Baishan Fang

Predicting the cofactors of oxidoreductases based on amino acid composition distribution and Chou's amphiphilic pseudo-amino acid composition

Predicting the cofactors of oxidoreductases plays an important role in inferring their catalytic mechanism. Feature extraction is a critical part in the prediction systems, requiring raw sequence data to be transformed into appropriate numerical feature vectors while minimizing information loss. In this paper, we present an amino acid composition distribution method for extracting useful features from primary sequence, and the k-nearest neighbor was used as the classifier. The overall prediction accuracy evaluated by the 10-fold cross-validation reached 90.74%. Comparing our method with other eight feature extraction methods, the improvement of the overall prediction accuracy ranged from 3.49% to 15.74%. Our experimental results confirm that the method we proposed is very useful and may be used for other bioinformatical predictions. Interestingly, when features extracted by our method and Chous amphiphilic pseudo-amino acid composition were combined, the overall accuracy could reach 92.53%.

Predicting Lipase Types by Improved Chous Pseudo-Amino Acid Composition

By proposing a improved Chous pseudo amino acid composition approach to extract the features of the sequences, a powerful predictor based on k-nearest neighbor was introduced to identify the types of lipases according to their sequences. To avoid redundancy and bias, demonstrations were performed on a dataset where none of the proteins has > or =25% sequence identity to any other. The overall success rate thus obtained by the 10-fold cross-validation test was over 90%, indicating that the improved Chous pseudo amino acid composition might be a useful tool for extracting the features of protein sequences, or at lease can play a complementary role to many of the other existing approaches.

Support vector machine for discrimination of thermophilic and mesophilic proteins based on amino acid composition.

The identification of the thermostability from the amino acid sequence information would be helpful in computational screening for thermostable proteins. We have developed a method to discriminate thermophilic and mesophilic proteins based on support vector machines. Using self-consistency validation, 5-fold cross-validation and independent testing procedure with other datasets, this module achieved overall accuracy of 94.2%, 90.5% and 92.4%, respectively. The performance of this SVM-based module was better than the classifiers built using alternative machine learning and statistical algorithms including artificial neural networks, Bayesian statistics, and decision trees, when evaluated using these three validation methods. The influence of protein size on prediction accuracy was also addressed.

Molecular Docking of Bacillus pumilus Xylanase and Xylan Substrate Using Computer Modeling

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 expression of the genes encoding glycerol dehydratase reactivase and identification of its biological activity

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).