Plastic polymers are widely used in agriculture, industry, and our daily life because of their convenient and economic properties. However, pollution caused by plastic polymers, especially polyethylene (PE), affects both animal and human health when they aggregate in the environment, as they are not easily degraded under natural conditions. In this study, Enterobacter sp. D1 was isolated from the guts of wax moth (Galleria mellonella). Microbial colonies formed around a PE film after 14 days of cultivation with D1. Roughness, depressions, and cracks were detected on the surface of the PE film by scanning electron microscopy (SEM) and atomic force microscopy (AFM). Fourier transform infrared spectroscopy (FTIR) showed the presence of carbonyl functional groups and ether groups on the PE film that was treated with D1. Liquid chromatography-tandem mass spectrometry (LC-MS) also revealed that the contents of certain alcohols, esters, and acids were increased as a result of the D1 treatment, indicating that oxidation reaction occurred on the surface of the PE film treated with D1 bacteria. These observations confirmed that D1 bacteria has an ability to degrade PE.

Protein thermostability can be increased by some glycine to proline mutations in a target protein. However, not all glycine to proline mutations can improve protein thermostability, and this method is suitable only at carefully selected mutation sites that can accommodate structural stabilization. In this study, homology modeling and molecular dynamics simulations were used to select appropriate glycine to proline mutations to improve protein thermostability, and the effect of the selected mutations was proved by the experiments. The structure of methyl parathion hydrolase (MPH) from Ochrobactrum sp. M231 (Ochr‐MPH) was constructed by homology modeling, and molecular dynamics simulations were performed on the modeled structure. A profile of the root mean square fluctuations of Ochr‐MPH was calculated at the nanosecond timescale, and an eight‐amino acid loop region (residues 186–193) was identified as having high conformational fluctuation. The two glycines nearest to this region were selected as mutation targets that might affect protein flexibility in the vicinity. The structures and conformational fluctuations of two single mutants (G194P and G198P) and one double mutant (G194P/G198P) were modeled and analyzed using molecular dynamics simulations. The results predicted that the mutant G194P had the decreased conformational fluctuation in the loop region and might increase the thermostability of Ochr‐MPH. The thermostability and kinetic behavior of the wild‐type and three mutant enzymes were measured. The results were consistent with the computational predictions, and the mutant G194P was found to have higher thermostability than the wild‐type enzyme.

Protein engineering is commonly used to improve the robustness of enzymes for activity and stability at high temperatures. In this study, we identified four residues expected to affect the thermostability of Streptomyces sp. strain S9 xylanase XynAS9 through multiple-sequence analysis (MSA) and molecular dynamic simulations (MDS). Site-directed mutagenesis was employed to construct five mutants by replacing these residues with proline or glutamic acid (V81P, G82E, V81P/G82E, D185P/S186E, and V81P/G82E/D185P/S186E), and the mutant and wild-type enzymes were expressed in Pichia pastoris . Compared to the wild-type XynAS9, all five mutant enzymes showed improved thermal properties. The activity and stability assays, including circular dichroism and differential scanning calorimetry, showed that the mutations at positions 81 and 82 increased the thermal performance more than the mutations at positions 185 and 186. The mutants with combined substitutions (V81P/G82E and V81P/G82E/D185P/S186E) showed the most pronounced shifts in temperature optima, about 17°C upward, and their half-lives for thermal inactivation at 70°C and melting temperatures were increased by >9 times and approximately 7.0°C, respectively. The mutation combination of V81P and G82E in adjacent positions more than doubled the effect of single mutations. Both mutation regions were at the end of long secondary-structure elements and probably rigidified the local structure. MDS indicated that a long loop region after positions 81 and 82 located at the end of the inner β-barrel was prone to unfold. The rigidified main chain and filling of a groove by the mutations on the bottom of the active site canyon may stabilize the mutants and thus improve their thermostability.

Background: para-Nitrophenol (PNP), a priority environmental pollutant, is hazardous to humans and animals. However, the information relating to the PNP degradation pathways and their enzymes remain limited. Results: Pseudomonas sp.1-7 was isolated from methyl parathion (MP)-polluted activated sludge and was shown to degrade PNP. Two different intermediates, hydroquinone (HQ) and 4-nitrocatechol (4-NC) were detected in the catabolism of PNP. This indicated that Pseudomonas sp.1-7 degraded PNP by two different pathways, namely the HQ pathway, and the hydroxyquinol (BT) pathway (also referred to as the 4-NC pathway). A gene cluster (pdcEDGFCBA) was identified in a 10.6 kb DNA fragment of a fosmid library, which cluster encoded the following enzymes involved in PNP degradation: PNP 4-monooxygenase (PdcA), p-benzoquinone (BQ) reductase (PdcB), hydroxyquinol (BT) 1,2-dioxygenase (PdcC), maleylacetate (MA) reductase (PdcF), 4-hydroxymuconic semialdehyde (4-HS) dehydrogenase (PdcG), and hydroquinone (HQ) 1,2-dioxygenase (PdcDE). Four genes (pdcDEFG) were expressed in E. coli and the purified pdcDE, pdcG and pdcF gene products were shown to convert HQ to 4-HS, 4-HS to MA and MA to β-ketoadipate respectively by in vitro activity assays. Conclusions: The cloning, sequencing, and characterization of these genes along with the functional PNP degradation studies identified 4-NC, HQ, 4-HS, and MA as intermediates in the degradation pathway of PNP by Pseudomonas sp.1-7. This is the first conclusive report for both 4-NC and HQ- mediated degradation of PNP by one microorganism.

Background: An important aspect of protein design is the ability to predict changes in protein thermostability arising from single- or multi-site mutations. Protein thermostability is reflected in the change in free energy (ΔΔG) of thermal denaturation. Results: We have developed predictive software, Prethermut, based on machine learning methods, to predict the effect of single- or multi-site mutations on protein thermostability. The input vector of Prethermut is based on known structural changes and empirical measurements of changes in potential energy due to protein mutations. Using a 10-fold cross validation test on the M-dataset, consisting of 3366 mutants proteins from ProTherm, the classification accuracy of random forests and the regression accuracy of random forest regression were slightly better than support vector machines and support vector regression, whereas the overall accuracy of classification and the Pearson correlation coefficient of regression were 79.2% and 0.72, respectively. Prethermut performs better on proteins containing multi-site mutations than those with single mutations. Conclusions: The performance of Prethermut indicates that it is a useful tool for predicting changes in protein thermostability brought about by single- or multi-site mutations and will be valuable in the rational design of proteins.