iGEM 2023 — Team washington

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{% block title %}Notebook{% endblock %}
{% block lead %}Document the dates you worked on your project. This should be a detailed account of the work done each day for your project.{% endblock %}
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# NOTEBOOK

[### April](#april)
[### May](#may)
[### June](#june)
[### July](#july)
[### August](#august)
[### September](#september)
[### October](#october)

April

Week 1

* April 1, 2023: First day of the iGEM season! Past UW iGEM members introduced the basics of iGEM to the new members this year. Project ideas were also presented to the members, and four main themes were decided: oral vaccines for pacific salmon, degradation of hazardous materials in Washington water bodies, nitrogen fixation, and artificial fish.
* April 5, 2023: Further discussions on the project ideas were made. During this meeting people conducted more literature review and discussed the interest and feasibility of each project idea.
* April 7, 2023: Members of wet lab started online laboratory safety training.

Week 2

* April 14, 2023: All members of wet lab finished their online laboratory safety training.
* April 15, 2023: First vote was cast and the top two choices were subunit vaccine and bioremediation; members who voted for each idea would then do more research and compose a presentation to convince the other side.
* April 16, 2023: The wet lab team decided to defend the subunit vaccine idea, and more literary reviews were conducted.

Week 3

* April 17, 2023: First lab orientation. Dr. Li Sun showed wet lab members the lab that we will be working at for the remainder of the season, as well as some safety information.
* April 18, 2023: Second lab orientation for the rest of the wet lab team.
* April 22, 2023: Second vote is casted and the final choice is bioremediation. The initial project idea is transforming algaes so that they will be able to take up and fix the nitrogen that is present in the water.

Week 4

* April 24, 2023: With a specific project in mind, the wet lab subteam decided to go into the lab and sort through the chemicals we can use. We first did a general clean-up and discarded all things that were expired or no longer in working condition. Then we cleaned the lab benches, racks and other surfaces with alcohol.
* April 25, 2023: Continuing our cleaning, we reorganized the contents of the drawers and cabinets, then applied new labels to those places.

May

Week 1

* May 1, 2023: More research regarding our project is done, and we started to look for mentors or advisors that can guide us through the project. Through our research we proposed that we could also make use of the metabolites that algae can produce.
* May 3, 2023: With more discussion of our project we realized that doing nitrogen fixation and repurposing metabolites might require too much time that will cause us to not be able to finish either of them. There were also concerns regarding the novelty of modifying algae for nitrogen fixation.
* May 6, 2023: New ideas for our project were presented, we were trying to determine what issue is most prominent to Washington State and what threatens the health of Washington’s water system.

Week 2

* May 13, 2023: After a week long research we eventually decided on PCB degradation as the final goal of our project.

Week 3

* May 15, 2023: Further research shows that cyanobacteria might be a better candidate for genetic transformation than algae, as they are easier to transform.
* May 20, 2023: The entire team agreed that cyanobacteria might be a better option for our project. We decided that our final project will be genetically transforming cyanobacteria so that we will be able to take up and break down PCBs present in Washington’s waste water, after we successfully transformed the cyanobacteria then we will create a biofilm for easy water filtration.

Week 4

* May 22, 2023: Now with a definite project down, we looked for professors that specialize in either PCB degradation, cyanobacteria, or biofilms.
* May 27, 2023: Researched on which types of PCB are more common in Washington State.
* May 28, 2023 to June 5, 2023: The team decided on the type of PCB that we wanted to target: PCB-11, PCB-209, PCB-132.

June

Week 1

* June 7, 2023 to June 19, 2023: Purchased the necessary materials for our transformation: PCB-11, PCB-209, PCB-132, LB broth, sodium thiosulfate, pET vectors. Some other materials were obtained through grants. Strain of bacteria was decided to be Synechocystis sp. PCC 6803. Did extensive literature review looking for sources of wild type breakdown pathway plasmid due to equipment and cost restrictions from purc

July

Week 3

* July 21, 2023: Durva, Ethan, and Wenyu went into the lab and prepared plates for future bacteria culturing.

![](https://static.igem.wiki/teams/4821/wiki/notebook/wetlab-results-1.jpg)

Week 4

* July 24, 2023: Written and revised protocol for natural bacteria transformation.
* July 25, 2023: Maddie, Druva, Ethan, and Anna met with Dr. Matthew Parsek, an expert in biofilms. In the meeting we learned that Dr. Parsek did his PhD work on PCB degradation and got some insights on PCB quantification.
* July 27, 2023: Wenyu and Anna met with Dr. Carrie Harwood, an expert in bacteria transformation. In the meeting she informed us that she does not have a lot of experience with marine bacteria transformation and referred us to Dr. Gabrielle Rocap but we were not able to get in contact with her.

