<p>The design of the gene inserts and their addition into the plasmid was made by our team (REFERENCE DESIGN TAB OF WIKI). Preliminary protocols for procedures for our lab work were also created, including the ligation of the insert, PFAS testing, and Lactose testing (as a control). These protocols included basic lab techniques, such as Miniprep, PCR Purification columns, transformation, etc. In this cycle of the experiment, we were interested in making sure that the ligation procedure would result in our desired insert.</p>
<p>In the lab, the dried DNA delivered by Twist was resuspended and an aliquot of resuspended parts A, B, C were taken for assembly, with the rest to be stored in plasmids (reference Storage Vectors). Part A was digested with SpeI, B with XbaI, and C with XbaI overnight. A PCR cleanup column procedure was performed on all 3 parts. A and B were then ligated with T4 DNA Ligase to form part AB. AB was then digested with SpeI before going through a PCR cleanup column. After cleanup, AB was ligated with C to form ABC and digested with EcoRI and PstI to prepare it for insertion into a vector backbone. However, during the final PCR cleanup procedure for part ABC, Wash Buffer was added instead of Binding Buffer on accident. This resulted in a low yield for the ABC insert, necessitating multiple cycles of recovery cleanup through gradual addition of Binding Buffer and ethanol evaporation.</p>
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<h4style="margin-top: 3vh;">Final test of ABC ligation</h4>
<p>We loaded the ABC construct created in this cycle as well as the ABC construct created in Cycle 1 into the Bioanalyzer to compare band intensities and locations.</p>
<p>Bioanalyzer results for the ABC construct created in Cycle 1. The Bioanalyzer showed that the A, B, and C parts used in Cycle 1 did not ligate at all. In the 3rd and 2nd engineering cycles, the construct did partially ligate to form AB but did not successfully incorporate part C. This means no detectable amounts of the full ABC construct was made in our lab. We decided not to pursue another cycle due to extreme time constraints.</p>
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<h4style="margin-top: 3vh;">Test</h4>
<p>The ligated fragments were run through a bioanalyzer, which showed sufficient amounts of ligation and a high concentration of ligated plasmid. The following day, the plate growth was also verified (shown below).</p>
<p>Although no work with these storage bacteria has been done, we did modify our lab expectations for this competition season. We originally expected to do transformations and testing for both the PFAS sensitive and Lactose sensitive plasmids, but after the lab work done, we realized that only the transformation of the PFAS sensitive insert was plausible in that timeframe.</p>
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<h3style="margin-top: 5vh;">Build</h3>
<p>With the simulation design in place, the next step was to construct the script. Our first task was to dock the ligand and protein, which led us to discover the ROSIE server. This powerful tool generated over 200 iterations, and from this pool, we carefully selected the top 10 for inclusion in our simulation. Given our limited experience, we often defaulted to simplicity and used default settings, even opting to exclude water from the simulation.</p>
<p>When we attempted to execute the script, we encountered a series of errors. These ranged from minor syntax issues and deprecated libraries to a more significant problem: OpenMM's inability to recognize our ligand and assign appropriate charges to it. To name some errors we received, our original idea to use Swissdock did not work because it did not accept our input files, resulting in us using ROSIE. Regarding our input files, OpenMM could not identify the name of the ligand residue, and so it was an unknown, causing errors. One of the most fatal errors we recieved was that when ever we tried to do something related to the bonds, it could not recognise the bond and or its charge.</p>
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<h3style="margin-top: 5vh;">Test</h3>
<p>With the modified simulation script in place, we ran the simulation once again. This time, however, we encountered new challenges, as the generated values did not align with our expectations. These discrepancies ranged from minor issues, such as temperature fluctuations, to more significant problems, including negative box volumes that raised questions about the theoretical space occupied by the simulation.</p>
<p>In the second cycle, our journey with OpenMM led to a deeper understanding of forcefield design, partial charge assignment, and simulation optimization. We learned valuable lessons about fine-tuning parameters and the importance of meticulous adjustments to achieve meaningful and reliable results in molecular simulations. We also learned how simulations can be inaccurate based on how accurate the information being fed to them is, i.e. forcefields. And forcefields in their own right are hard to develop and even harder to get right, so considering the fact that some info in the forcefield could be incorrect, the data returned to us made sense to us, and though some outputs were completely out of the question such as a negative box volume, it was just a simple error! We incorrectly stored the data for that column, causing a slight confusion. We also learned that the ensemble average of a property (like temperature) calculated over multiple simulation trajectories should converge to the expected macroscopic value. In other words, if your simulated system was big enough to stick a real thermometer into it (like a protein in a buffer on your lab bench), the temperature is converged to a single, macroscopic value that is read out on your thermometer device. However, at the level of a simulation, a single trajectory is just one possible realization of the system's behavior over time. Fluctuations in any single trajectory are expected due to the inherent statistical nature of the system. Over multiple trajectories or a long enough time, the average of these fluctuations should converge to the expected value.</p>
