Extracellular overexpression of recombinant Thermobifida fusca cutinase by alpha-hemolysin secretion system in E. coli BL21(DE3)

<p>Abstract</p> <p>Background</p> <p>Extracellular expression of proteins has an absolute advantage in a large-scale industrial production. In our previous study, <it>Thermobifida fusca </it>cutinase, an enzyme mainly utilized in textile industry, was expressed via type II secretory system in <it>Escherichia coli </it>BL21(DE3), and it was found that parts of the expressed protein was accumulated in the periplasmic space. Due to the fact that alpha-hemolysin secretion system can export target proteins directly from cytoplasm across both cell membrane of <it>E. coli </it>to the culture medium, thus in the present study we investigated the expression of cutinase using this alpha-hemolysin secretion system.</p> <p>Results</p> <p><it>T. fusca </it>cutinase was fused with the specific signal peptide of alpha-hemolysin scretion system and expressed in <it>E. coli </it>BL21(DE3). In addition, HlyB and HlyD, strain-specific translocation components of alpha-hemolysin secretion system, were coexpressed to facilitate the enzyme expression. The cultivation of this engineered cell showed that cutinase activity in the culture medium reached 334 U/ml, which is 2.5 times that from type II secretion pathway under the same culture condition. The recombinant cutinase was further purified. Biochemical characterization of purified enzyme, which had an α-hemolysin secretion pathway signal peptide attached, had substrate specificity, pH and temperature profile, as well as application capability in bioscouring similar to that of wild-type cutinase.</p> <p>Conclusions</p> <p>In the present study, <it>T. fusca </it>cutinase was successfully secreted to the culture media by α-hemolysin secretion system. This is the first report of cutinase being efficiently secreted by this pathway. Due to the limited cases of successful expression of industrial enzyme by <it>E. coli </it>α-hemolysin secretion system, our study further explored the utilization of this pathway in industrial enzymes.</p

Resonant Quantum Magnetodielectric Effect in Multiferroic Metal-Organic Framework [CH3NH3]Co(HCOO)3

We report the observation of both resonant quantum tunneling of magnetization (RQTM) and resonant quantum magnetodielectric (RQMD) effect in the perovskite multiferroic metal-organic framework [CH3NH3]Co(HCOO)3. An intrinsic magnetic phase separation emerges at low temperatures due to hydrogen-bond-modified long range super-exchange interaction, leading to the coexistence of canted antiferromagnetic order and single-ion magnet. Subsequently, a stair-shaped magnetic hysteresis loop along the [101] direction characterizing the RQTM appears below the magnetic blocking temperature. More interestingly, the magnetic field dependence of dielectric permittivity exhibits pronounced negative peaks at the critical fields corresponding to the RQTM, a phenomenon termed the RQMD effect which enables electrical detection of the RQTM. These intriguing properties make the multiferroic metal-organic framework a promising candidate for solid-state quantum computing.Comment: 13 pages, 4 figure

PP-012 Novel blaCTX-M-79 gene from community isolates in association with ISEcp1 in Shenyang, China

Digitalitzat per Artypla

Protein subcellular localization prediction based on compartment-specific features and structure conservation

BACKGROUND: Protein subcellular localization is crucial for genome annotation, protein function prediction, and drug discovery. Determination of subcellular localization using experimental approaches is time-consuming; thus, computational approaches become highly desirable. Extensive studies of localization prediction have led to the development of several methods including composition-based and homology-based methods. However, their performance might be significantly degraded if homologous sequences are not detected. Moreover, methods that integrate various features could suffer from the problem of low coverage in high-throughput proteomic analyses due to the lack of information to characterize unknown proteins. RESULTS: We propose a hybrid prediction method for Gram-negative bacteria that combines a one-versus-one support vector machines (SVM) model and a structural homology approach. The SVM model comprises a number of binary classifiers, in which biological features derived from Gram-negative bacteria translocation pathways are incorporated. In the structural homology approach, we employ secondary structure alignment for structural similarity comparison and assign the known localization of the top-ranked protein as the predicted localization of a query protein. The hybrid method achieves overall accuracy of 93.7% and 93.2% using ten-fold cross-validation on the benchmark data sets. In the assessment of the evaluation data sets, our method also attains accurate prediction accuracy of 84.0%, especially when testing on sequences with a low level of homology to the training data. A three-way data split procedure is also incorporated to prevent overestimation of the predictive performance. In addition, we show that the prediction accuracy should be approximately 85% for non-redundant data sets of sequence identity less than 30%. CONCLUSION: Our results demonstrate that biological features derived from Gram-negative bacteria translocation pathways yield a significant improvement. The biological features are interpretable and can be applied in advanced analyses and experimental designs. Moreover, the overall accuracy of combining the structural homology approach is further improved, which suggests that structural conservation could be a useful indicator for inferring localization in addition to sequence homology. The proposed method can be used in large-scale analyses of proteomes

The open banking era:An optimal model for the emergency fund

The COVID-19 outbreak has negatively impacted the income of many bank users. Many users without emergency funds had difficulty coping with this unexpected event and had to use credit or apply to the government for bailout funds. Therefore, it is necessary to develop spending plans and deposit plans based on transaction data of users to assist them in saving sufficient emergency funds to cope with unexpected events. In this paper, an emergency fund model is proposed, and two optimization algorithms are applied to solve the optimal solution of the model. Secondly, an early warning mechanism is proposed, i.e. an unexpected prevention index and a consumption index are proposed to measure the ability of users to cope with unexpected events and the reasonableness of their expenditure respectively, which provides early warning to users. Finally, the model is experimented with real bank users and the performance of the model is analysed. The experiments show that compared to the no-planning scenario, the model helps users to save more emergency funds to cope with unexpected events, furthermore, the proposed model is real-time and sensitive.</p