August

Week 1

* August 1, 2023: More plates with antibiotics (kanamycin) were made in preparation for bacteria.

![](https://static.igem.wiki/teams/4821/wiki/notebook/wetlab-results-2.jpg)

Week 3

* August 14, 2023: Maddie, Wenyu, and Anna met with Stephen Blaskowski, an expert at marine bacteria, and learned about the hardship of transforming cyanobacteria. In addition to the previous hardship contoured with purchasing cyanobacteria the team decided it might be best to switch to E. coli for transformation, as they are a lot more easier to obtain.
* August 20, 2023: Contacted Ellie Reese, Dr. Beth Traxler, and Brandon Bol, for possible useful chemicals, E. coli transformation tips, and GC-MS machine training respectively.

Week 5

* August 29, 2023: Anna met with Ellie Reese the lab manager for SEFS Genetics Lab to see if they can provide any chemicals that would be helpful for our project, unfortunately none of the chemicals they were needed for our project.

September

Week 1

* September 1, 2023: Ethan and Anna met with Dr. Beth Traxler, an E. coli expert, and learned about the transformation and culturing process of E. coli.

Week 2

* September 7, 2023 to September 13, 2023: Maddie secured strands of competent E. coli from Yuxin Pan of Dr. Gu’s lab.

Week 3

* September 12, 2023: Sanjana and Anna attend the training session for operating the GC-MS machine.

October

Week 1

* October 2, 2023: Maddie and Wenyu started the process of transformation of E. coli with the mutated protein that the protein modeling subteam provided us with.
![](https://static.igem.wiki/teams/4821/wiki/notebook/wetlab-results-3.jpg)

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{% block title %}Engineering Success{% endblock %}
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# ENGINEERING

##### content

* [Introduction](#introduction)
* [Design](#design)
* [Build](#build)
* [Test](#test)
* [Learn](#learn)
* [References](#references)

### Introduction

---

PCB pollution is a dire environmental issue that demands our immediate attention.
To address this problem, we mutated an existing aerobic dehalogenase RdhANp to
confer it novel activity against 3 PCB congeners – PCB 11, 132 and 209. Our
project is strongly guided by the principles of the engineering cycle. The
procession of the drylab to wetlab pipeline represents a macro iteration of
design, build, test and learn while the details of our work can be further
split into micro iterations of the engineering cycle.

![](https://static.igem.wiki/teams/4821/wiki/engineering/engineering-cycle.png)

### MACRO DESIGN: EVALUATION METRICS + PROTEIN MODELING

---

#### Evaluation Matrix: Design

To ensure that our computationally generated mutations of RdhANp would indeed
dehalogenate selected PCB congeners, we began by developing an evaluation
protocol using Rosetta, a powerful command-line program suite for computational
macromolecular modeling. The initial evaluation protocol was generated based on
a basic Rosetta GALigandDock to recapitulate the reported activities of RdhANp
against a set of bromo/chloro organic halides.

---

#### Evaluation Matrix: Build

We downloaded the set of bromo/chloro organic halides from PubChem and docked
them against the crystal structure of RdhANp (PDB ID: 6ZY1 [1]). We then relaxed
the structure using the following Rosetta script:

$ROSETTA/main/source/bin/relax.static.macosclangrelease -s complex\_relaxed.pdb
-relax:constrain\_relax\_to\_start\_coords -relax:coord\_constrain\_sidechains
-relax:ramp\_constraints false -missing\_density\_to\_jump -extra\_res\_fa params/B12.params
-beta -extra\_res\_fa params/LIGAND.params -default\_repeats 2
-optimization:default\_max\_cycles 200

---

#### Evaluation Matrix: Test

The relaxed structure was docked using a basic GALigandDock protocol on “eval” mode,
which calculates the Rosetta energy scores of the entire structure without significant
repacking (allowing significant rotamer changes or sampling ligand binding space).
dH and dG obtained from the score files were our primary measures of interest. Unfortunately,
there was only some correlation between the dH/dG values with the reported activities of RdhANp
against the set of bromo/chloro halides we tested with this initial protocol, and
the ligand positions varied.

