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Keywords
Bacillus subtilis and Bacillus licheniformis are widely used for the large-scale industrial production of proteins. These strains can efficiently secrete proteins into the culture medium using the general secretion (Sec) pathway. A characteristic feature of all secreted proteins is their N-terminal signal peptides, which are recognized by the secretion machinery. Here, we have studied the production of an industrially important secreted protease, namely, subtilisin BPN′ from Bacillus amyloliquefaciens. One hundred seventy-three signal peptides originating from B. subtilis and 220 signal peptides from the B. licheniformis type strain were fused to this secretion target and expressed in B. subtilis, and the resulting library was analyzed by high-throughput screening for extracellular proteolytic activity. We have identified a number of signal peptides originating from both organisms which produced significantly increased yield of the secreted protease. Interestingly, we observed that levels of extracellular protease were improved not only in B. subtilis, which was used as the screening host, but also in two different B. licheniformis strains. To date, it is impossible to predict which signal peptide will result in better secretion and thus an improved yield of a given extracellular target protein. Our data show that screening a library consisting of homologous and heterologous signal peptides fused to a target protein can identify more-effective signal peptides, resulting in improved protein export not only in the original screening host but also in different production strains.
The objectives of the present work are to characterize the Gas Metal Arc Welding process of DP 600 sheet steel and to summarize the modelling techniques. The time-temperature evolution during the welding cycle was measured experimentally and modelled with the softwaretool SimWeld. To model the phase transformations during the welding cycle dilatometer tests were done to quantify the parameters for phase field modelling by MICRESS®. The important input parameters are interface mobility, nucleation density, etc. A contribution was made to include austenite to bainite transformation in MICRESS®. This is useful to predict the microstructure in the fast cooling segments. The phase transformation model is capable to predict the microstructure along the heating and cooling cycles of welding. Tensile tests have shown the evidence of failure at the heat affected zone, which has the ferrite-tempered martensite microstructure.