Team:Paris Bettencourt/Optimization
Optimization
Based on the results of the Testing and Modeling groups, we arrived at the following simple model for how StarCores function.
| Box 1: Why are StarCore Proteins Difficult to Express? |
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Based on the results of the Testing and Modeling groups, we arrived at the following simple model for how StarCores function. |
With these considerations in mind, we set out to improve on the basic StarCore designs developed in the Design Section. Our method of choice was targeted saturation mutagenesis. We developed a 12 000 variant library of StarCores varying mainly in the number and location of their positive charges. We systematically screened this library for activity, then explored the key determinants of activity with a custom-made machine learning software.
We were expecting that the free AMPs behaved differently when they are fused to a protein scaffold. The previously available literature that describe free AMPs does not seem to apply for our constructs. Consequently, we choose to devise a new model for studying the optimization of fused AMPs. Therefore, we design a library of 12k variant that we attached to mCherry through a linker in order to optimize the already characterized effectiveness of core-AMPs.
Results
Design of the AMP Twist Library
At the core of the StarCore library are three AMPs: Ovispirin, X, Y. We chose to fuse each AMP to the fluorescent protein mCherry. This allowed us to easily quantify the protein expression level. It also served as a “simulated core” that allowed us to test AMP activity in the context of a large fusion protein. As we learned in the production section, protein expression and folding are critical to obtaining an effective StarCore.
We chose to focus library diversity on positively charged residues. Charge density is known to critically affect both protein folding and AMP function. Therefore, we systematically generated mutations to move, concentrate and amplify positive charges. These mutations are described in figure 1.
Screening 12 000 AMP clones for specific activity
We cloned the DNA sequence library into a T7-based expression vector and transformed it into a strain of self-lysing E. coli. Cells were grown to mid-log phase then induced for StarCore expression and self-lysis. The resulting lysates were added to fresh cultures of E. coli. Peptide expression was measured by mCherry fluorescence and effects on growth were quantified by OD.
Of 12 000 total clones screened, we selected the 200 most effective for sequencing and characterization.
Methods
Library design
Roughly 12 000 variants were designed using 5 templates from naturally occurring peptides. 3 types of variation rules were applied:
Due to their short length, AMPs were expressed as a fusion protein to prevent post-transcriptional degradation. The fluorescent reporter mCherry was chosen for this purpose. A long flexible linker was chosen to give the AMPs freedom to interact with the membrane.
| Box 1: Library Design |
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Roughly 12 000 variants were designed using 5 templates from naturally occurring peptides. 3 types of variation rules were applied: |
Due to their short length, AMPs were expressed as a fusion protein to prevent post-transcriptional degradation. The fluorescent reporter mCherry was chosen for this purpose. A long flexible linker was chosen to give the AMPs freedom to interact with the membrane.
Twist Library Cloning
We first used cold fusion assembly to construct a low-copy expression vector containing mCherry. Into this vector, the Twist DNA library was cloned using Golden Gate assembly.
Once our pDuet-mCherry vector obtained, we pursue the cloning procedure by performing Golden Gate assembly of the all the library. This method enabled us to join the 12k variants in a one pot reaction. For this, 60 cycles of digestion/ligation were realized to minimize the level of empty vector. High efficiency electrocompetent cells were used to recover the all library size. After plasmid purification, the plasmids containing the library were transformed once more into expression cells.
paris-bettencourt-2018@cri-paris.org 