---

#### Evaluation Matrix: Learn & Redesign

With further research, we found that the dehalogenase facilitates catalysis
primarily via proton transfer at Y426 and the ideal distance between O of
Y426A and the halogenated carbon is 3.5 Å [2]. A 5.0 Å distance between
cobalamin and halogenated carbon was also an important determinant for effective
coordination [3]. We thus added these parameters into our relax protocol using
constraint files.

We also experimented with different relax parameters and constraint weights,
repeating the relaxation and docking steps to better recapitulate the reported
activities. We tried pairfitting the tested substrates with the ligand of another
RdhANp crystal structure (PDB ID: 6ZXX [1]). These repeated experiments led us
to a set of parameters that more accurately and reliably recapitulated the reported
activities with output dH values. Our final the relaxation command used was the following:

$ROSETTA/main/source/bin/relax.static.macosclangrelease -s $prefix.pdb
-relax:constrain\_relax\_to\_start\_coords -relax:coord\_constrain\_sidechains
-relax:ramp\_constraints true -missing\_density\_to\_jump -extra\_res\_fa B12.params
-extra\_res\_fa LIGAND.params -default\_repeats 2
-optimization:default\_max\_cycles 200 -beta
-constraints:cst\_fa\_file LIGAND.txt -constraints:cst\_fa\_weight 0.1

And our final constraint file was the following:

AtomPair OH 426A C9 1X SCALARWEIGHTEDFUNC 1000 HARMONIC 3.5 0.1

AtomPair CO 703A C9 1X SCALARWEIGHTEDFUNC 50 HARMONIC 5.0 0.1

Our working protocol suggests that a dH of -17 to -20 would represent a RdhANp
activity of 65-80%. This would hence be the threshold dH value we use for
determining computationally whether the RdhANp mutation sequence confers
sufficient activity against the selected PCB congeners.

---

#### Protein Design: Design

Once we have established a working protocol for evaluating RdhANp activity
against various substrates, we constructed models of the PCB congeners the
wet lab would be using (PCB 11, 132 and 209) with RdhANp. PCB 132 has two
docking conformations – para and ortho – which were treated as two separate
docked structures. We evaluated the initial activities of the structures with
our protocol. PCB 11 seemed to have the potential for highest activity with
the wild-type enzyme based on the dH values resulting from the docking evaluation.
All results obtained may be found in the results page of our wiki.

To redesign the protein-ligand interface such that RdhANp may possess greater
affinity for the PCB congeners, we looked into different softwares to use.
This is as although RosettaDesign is a plausible platform for making redesigns,
it is less effective for native protein structures which we are using. Our
graduate advisor, Preetham, strongly recommended that we use an updated version of
ProteinMPNN [4], a MPNN protocol for redesigning proteins to better interact with
ligands of choice which is yet to be published.

---

#### Protein Design: Build

We sent our PCB-docked enzyme structures to Preetham to generate the mutated
sequences. Mutations were constrained to be within 8 angstroms of the ligand
and 5 angstroms away from the cobalamin as ligand interaction with the cofactor
is critical to the dehalogenation activity.

As a preliminary test, the mutational sequences for RdhANp docked to
2,6-dichlorophenol were generated to verify whether the mutations generated
changed RdhANp energy according to our evaluation model. Then, Preetham helped us
generate 16 sequences for each of the PCB-RdhANp constructs. However, the first
set of mutations did not fix the catalytic tyrosine Y426 and it was mutated to a
phenylalanine. As phenylalanine is structurally similar to tyrosine, we reverted
the mutation and moved forward with our evaluations as well. At the same time, a
second set of mutations were generated with ligandMPNN, with Y426 fixed.

---

#### Protein Design: Test

These mutated sequences (both the Y426F and fixed Y426) were uploaded to Benchling
and aligned to identify the full set of mutations. Of the mutation sequences
generated, 70-80% were unique sequences. We then ran our evaluation protocol
against the PDB files for each sequence. While sequences resulted in worse dH,
most sequences improved the dH energy of our PCB-RdhANp structure. However,
only a select few crossed the dH threshold of -17 to -20. We selected the
sequences that resulted in the lowest dH to be sent for experimental validation.

---

#### Protein Design: Learn

The mutations which appeared most frequently were across all 4 docked PCB models
were K295L, E298L, K310A, P314Y, M315L, S423T, S425V, M426L, H457D, V556I and
A563G. Hence it is likely that the same mutation sequence may have good activity
against all the PCB congeners.

