Difference between revisions of "IGEM:Harvard/2007/Two Component Systems"

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== Completed Work ==
== Completed Work ==
See [[IGEM:Harvard/2007/Laboratory Notebooks/Two Component System|Two Component System Protocol]] for completed work.

Revision as of 14:16, 9 July 2007

FecA Two Component System

In the beginning, our team decided to use cells as biosensors, used to bind to targets, like breast cancer cells. We were later compelled to up the ante. So, we directed our thinking toward a 2 component system where E Coli would bind to a target and then produce a reporting signal. From this came the search for E Coli outer membrane receptors that are already part of a signally pathway, trying to do as little signal pathway re-engineering as possible. There are very few outer membrane receptors that suit our purpose. In fact, there is only one that we could find: FecA.

Using the FecA receptor from the outer membrane of Escherichia Coli, we hope to bind to given targets and produce a reporting signal. We originally planned to insert a random peptide library into the FecA protein and see which n-mer binds to our target. And this receptor-ligand binding should set off the FecA signalling pathway. The concerns presented are:

1) Where should the random library be inserted? FecA has several loops which serve as potential sites for insertion. Literature suggests that the conformational changes of loops 7 and 8 are most critical to binding and signal production. So then, would it be best to insert a library into these loops? Would it be best to use several locations and several libraries at once to get the correct response?

2) Random library insertion is a game of chance, given the number of possibilities that can be produced and biases in different methods of producing random libraries. And how does the insertion of new peptides effect specificity? THUS....

3) Computational methods of predicting which sequences produce the desired binding and signal are attractive, but are at this point unknown to us. These methods (IPRO, CHARMM, dead end elimination) have not been attempted with FecA, as far as we can tell. They are also fairly new methods. Our advantage is that FecA is well characterized liganded and unliganded, which is much easier than creating a protein from scratch that will bind to a particular target. In this vein, we have contacted several researchers who have produced papers on computational design of proteins, receptors in particular.

Planned Work

Completed Work

See Two Component System Protocol for completed work.


Ordered oligos to create a FecA promoter BioBrick that can be used to recombine with GFP to create a reporter system.

The oligos ordered were as follows: Construct 1 5’- GTTTCTTCGAATTCGCGGCCGCTTCTAGAGatttcaccactgtaaggaaaataattcttatttc – 3’


Grew bacteria (FecA, FecI, FecR) in liquid culture to prepare for Miniprep and sequencing tomorrow. Inoculation in 2 ml LB and 2 ul amp 50mg/ml 1000x.

6/28/07 Miniprep done. Sent for sequencing.

Emails sent to:

Dr. Homme W. Hellinga
Dr. Loren L. Looger

Penn State:
Dr. Hossein Fazelinia
Dr. Costas D. Maranas
Dr. Gregory L. Moore

PCR extension of constructs 1 and 2
-3 cycles of PCR
-diluted first to 500 uM, then to 20 uM
-15 uL of water, 10 uL of DNA, 25 uL of PCR master mix for each.
-two redundant reactions, R1 and R2
-R2 may be wrong becuase of volume errors

6/29/07 Reply from George Khoury of Penn State lab under Maranas. He has agreed to assist us with computational design.

Week of 7/1/07
-Continued contact with George Khoury, including a cell phone conversation w/ Ellenor on 7/3/07 and an in-person talk w/ Shaunak to be scheduled for 7/7/07. We've decided on lead (Pb) a small molecule ligand and Muc1 as a macromolecule (glycoprotein) ligand for FecA. Muc1 is a cancer antigen, a glycoprotein overexpressed on tumor cells. They are underglycolysated on cancerous cells.
- 7/5/07: Directed Mutagenesis of FecA and FecR to remove Pst1 restriction site.
- 7/6/07: Transformation of FecA' and FecR' (pending recovery of previous day's PCR product)
Also, Shaunak and George's meeting went well. Notes are under "Brainstorming"



Given the complexity of reengineering a receptor binding site such that it binds with a target other than its wild type ligand, we could create a "negative gate." The target will block ferric citrate from binding, thus turning off the FecA signal. Loops 9 and 10 look promising because they are accessible and do not participate in the binding of ferric citrate (they do not contain binding residues). Loop 11 would be another choice except that it has binding residues.


From convo between George K. and Shaunak
-Use IPRO for binding FecA to a small molecule or ion, can make a biosensor. Small molecule or metal ion should be simple, biologically relevant, and have no existing biosensors for it (something new). Shouldn't be too hard for IPRO to handle.
- Novel: bind FecA to a small polypeptide. Need small polypeptide ligand that has been crystallized bound to another protein before for "active site transplantation" (different from IPRO). It should be biologically relevant, but need structure bound to another protein.
-They would like us to test FecA against chemically similar molecules to give them a baseline etc.


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  11. Breidenstein E, Mahren S, and Braun V. Residues involved in FecR binding are localized on one side of the FecA signaling domain in Escherichia coli. J Bacteriol. 2006 Sep;188(17):6440-2. DOI:10.1128/JB.00741-06 | PubMed ID:16923915 | HubMed [FecAPorins11]
  12. Russ WP, Lowery DM, Mishra P, Yaffe MB, and Ranganathan R. Natural-like function in artificial WW domains. Nature. 2005 Sep 22;437(7058):579-83. DOI:10.1038/nature03990 | PubMed ID:16177795 | HubMed [FecAPorins12]
  13. Looger LL, Dwyer MA, Smith JJ, and Hellinga HW. Computational design of receptor and sensor proteins with novel functions. Nature. 2003 May 8;423(6936):185-90. DOI:10.1038/nature01556 | PubMed ID:12736688 | HubMed [FecAPorins13]
  14. Dwyer MA, Looger LL, and Hellinga HW. Computational design of a Zn2+ receptor that controls bacterial gene expression. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11255-60. DOI:10.1073/pnas.2032284100 | PubMed ID:14500902 | HubMed [FecAPorins14]
  15. Looger LL and Hellinga HW. Generalized dead-end elimination algorithms make large-scale protein side-chain structure prediction tractable: implications for protein design and structural genomics. J Mol Biol. 2001 Mar 16;307(1):429-45. DOI:10.1006/jmbi.2000.4424 | PubMed ID:11243829 | HubMed [FecAPorins15]
  16. Fazelinia H, Cirino PC, and Maranas CD. Extending Iterative Protein Redesign and Optimization (IPRO) in protein library design for ligand specificity. Biophys J. 2007 Mar 15;92(6):2120-30. DOI:10.1529/biophysj.106.096016 | PubMed ID:17208966 | HubMed [FecAPorins16]
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All Medline abstracts: PubMed | HubMed