iGEM 2020 — Team ICS_BKK

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# Team:ICS BKK/Notebook

# Notebook

Document the dates you worked on your project. This should be a detailed account of the work done each day for your project.

### What should this page have?

* Chronological notes of what your team is doing.
* Brief descriptions of daily important events.
* Pictures of your progress.
* Mention who participated in what task.

### Inspiration

You can see what others teams have done to organize their notes:

* [2018 Munich](https://2018.igem.org/Team%3AMunich/Notebook)
* [2014 ATOMS-Turkiye](https://2014.igem.org/Team%3AATOMS-Turkiye/Notebook)
* [2014 Tec Monterrey](https://2014.igem.org/Team%3ATec-Monterrey/ITESM14_project.html#tab_notebook)
* [2014 Kyoto](https://2014.igem.org/Team%3AKyoto/Notebook/Magnetosome_Formation#title)
* [2014 Cornell](https://2014.igem.org/Team%3ACornell/notebook)

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# Team:ICS BKK/Engineering

Team:ICS BKK/Engineering - 2020.igem.org

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Team

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[Attributions](https://2020.igem.org/Team%3AICS_BKK/Attributions)

Project

[Description](https://2020.igem.org/Team%3AICS_BKK/Description)
[Experiments](https://2020.igem.org/Team%3AICS_BKK/Experiments)
[Engineering](https://2020.igem.org/Team%3AICS_BKK/Engineering)
[Results](https://2020.igem.org/Team%3AICS_BKK/Results)
[Contribution](https://2020.igem.org/Team%3AICS_BKK/Contribution)
[Implementation](https://2020.igem.org/Team%3AICS_BKK/Implementation)

Human Practices

[Human Practices](https://2020.igem.org/Team%3AICS_BKK/Human_Practices)
[Education](https://2020.igem.org/Team%3AICS_BKK/Education)
[Collaborations](https://2020.igem.org/Team%3AICS_BKK/Collaborations)

[Judging Form](https://igem.org/2020_Judging_Form?team=ICS_BKK)

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[**Research**](#Research)
[**Imagine**](#Imagine)
[**Design**](#Design)
[**Build**](#Build)
[**Test**](#Test)
[**Learn**](#Learn)
[**Improve**](#Improve)

# **ENGINEERING**

  Working in a wet lab may not seem to have much involvement with engineering. However, in order to be successful, our wet lab process follows the engineering design cycle of research, imagine, design, build, test, learn, and improve. Hence, the process of phage therapy, or more specifically biopanning and ELISA, requires incorporation of engineering principles to a certain extent.

## **RESEARCH**

  The first and foremost step to be able to successfully work efficiently in the wet lab is to understand and know what it is that we are doing. To understand, one must research! As high school students, it would be impossible for us to be able to process scientific protocols and analysis, let alone in a limited amount of time, without having to research. All wet lab team members spent hours browsing numerous scientific papers in order to acquire a basic understanding of the process. We researched more on how biopanning is performed, ELISA is analyzed, and gene sequencing is utilized with all focus surrounding our target bacteria.

![](https://static.igem.org/mediawiki/2020/0/07/T--ICS_BKK--wetlabteampic.jpg)

## **IMAGINE**

  Before starting the wet lab, we had to imagine how we could get sequences of the target protein, or receptor binding protein, since that is the final result that we wish to achieve from the lab. It is crucial for us to imagine how each part of the process will ultimately form the big picture. Starting from plotting a growth curve of *S. aureus*, conducting biopanning of the KM13 phages, precipitating the phages, analyzing ELISA, and finally sequencing the gene sequence of the receptor binding domain.

  In a more practical manner, due to our work schedule in the wet lab overlapping with our school days, the wet lab team had to alternate different members to go work at the wet lab on each day. Each day working at the wet lab was filled with meticulous details and packed with immense progress. With a different member going to the wet lab each day and having to continue the previous day’s work, a lot of coordination and imagination had to take place. Every member had to report what they did along with sending the lab notes in order for the next person to understand how to continue work on the next day. Since we did not experience the work ourselves first-hand, we had to imagine the different steps that took place.

![](https://static.igem.org/mediawiki/2020/a/a1/T--ICS_BKK--ingwetlabpic.jpg)

## **DESIGN**

  Ideally, biopanning should be completed three times for three weeks with a week of ELISA after that. However, since our team had an extremely limited amount of time to work in the wet lab due to COVID-19 disrupting our initial plan, we were only able to complete biopanning once for one week along with three days of ELISA. Thus, we had to make changes to the protocol design and schedule in order to finish the work in time. In other words, we had to design a new schedule to accommodate the limited time we have.