---

### MACRO BUILD: PLASMID DESIGN

---

#### Design

We choose the expression vector pET24(+) for E.coli, 5232 bp (commercially available, by novagen). It is kanamycin resistant, has C terminal 6xHis tag for later steps such as western blots for protein verification, restriction enzyme site, and is medium copy.

---

#### Build

We take the sequence from pm, codon optimization for E. coli k-12 using IDT codon optimization tool. Since the backbone lacks ribosome binding site/start codon, add ATG (Met) to each protein sequence, as well as a stop codon (TAA since it's most common for E. coli).

---

#### Test and Learn

Unfortunately as of the wiki freeze our experimental team was unable to validate the expression of the plasmids due to purchasing complications with Twist Bioscience.

### MACRO LEARN: PROTEIN REDESIGN

---

#### Protein Redesign: Design

While waiting on feedback from Wetlab, we decided to further
evaluate the mutation sequences generated by the MPNN design. This was to
assess whether the mutations made were indeed beneficial to improving RdhANp
activity against the target PCB congener.

---

#### Protein Redesign: Build

Each mutation of the sequences sent to the wet lab was reverted to the wildtype
sequence using Pymol and saved as a new structure. The structures were then
relaxed and scored using our evaluation metrics previously established.

---

#### Protein Redesign: Test

Using the original dH as a baseline for comparison, we found that indeed certain
mutations improved the dH energy while others have made it worse. Additionally,
reverting all mutations that made the dH score worse did not result in a
significant improvement in overall dH. Instead, only a unique combination of
reversions effectively improved the dH energy of the structures. We have thus
far managed to improve the dH of PCB 132 ortho and para structures with a unique
set of reversions

---

#### Protein Redesign: Learn

We learnt that not all mutations introduced by MPNN led to better scores and
that energy scores are not additive. Thus moving forward, an additional assessment
of each mutation of the best mutational sequence generated by MPNN should be
included to ensure that all mutations introduced are effectively improving the
activity of the protein against the target ligand.

### References

---

1. Halliwell, T., Fisher, K., Payne, K. A. P., Rigby, S. E. J., & Leys, D. (2020). Catabolic Reductive Dehalogenase Substrate Complex Structures Underpin Rational Repurposing of Substrate Scope. Microorganisms, 8(9), 1344. https://doi.org/10.3390/microorganisms8091344
2. Payne, K. A., Quezada, C. P., Fisher, K., Dunstan, M. S., Collins, F. A., Sjuts, H., Levy, C., Hay, S., Rigby, S. E., & Leys, D. (2015). Reductive dehalogenase structure suggests a mechanism for B12-dependent dehalogenation. Nature, 517(7535), 513–516. https://doi.org/10.1038/nature13901
3. Johannissen, L. O., Leys, D., & Hay, S. (2017). A common mechanism for coenzyme cobalamin-dependent reductive dehalogenases. Physical chemistry chemical physics : PCCP, 19(8), 6090–6094. https://doi.org/10.1039/c6cp08659d
4. Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., Leung, P. J. Y., Huddy, T. F., Pellock, S., Tischer, D., Chan, F., Koepnick, B., Nguyen, H., Kang, A., Sankaran, B., Bera, A. K., … Baker, D. (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science (New York, N.Y.), 378(6615), 49–56. https://doi.org/10.1126/science.add2187

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{% block title %}Experiments{% endblock %}
{% block lead %}Describe the research, experiments, and protocols you used in your iGEM project.{% endblock %}
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# EXPERIMENTS

##### content

* [transformation of E. coli](#transform)
* [Verification of transformation](#verify)
* [exposure of E. coli to PCB-11](#exposure)
* [Measurement of PCB Products in Wastewater Using GCMS](#measure)

### Transformation of E. coli Using Heat Shock Method

---

1. Mix DNA into competent cells in a microcentrifuge.
2. Incubate the competent cell/DNA mixture on ice for 20-30 mins.
3. Heat shock each transformation tube by placing the bottom 1/2 to 2/3 of the tube
   into a 42°C water bath for 30-60 secs
4. Put the tubes back on ice for 2 min.
5. Add 250-1,000 μl LB or SOC media (without antibiotic) to the bacteria and
   grow in 37°C shaking incubator for 45 min.
6. Plate some or all of the transformation onto a LB agar plate containing
   the appropriate antibiotic.
7. Incubate plates at 37°C overnight.

