Methane monooxygenases (MMO), regarded as "an amazing biomolecular machine", catalyze the oxidation of methane to methanol under aerobic conditions. MMO catalyze the oxidation of methane elaborately, which is a novel way to catalyze methane to methanol. Furthermore, MMO can inspire the biomolecular machine design. In this review, we introduced MMO including structure, gene and catalytic mechanism. The history and the taxonomy of MMO were also introduced.
Catalysis ; Methane ; metabolism ; Methanol ; metabolism ; Oxygenases ; metabolism

Types of cofactor independency for newly found oxidoreductases sequences are usually determined by experimental analysis. These experimental methods are both time-consuming and costly. With the explosion of oxidoreductases sequences entering into the databanks, it is highly desirable to explore the feasibility of selectively classifying newly found oxidoreductases into their respective cofactor independency classes by means of an automated method. In this study, we proposed a modified Chou's pseudo-amino acid composition method to extract features from sequences and the k-nearest neighbor was used as the classifier, and the results were very encouraging. When lambda = 48, w = 0.1, the areas under the ROC curve of k-nearest neighbor in 10-fold cross-validation was 0.9536; and the success rate was 92.0%, which was 3.5% higher than that of pseudo-amino acid composition. It was also better than all the other 7 feature extraction methods. Our results showed that predicting the cofactors of oxidoreductases was feasible and the modified pseudo-amino acid composition method may be a useful method for extracting features from protein sequences.
Amino Acid Motifs ; Amino Acids ; analysis ; Coenzymes ; chemistry ; Computational Biology ; Models, Chemical ; Oxidoreductases ; chemistry ; Predictive Value of Tests

We used amino acid composition distribution (AACD) to discriminate thermophilic and psychrophilic proteins. We used 10-fold cross-validation and independent testing with other dataset to evaluate the models. The results showed that when the segment was 1, the overall accuracy reached 92.9% and 90.2%, respectively. The AACD method improved the prediction accuracy when support vector machine was used as the classifier. When six new features were introduced, the overall accuracy of random forest improved to 93.2% and 92.2%, the areas under the receiver operation characteristic curve were 0.9771 and 0.9696, which was better than other ensemble classifiers and comparable with that of SVM.
Algorithms ; Amino Acid Sequence ; Bacteria ; genetics ; Bacterial Proteins ; chemistry ; classification ; Computer Simulation ; Discriminant Analysis ; Models, Chemical ; Models, Molecular ; Protein Structure, Secondary ; Sequence Analysis, Protein ; methods ; Temperature

In this work, we systematically analyzed the secondary structure amino acid compositions of acidic and alkaline enzymes and compared them with neutral ones. We found that the propensity of the individual residues to participate in secondary structures and the consistently higher composition of neutral and tiny residues might be the general stability mechanisms for their adaptation to pH extremes. Based on this, we presented a secondary structure amino acid composition method for extracting useful features from sequence. The overall prediction accuracy evaluated by the 10-fold cross-validation reached 80.3%. Comparing our method with other feature extraction methods, the improvement of the overall prediction accuracy ranged from 9.4% to 18.7%. The random forests algorithm also outperformed other machine learning techniques with an improvement ranging from 2.7% to 21.8%.
Algorithms ; Amino Acids ; chemistry ; Bacteria ; enzymology ; Enzymes ; chemistry ; classification ; Hydrogen-Ion Concentration ; Models, Chemical ; Protein Structure, Secondary

It is of theoretical and practical significance to understand the mechanism of enzyme adaptation to acidic and alkaline environments and classification of them based on sequence information. In present work, the amino acid composition of 105 acidic and 111 alkaline enzyme sequences was systematically analyzed. Acidic enzymes contained significantly more Trp, Tyr, Thr and Ser, whereas less Glu, Lys, Met and Arg. On the other hand, alkaline enzymes have slightly more Trp, Ala and Cys, whereas less Lys, Arg and Glu. The amount of Ala, Glu, Leu, Asn, Arg, Ser and Thr in acidic and alkaline enzymes varied largely. Hence, a weighted amino acid composition method was developed for the discrimination of acidic and alkaline enzymes. Using the back-check and the 5-fold cross validation methods, the overall accuracy could reach 86.1% and 83.3%, respectively. A new method to classify acidic and alkaline enzymes based on their sequences was established.
Amino Acids ; chemistry ; genetics ; Enzymes ; chemistry ; classification

Lipases are widely used enzymes in biotechnology. Although they catalyze the same reaction, their sequences vary. Therefore, it is highly desired to develop a fast and reliable method to identify the types of lipases according to their sequences, or even just to confirm whether they are lipases or not. By proposing two scales based pseudo amino acid composition approaches to extract the features of the sequences, a powerful predictor based on k-nearest neighbor was introduced to address the problems. The overall success rates thus obtained by the 10-fold cross-validation test were shown as below: for predicting lipases and nonlipase, the success rates were 92.8%, 91.4% and 91.3%, respectively. For lipase types, the success rates were 92.3%, 90.3% and 89.7%, respectively. Among them, the Z scales based pseudo amino acid composition was the best, T scales was the second. They outperformed significantly than 6 other frequently used sequence feature extraction methods. The high success rates yielded for such a stringent dataset indicate predicting the types of lipases is feasible and the different scales 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.
Amino Acids ; analysis ; chemistry ; Computational Biology ; Lipase ; chemistry ; classification ; Models, Chemical ; Sequence Analysis, Protein ; methods

The gldA gene coding glycerol dehydrogenase (GDH) was amplified by PCR with the genomic DNA of Klebsiella pneumoniae as the template. The gldA were inserted in pMD-18T to construct the recombinant cloning vector pMD-gldA. After the DNA sequence was determined, the gldA was subcloned into expression vector pET-32a (+) to construct the recombinant expression vector pET-32gldA. Upon lactose induction, soluble GDH was over-produced by E. coli BL21 (DE3) harboring the expression construct. Recombinant GDH purified by Ni-NTA affinity chromatography showed a single band about 54 kD on SDS-PAGE gel, and the specified activity was about 188 u/mg, the purification fold is 3 times and the activity recovery is 67.5%.
Chromatography, Affinity ; Cloning, Molecular ; DNA, Bacterial ; genetics ; Escherichia coli ; genetics ; isolation & purification ; metabolism ; Klebsiella pneumoniae ; enzymology ; genetics ; Sugar Alcohol Dehydrogenases ; biosynthesis ; genetics ; isolation & purification

The kinetic mechanisms of two key enzymes in the biotransformation of glycerol to 1,3-propanediol (1,3-PD) by Klebsiella pneumoniae, glycerol dehydrogenase (GDH) and 1,3-propanediol oxidoreductase (PDOR), was characterized. Kinetics on initial velocity and product inhibition revealed that GDH and PDOR follow an ordered Bi-Bi sequential mechanism. Kinetic models for GDH and PDOR showed that the oxidation reaction catalyzed by GDH was the rate-limiting step in coupled enzymatic reaction when the GDH/PDOR was 1:1, and the NAD+ was the main form of coenzyme in the reaction. Knowledge about the kinetic mechanisms will be helpful to understand how these enzymes is regulated, which will be useful for further enzyme catalysis and metabolic engineering studies.
Alcohol Dehydrogenase ; metabolism ; Bacterial Proteins ; metabolism ; Glycerol ; metabolism ; Kinetics ; Klebsiella pneumoniae ; enzymology ; Models, Theoretical ; Propylene Glycols ; metabolism ; Substrate Specificity ; Sugar Alcohol Dehydrogenases ; metabolism