Difference between revisions of "Team:Stony Brook/HP/Gold Integrated"

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<p>We met with Dr. Jarrod French, a researcher and assistant professor in the Department of Chemistry at Stony Brook University. During the conversation with Dr. French, we learned that several characteristics of our chosen bacteriocins were not feasible to experiment with and use for marketing in the future. In order to mass produce the bacteriocin as a product on the market, it would need to be expressed in E. coli and easily purified and stored. The key issues with our peptides and the implications we needed to consider were:</p>
 
<p>We met with Dr. Jarrod French, a researcher and assistant professor in the Department of Chemistry at Stony Brook University. During the conversation with Dr. French, we learned that several characteristics of our chosen bacteriocins were not feasible to experiment with and use for marketing in the future. In order to mass produce the bacteriocin as a product on the market, it would need to be expressed in E. coli and easily purified and stored. The key issues with our peptides and the implications we needed to consider were:</p>
<ul>
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<ul style="list-style-type:disc">
 
   <li>Some of our bacteriocins were too small. It would be favorable to pick bacteriocins that were  10 kDa or about 75 amino acids long, so that we could detect its presence on a gel. Bacteriocins are small in general, thus we needed to tag the peptides in order to increase their size.</li>
 
   <li>Some of our bacteriocins were too small. It would be favorable to pick bacteriocins that were  10 kDa or about 75 amino acids long, so that we could detect its presence on a gel. Bacteriocins are small in general, thus we needed to tag the peptides in order to increase their size.</li>
 
   <li>Some of our bacteriocins were cyclic and non-linear. It would be feasible for us to pick bacteriocin that were linear, so that the peptides are easier to tag, hence they could be spotted on a gel, indicating that they were being produced in the E. coli. This also would allow companies to manufacture our product and purify our bacteriocin efficiently, with a tag that is easily removable leaving a fully functional peptide behind.</li>
 
   <li>Some of our bacteriocins were cyclic and non-linear. It would be feasible for us to pick bacteriocin that were linear, so that the peptides are easier to tag, hence they could be spotted on a gel, indicating that they were being produced in the E. coli. This also would allow companies to manufacture our product and purify our bacteriocin efficiently, with a tag that is easily removable leaving a fully functional peptide behind.</li>

Revision as of 19:24, 31 October 2017

Stony Brook 2017

Our Initial Outlook

When we sat down to brainstorm the types of antimicrobial peptides we could use to tackle MRSA, we researched various scientific literature and listed a variety of bacteriocins that were plausible candidates to potentially kill MRSA. Unfortunately, we came upon various hurdles throughout each step of our journey. We seeked out the expertise from professors and medical professionals to analyze the issues at hand and integrate their suggestions into our work.

We met with Dr. Jarrod French, a researcher and assistant professor in the Department of Chemistry at Stony Brook University. During the conversation with Dr. French, we learned that several characteristics of our chosen bacteriocins were not feasible to experiment with and use for marketing in the future. In order to mass produce the bacteriocin as a product on the market, it would need to be expressed in E. coli and easily purified and stored. The key issues with our peptides and the implications we needed to consider were:

  • Some of our bacteriocins were too small. It would be favorable to pick bacteriocins that were 10 kDa or about 75 amino acids long, so that we could detect its presence on a gel. Bacteriocins are small in general, thus we needed to tag the peptides in order to increase their size.
  • Some of our bacteriocins were cyclic and non-linear. It would be feasible for us to pick bacteriocin that were linear, so that the peptides are easier to tag, hence they could be spotted on a gel, indicating that they were being produced in the E. coli. This also would allow companies to manufacture our product and purify our bacteriocin efficiently, with a tag that is easily removable leaving a fully functional peptide behind.
  • Some of our bacteriocins underwent post-translational modifications. Post-translational modifications would require more inserts of DNA that codes for the modifications, which is unfavorable for mass production of our bacteriocins using E. coli.

Through these alterations, we were lead to class IId bacteriocins. These are medium sized, linear peptides that require no post-translational modifications in order to kill the target bacteria.

Second Challenge: Creating the Hybrid

After taking the advice from Dr. French into consideration, we picked bacteriocins that were linear, medium-sized, and had no post-translational modifications. These bacteriocins were non-toxic to E. coli, hence we did not require additional immunity genes for our constructs. The next step was to create hybrids of these bacteriocins, but how do we know which of these bacteriocins could work with one another synergistically? We decided to make a phylogeny to understand and model how these peptides relate to one another. We decided to pick bacteriocins that were in the same group, class IId, as well bacteriocins that employed similar modes of action to penetrate the pathogenic strain. We met with Dr. Joshua Rest, a researcher and an associate professor in the Department of Ecology and Evolution at Stony Brook University. He advised us to not completely rule out other classes of bacteriocins and include a large number of bacteriocins from class IId, as well as non-class IId bacteriocins to our phylogenetic tree to confirm that the bacteriocins we picked are in fact more closely related to one another. He also explained the difference between character-based and distance-based models, and advised us to use a maximum likelihood method to construct our tree, with the inclusion of an outgroup to measure relative divergence times of each branch.