![](https://static.igem.org/mediawiki/2020/0/01/T--ICS_BKK--pannwetlabpic.jpg)

## **BUILD**

  The biopanning process is made up of many separate steps that build up on each other. We had to start with growing the *S. aureus* so that we are able to use it during its log curve or the phase where its growth is favorable for the experiment. This is then later used in the biopanning process for the KM13 phage to bind to the bacteria. Once achieved, ELISA is conducted to analyze its binding affinity. Finally, the selected phage is sent to be sequenced so that the binding domain can be further analyzed. You see, all these steps are all equally important since the next step could not be done without the previous one completed. They all build up on each other to create the final result.

  A more specific example is the ELISA process itself. To simply explain, this procedure consists of a series of steps building on each other. We had to bound the phages onto the *S. aureus*. After that, we added an anti-KM13 antibody as our primary antibody, HRP conjugate as our secondary antibody, and finally the TMB substrate. The anti-KM13 antibody binds to the phages with the KM13 phage attached. The HRP (horseradish peroxidase) conjugate binds to the anti-KM13 phage to show us the level of affinity present. Finally, the TMB substrate plays a role in changing the solution to a blue color. This blue color will then be reacted in an oxidation reaction to change into a yellow color, indicating that a phage has successfully been bound to *S. aureus*.

![](https://static.igem.org/mediawiki/2020/2/2d/T--ICS_BKK--wetlabpic3.jpg)

## **TEST**

  In the wet lab, we conducted the catalase test which is a biochemical test that detects if the organism produces the catalase enzyme. We tested it on *S. aureus* which is a catalase-positive organism. This meant that when we dropped in hydrogen peroxide onto *S. aureus*, catalase would hydrolyze the hydrogen peroxide into water and gaseous oxygen, resulting in a reaction of gas bubbles. This test ensures if the organism that we are working with is purely that organism and not contaminated with any other species.

![](https://static.igem.org/mediawiki/2020/8/84/T--ICS_BKK--wetlabpic2.jpg)

## **LEARN**

  Of course, the wet lab process has definitely made us learn more than we could ever imagine. From simple procedures such as how to use a pipette to more complex protocols of washing phages, we all have taken snippets of new knowledge. Along the process, our lab instructor continued to explain how to conduct the different processes, why they are done, and where they fit in the final result.

  One of the biggest learning experiences was when we conducted biopanning. Since it is quite a complex process, there were times when the biopanning results failed. Once, there were no visible *S. aureus* bacteria that grew while another time, there were too many colonies on the plate! Hence, we learned from our mistakes until we successfully completed the biopanning results through this learning curve.

![](https://static.igem.org/mediawiki/2020/2/28/T--ICS_BKK--khaooatwetlabpic.jpg)

## **IMPROVE**

  The wet lab can definitely be improved in many ways. Due to COVID-19, we could not receive the bacteria we wanted to use which was *S. pyogenes*; instead we had to use *S. aureus*. Also, our results could be more accurate and reliable if we had time to repeat biopanning three times instead of just once. In terms of improvements whilst working in the lab, we can always minimize the errors that we made. For instance, we spilled a test tube of a specific phage, making it unable to be used anymore. Thus, these mistakes can be minimized and our methodology can be improved to ensure more reliable results.

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International Community School of Bangkok

Contact:
[igem.icsbkk@gmail.com](igem.icsbkk%40gmail.com)

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# Team:ICS BKK/Experiments

Team:ICS BKK/Human Practices - 2020.igem.org

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Project

[Description](https://2020.igem.org/Team%3AICS_BKK/Description)
[Experiments](https://2020.igem.org/Team%3AICS_BKK/Experiments)
[Engineering](https://2020.igem.org/Team%3AICS_BKK/Engineering)
[Results](https://2020.igem.org/Team%3AICS_BKK/Results)
[Contribution](https://2020.igem.org/Team%3AICS_BKK/Contribution)
[Implementation](https://2020.igem.org/Team%3AICS_BKK/Implementation)

Human Practices

[Human Practices](https://2020.igem.org/Team%3AICS_BKK/Human_Practices)
[Education](https://2020.igem.org/Team%3AICS_BKK/Education)
[Collaborations](https://2020.igem.org/Team%3AICS_BKK/Collaborations)

[Judging Form](https://igem.org/2020_Judging_Form?team=ICS_BKK)

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[**Computational Work Overview**](#Computational-Work-Overview)
[**Wet Lab Summary**](#Wet-Lab-Summary)
[**Methodology Overview**](#Methodology-Overview)
[**Wet Lab Protocol**](#Wet-Lab-Protocol)
[**References**](#References)

