was systematically analyzed. We found that the thermophilic proteins contained more dipeptides such as EE, EK, KE, VE, EI, KI, EV, KK, VK and IE, whereas less dipeptides such as AA, LL, LA, AL, QA, QL, AQ, LT, TL and EQ. Based on this information, a statistical method for discriminating thermophilic and mesophilic proteins was developed. Our approach correctly picked up the thermophilic proteins with the accuracy of 94.0% and 89%, respectively, for the testing sets of 382 and 73 thermophilic proteins. And for the testing 325 and 73 mesophilic proteins, the accuracy was 85.2% and 89%, respectively. The influence of specific dipeptides on discrimination was also discussed.

Cloning and expression of the genes encoding glycerol dehydratase reactivase and identification of its biological activity / 生物工程学报

The gdrA, gdrB gene coding glycerol dehydratase reactivase factor were amplified by using the genomic DNA of Klebsiella pneumoniae as the template. The gdrA and gdrB were inserted in pMD-18T to yield the recombinant cloning vector pMD-gdrAB. After the DNA sequence was determined, the gdrAB gene was subcloned into expression vector pET-28a(+) to yield the recombinant expression vector pET-28gdrAB. Under screening pressure by ampicillin and kanamycin simultaneously, the activity of glycerol dehydratase reactivase was characterized by coexpression of pET-32gldABC, which carry the gldABC gene encoding glycerol dehydratase, and pET-28gdrAB in E. coli BL21(DE3).

Application of boosting-based decision tree ensemble classifiers for discrimination of thermophilic and mesophilic proteins / 生物工程学报

In this paper, the Boosting-based decision tree ensemble classifiers were applied to discriminate thermophilic and mesophilic proteins. Three methods, namely, self-consistency test, 5-fold cross-validation and independent testing with other dataset, were used to evaluate the performance and robust of the models. Logitboost, as a novel classifier in Boosting algorithm, performed better than Adaboost. The overall accuracy of the three methods was 100%, 88.4% and 89.5%, respectively. It was demonstrated that LogitBoost performed comparably or even better than that of neural network, a very powerful classifier widely used in biological literatures. The influence of protein size on discrimination was addressed. It is anticipated that the power in predicting many bio-macromolecular attributes will be further strengthened if the Boosting and some other existing algorithms can be effectively complemented with each other.

A model for amino acid composition and optimum pH in G/11 xylanase based on neural networks / 生物工程学报

In this paper, a prediction model for amino acid composition and optimum pH of xylanase in G/11 family was established in terms of an artificial neural networks based on uniform design. Results showed that the calculated and predicted pHs fitted the optimum pHs of xylanase very well and the MAPEs (Mean mean Absolute Percent Error) were 3.02% and 4.06%, the MSEs (Mean Square Error) were 0.19 and 0.19 pH unit, the MAE (Mean Absolute Error) were 0.11 and 0.19 pH unit, respectively. It was better in fittings and predictions compared with the reported model based on stepwise regression.

A study on the pattern recognition of thermophilic and mesophilic proteins / 生物工程学报

Pattern recognition of thermophilic and mesophilic proteins were studied through principle component analysis, partial least-square regression and BP neural network. The results showed that the fitting accuracy of the three methods was 92%, 95% and 98%, respectively. And the forecasting accuracy was 60%, 72.5% and 72.5%, respectively. The best forecasting accuracy for thermophilic proteins was 75%, and for mesophilic proteins was 85%. A mathematical model was established and the biological meaning of it was expatiated on, a new method to discriminate the thermophilic and mesophilic proteins based on their sequences was established here.