Background
Antimicrobial peptides (AMPs) are promising candidates for the development of novel antibiotics, but it is difficult to produce sufficient quantities for preclinical and clinical studies due to their toxicity towards microbial expression hosts. To avoid laborious trial-and-error testing for the identification of suitable expression constructs, we have developed a small-scale expression screening platform based on a combinatorial plasmid library.
Results
The combinatorial library is based on the Golden Gate cloning system. In each reaction, six donor plasmids (each containing one component: a promoter, fusion partner 1, fusion partner 2, protease cleavage site, gene of intere... More
Background
Antimicrobial peptides (AMPs) are promising candidates for the development of novel antibiotics, but it is difficult to produce sufficient quantities for preclinical and clinical studies due to their toxicity towards microbial expression hosts. To avoid laborious trial-and-error testing for the identification of suitable expression constructs, we have developed a small-scale expression screening platform based on a combinatorial plasmid library.
Results
The combinatorial library is based on the Golden Gate cloning system. In each reaction, six donor plasmids (each containing one component: a promoter, fusion partner 1, fusion partner 2, protease cleavage site, gene of interest, or transcriptional terminator) were combined with one acceptor plasmid to yield the final expression construct. As a proof of concept, screening was carried out in Escherichia coli and Pichia pastoris to study the expression of three different model AMPs with challenging characteristics, such as host toxicity or multiple disulfide bonds. The corresponding genes were successfully cloned in 27 E. coli and 18 P. pastoris expression plasmids, each in a one-step Golden Gate reaction. After transformation, small-scale expression screening in microtiter plates was followed by AMP quantification using a His6 tag-specific ELISA. Depending on the plasmid features and the expression host, the protein yields differed by more than an order of magnitude. This allowed the identification of high producers suitable for larger-scale protein expression.
Conclusions
The optimization of recombinant protein production is best achieved from first principles by initially optimizing the genetic construct. The unrestricted combination of multiple plasmid features yields a comprehensive library of expression strains that can be screened for optimal productivity. The availability of such a platform could benefit all laboratories working in the field of recombinant protein expression.