# **EXPERIMENTS**

## **COMPUTATIONAL WORK OVERVIEW**

  The purpose of the computational portion of this lab is to be able to identify specific properties of mutated Phage P2 that are able to bind to *S. pyogenes*. The overview of the method is as follows:

  1. Calculate binding energy of Phage P2 with *S. pyogenes* (Control)

  2. Create 450 Phage P2 mutants by permuting residues crucial to bacteria receptor binding

  3. Compute binding energy of the 450 candidates with *S. pyogenes*

  4. Compare the binding energy of the best candidate with the control

  5. Calculate binding energy of native phage (Phage A25) with *S. pyogenes* and compare with the binding energy of the best candidate

  We utilized three different softwares: The Scripps Research Insitute’s Autodock Vina [1], BIOVIA Discovery Studio [2], and MODELLER [3]. Autodock Vina is the main tool we used to predict the binding affinity between the phage and the bacteria.

## **WET LAB SUMMARY**

  In order to acquire the gene sequence responsible for the binding mechanisms of bacteriophages to Streptococcus pyogenes, we used biopanning, enzyme-linked immunosorbent assay (ELISA), and gene sequencing. During biopanning, KM13 phage clones were exposed to S. pyogenes antigens, and bound phage clones were selected and isolated. By identification, isolation, and gene sequencing of the phages bound to ​S. pyogenes​, we can gain insight to the genetic mechanisms that allow phages to bind to S. pyogenes, and engineer them to inhibit ​S. pyogenes ​colonies in the future.

  However, due to COVID-19, we were unable to acquire S. pyogenes cultures and blood agar in a timely manner. Therefore, for this year’s project, we decided to use our limited time in the lab to validate the process of biopanning, ELISA, and gene sequencing by binding phage M13 to Staphylococcus aureus, a bacteria already accessible in our lab, instead of Streptococcus pyogenes. Since both S. pyogenes and S. aureus are gram-positive bacteria, we expect for the results of biopanning on S. aureus to be informative for the future tests on S. pyogenes. In future projects, we hope to complete our goal of sequencing the mutated Phage M13 that binds to S. pyogenes best.

  KM13 phage was determined to be a suitable phage candidate because it is lysogenic, in which the viral genetic material is integrated into the bacterium’s genome, and a recombinant protein is produced on the surface of the phage.

## **METHODOLOGY OVERVIEW**

  The steps of the wet lab process are as follows: display, panning, enrichment, amplification, and ELISA (enzyme-linked immunosorbent assay).

(1) Display: Filamentous phage displaying variants of protein antibodies were displayed on the surface of the KM13 bacteriophages.

(2) Panning: The variant KM13 phage library is then exposed and incubated to immobilized S. aureus antigen. The excess of KM13 phages in 1000-fold allows for stability of the binding and minimal non-specific binding between KM13 and S. aureus.

(3) Enrichment: Non-binding phages were washed off, and bound phages were eluted by incubation with trypsin protease. This caused disruptions in the antigen-antibody interactions, leading to enrichment of the specific bound phages.

(4) Amplification: The eluted phages were then amplified overnight via infection of E. coli, resulting in an enriched phage population produced in a 96-well microtiter plate that displayed antibodies that bound to the S. aureus target.

(5) ELISA: To test for affinity of the phage clones after biopanning, ELISA, also known as enzyme-linked immunosorbent assay, was used to determine whether antigen binding had occurred. The dilutions of the selected phages were added to both the ELISA plates and a control protein. After incubation for 2 hours at room temperature, it was washed six times with TBST. The final activity was detected by the absorbances with an ELISA microplate reader. The absorbances were used to determine the affinity found among the selected M13 phages.

![](https://static.igem.org/mediawiki/2020/f/ff/T--ICS_BKK--experimentspic2.png)
![](https://static.igem.org/mediawiki/2020/2/2f/T--ICS_BKK--experimentspic3.png)

  Dr. Kitsricharoenchai and Dr. Hunsuwan both asserted that forming regular brushing habits is a more sustainable and healthy solution than engineering a product that patients need to depend on, even if the product is very effective.

  Therefore, we determined that our project would not be useful if dental cavities can already be simply prevented by tooth brushing. However, we already designed our project and determined our protocol. So, we shifted our interest to targeting *S. pyogenes* to treat strep throat and tonsillitis instead.

