RESUMO
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).
Assuntos
Proteínas de Bactérias , Química , Genética , Clonagem Molecular , Códon , Genética , DNA Bacteriano , Química , Genética , Bases de Dados de Proteínas , Ligação de Hidrogênio , Hidrogenase , Química , Genética , Klebsiella pneumoniae , Genética , Modelos Moleculares , Dados de Sequência Molecular , Conformação Proteica , Estrutura Secundária de Proteína , Estrutura Terciária de Proteína , Análise de Sequência de DNARESUMO
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.
Assuntos
Sequência de Aminoácidos , Bacillus , Genética , Proteínas de Bactérias , Genética , Metabolismo , Sequência de Bases , Simulação por Computador , Endo-1,4-beta-Xilanases , Genética , Metabolismo , Modelos Químicos , Modelos Moleculares , Dados de Sequência Molecular , Ligação Proteica , Especificidade por Substrato , Xilanos , Genética , MetabolismoRESUMO
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).
Assuntos
Proteínas de Bactérias , Genética , Metabolismo , Clonagem Molecular , Eletroforese em Gel de Poliacrilamida , Escherichia coli , Genética , Expressão Gênica , Vetores Genéticos , Genética , Hidroliases , Genética , Metabolismo , Plasmídeos , Genética , Reação em Cadeia da PolimeraseRESUMO
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.
Assuntos
Algoritmos , Árvores de Decisões , Peso Molecular , Redes Neurais de Computação , Proteínas , Química , ClassificaçãoRESUMO
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.
Assuntos
Aminoácidos , Química , Bactérias , Química , Proteínas de Bactérias , Química , Dipeptídeos , Química , Análise Discriminante , Temperatura Alta , Análise de Sequência de Proteína , TermodinâmicaRESUMO
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.
Assuntos
Aminoácidos , Química , Concentração de Íons de Hidrogênio , Modelos Químicos , Redes Neurais de Computação , Xilano Endo-1,3-beta-Xilosidase , QuímicaRESUMO
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.