Dataset Information

Improvement in Thermostability of an Achaetomium sp. Strain Xz8 Endopolygalacturonase via the Optimization of Charge-Charge Interactions.

ABSTRACT: Improving enzyme thermostability is of importance for widening the spectrum of application of enzymes. In this study, a structure-based rational design approach was used to improve the thermostability of a highly active, wide-pH-range-adaptable, and stable endopolygalacturonase (PG8fn) from Achaetomium sp. strain Xz8 via the optimization of charge-charge interactions. By using the enzyme thermal stability system (ETSS), two residues--D244 and D299--were inferred to be crucial contributors to thermostability. Single (D244A and D299R) and double (D244A/D299R) mutants were then generated and compared with the wild type. All mutants showed improved thermal properties, in the order D244A < D299R < D244A/D299R. In comparison with PG8fn, D244A/D299R showed the most pronounced shifts in temperature of maximum enzymatic activity (Tmax), temperature at which 50% of the maximal activity of an enzyme is retained (T50), and melting temperature (Tm), of about 10, 17, and 10.2°C upward, respectively, with the half-life (t1/2) extended by 8.4 h at 50°C and 45 min at 55°C. Another distinguishing characteristic of the D244A/D299R mutant was its catalytic activity, which was comparable to that of the wild type (23,000 ± 130 U/mg versus 28,000 ± 293 U/mg); on the other hand, it showed more residual activity (8,400 ± 83 U/mg versus 1,400 ± 57 U/mg) after the feed pelleting process (80°C and 30 min). Molecular dynamics (MD) simulation studies indicated that mutations at sites D244 and D299 lowered the overall root mean square deviation (RMSD) and consequently increased the protein rigidity. This study reveals the importance of charge-charge interactions in protein conformation and provides a viable strategy for enhancing protein stability.

SUBMITTER: Tu T

PROVIDER: S-EPMC4561696 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Improvement in Thermostability of an Achaetomium sp. Strain Xz8 Endopolygalacturonase via the Optimization of Charge-Charge Interactions.

Tu Tao T Luo Huiying H Meng Kun K Cheng Yanli Y Ma Rui R Shi Pengjun P Huang Huoqing H Bai Yingguo Y Wang Yaru Y Zhang Lujia L Yao Bin B

Applied and environmental microbiology 20150724 19

Improving enzyme thermostability is of importance for widening the spectrum of application of enzymes. In this study, a structure-based rational design approach was used to improve the thermostability of a highly active, wide-pH-range-adaptable, and stable endopolygalacturonase (PG8fn) from Achaetomium sp. strain Xz8 via the optimization of charge-charge interactions. By using the enzyme thermal stability system (ETSS), two residues--D244 and D299--were inferred to be crucial contributors to the ...[more]

PMID: 26209675

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Project description:BackgroundBiochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target.ResultsAn S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT.ConclusionsGASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.

Dataset Information

Improvement in Thermostability of an Achaetomium sp. Strain Xz8 Endopolygalacturonase via the Optimization of Charge-Charge Interactions.

Publications

Improvement in Thermostability of an Achaetomium sp. Strain Xz8 Endopolygalacturonase via the Optimization of Charge-Charge Interactions.

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