### Verification of Transformation via Colony PCR

---

1. Put sample of bacteria colony into thermal cycler along with necessary
   ingredients for PCR (dNTP, primers, buffer, Taq Polymerase) and heat to
   95 C for 30 seconds.
2. Use the thermal cycler to achieve the following temperatures for 30 cycles
3. Use a sample of the PCR product to confirm length of the DNA insert via
   gel electrophoresis

### Exposure of E. Coli to PCB-11

---

1. Add 25 uL of PCB-11 to 250 uL of water and add this mixture to E. Coli plate.
2. Place E. Coli plate in 30℃ incubator for 24 hours.

### Measurement of PCB Products in Wastewater Using GCMS

---

1. Add 10 uL of PCB water mixture from E. Coli plate to a centrifuge tube and
   spin down tube in centrifuge at 5,000 rpm for 10 min.
2. Add 100 uL of cyclohexane to GCMS tube and add 1 uL of water
   from centrifuge tube.
3. Measure PCB concentration using GCMS (Gas Chromatography
   Mass Spectrometry).

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{% block title %}Results{% endblock %}
{% block lead %}You can describe the results of your project and your future plans here.{% endblock %}
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# RESULTS

##### content

* [Protein modeling](#p-modeling)
* [Laboratory results](#lab)
* [Next Steps](#next-step)
* [References](#reference)

### Protein modeling experiments

---

The protein modeling experiments gave two main results:

* a model using Rosetta scripts to evaluate the theoretical activity of a
  mutated dehalogenase with different substrates
* mutated sequences for dehalogenase that score better on the aforementioned
  evaluation model

These models and mutated sequences have not yet been validated in a wet lab
environment, which is the next step. For more information about these modeling
experiments, please visit the [modeling](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27model%27%29%20%7D%7D) page.

See the files below to view the data tables summarizing the experiments.

---

#### Result #1

*Results for experiments editing dock and relaxation protocol parameters to
develop an evaluation model.*

**Summary:** most favorable energy (dH) scores found using 6ZXX structure,
relaxed with two distance constraints with SCALARWEIGHTEDFUNC = 50, and
the relaxation ramp parameter = TRUE, and no relax:coord\_constrain\_sidechains flag.

---

#### Result #2

*Mutations generated for three ligands (total of four orientations) using
ligand MPNN protocols. Includes mutation reversion experiments.*

---

#### Result #3

*Mutations generated for three ligands (total of four orientations) using
resfiles and a Rosetta Scripts protocol.*

### Laboratory results

---

*Figure 1. Gas chromatography mass spectroscopy (GC-MS) data for PCB-11 in cyclohexane.*

![](https://static.igem.wiki/teams/4821/wiki/results/wetlab-results-cropped.jpg)

![](https://static.igem.wiki/teams/4821/wiki/notebook/wetlab-results-4.png)

*A. Gas chromatography read out indicates the peak at 10.353 minutes has the greatest abundance.*

*B. Mass spectrometry read out of the largest peak indicates a mass of 222 g/mol which is the mass of PCB-11 as well as the library structure closely resembling that of PCB-11.*

---

Due to the difficulty of procuring cyanobacteria and plasmids in time for our project, we were unable to finish transformations and test the effectiveness of our modified dehalogenase. If we were able to successfully transform the E. Coli, we would measure the amount of PCB-11 in the water before and after exposure to the transformed E. Coli with the modified dehaolgenase protein and identify if there was a significant change. Using gas chromatography mass spectroscopy (GC-MS) we were able to verify that PCB-11 could be detected and identified by GC-MS making it an ideal method to measure the amount of PCB-11 was dechlorinated after being exposed to E Coli. transformed with the modified dehalogenase.

### Next Steps

---

Once we have confirmed that our protein design is efficient at breaking down
our selected PCB congeners, our next steps on the pathway towards implementation
are transforming cyanobacteria to express our enzyme, engineering the cyanobacteria
to form a stable biofilm, and testing our biofilm in wastewater conditions.
We plan to use *Synechocystis sp.* PCC 6803 as our cyanobacteria due to its
status as a prominent model organism, as well as its well documented ability
to form biofilms [1]. We are excited to move forward with our project post
iGEM in order to turn our project into a reality.

### References

---

1. Axenic Biofilm Formation and Aggregation by Synechocystis sp. Strain PCC
   6803 Are Induced by Changes in Nutrient Concentration and Require Cell
   Surface Structures - PMC

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