## **WET LAB PROTOCOL**

Catalase test

The catalase test is a biochemical test that detects if the organism produces the catalase enzyme. We tested it on S. aureus which is a catalase-positive organism, meaning that when we dropped in hydrogen peroxide, catalase would hydrolyze the hydrogen peroxide into water and gaseous oxygen, resulting in a reaction of gas bubbles. This test ensures if the organism that we are working with is purely that organism and not contaminated with any other species.

1. Bacterial culture preparation

  a. Finding the growth curve:

  - Sampling S.Aureus broth

    - We used a liquid solution of S.Aureus and broth (food for S.Aureus). We put that solution into a shaking incubator and took it out every 30 minutes to measure its OD600 value in a spectrophotometer. After 10 rounds (300 minutes) , we took the data for time and OD600, then plotted it on a scatter plot. Time is on the x-axis and OD600 value on the y-axis.

  - Scatterplot analysis:

    - Analyzing the growth curve, we looked for the biggest increase in the OD600 value, aka the mid log phase, which is when S.aureus is most productive. We determined the mid log phase by checking the greatest difference between two intervals.

  b. Growing S. aureus

  - **Culture S. aureus** (goal : OD600 = 0.4)

  - Put 30 microliters of S. aureus into new broth then put into shaking incubator and wait for one hour

  - Put broth into a cuvette and put into the spectrophotometer, then zero it to set a standard. Set wavelength to 600nm because OD600

  - Wash the cuvette then put solution into the cuvette. Got around 0.23, so put the solution back into the shaking incubator for 30 minutes. When measured again, we got 0.8.. so we diluted the solution, but the most we got was 0.6, using 350 microliters broth and 500 microL of S. aureus.

  - Dry the plates that came out from the fridge because there is water vapor that might contaminate the bacteria by putting them into the heating box for around an hour

  c. Pick single colony for bacterial growth curve plotting and colony forming unit assay

  - **Spread plate**

  - Use serial dilution to find the colony forming unit (CFU). Dilute by 10 times every time. Start by putting 900microL of LB into 5 out of 6 tubes. Put all of the solution into the first tube. Label as undiluted. Take out 100 microL of the undiluted solution into the next small tube, containing 900 microL of LB, and mix it using the vortex. Label as 10^-1. Take out 100 microL of the 10^-1 solution, put it into the next tube and mix it again. Label as 10^-2. Continue for the rest (Undiluted, 10^-1, 10^-2, 10^-3,10^ -4, 10^-5)

    - The reason for 100 and 900 is because the total volume is 1000 microL and we want the ratio of S. aureus and whole volume to be 1:10.

  - Put 100 microliters of each solution into separate plates, 6 in total. Use a metal rod to spread the solution. If not the colonies are going to be clumped together and cannot be used for biopanning. Be sure to dip the rod into alcohol and heat it up two times after using.

  - Put finished plates into incubator overnight

2. Biopanning

  a. **Coating plate**

  b. Put 50 microliters of S. aureus and BSA into two separate wells in the 96-well plate. Leave it for a day.

  c. Identify S. aureus or check for contamination through using hydrogen peroxide.

  - Hydrogen peroxide will catalyze gram positive bacteria (S. aureus) and form H2.

  d. Prepare buffers for Bio Panning

  - PBST + 0.1% Tween

  - PBST + 0.05% Tween

  - 5% BSA in PBS

  - Trypsin in TBSC buffers

  e. Bio Panning

  - 1) Wash the well with BSA and S. aureus with 200 microliter of PBST+0.05% Tween.

  - 2) Add 200 microliter of 5% BSA in PBS in the two wells.

  - 3) Incubate at 37 degrees celsius for 1 hour.

  - 4) Wash BSA well three times by using 200 microliter of 0.05% PBST

  - 5) Add the phage library

  - 6) incubate at 37 degrees celsius for 1 hour

  - 7) take the phage that didn’t bind with S. aureus well out

  - 8) Wash the S. aureus well 10 times with 200 microliter of 0.1% PBST

  - 9) Add 1xtrypsin dissolved in TBSC buffer

  - 10) Incubate at 37 degrees celsius for 1 hour

  - 11) take 1xtrypsin dissolved in TBSC buffer out

  - 12) Add in 200 microliter of buffer elution buffer

  - 13) Incubate at room temperature for 15 minutes

  - 14)Add in 100 microliter of elution buffers : 0.2 molar glycine of 2.2 pH and then take it out

  - 15) Add 15 microliter of neutralization buffer, 1 molar His - hcl (ph 9.14) to the solution that was taken out in step 14

  - 16) Take elute phages to infect phage TG.1

Round 2:
Same as above except changing from 10 μl of solution with 200 μl of TG, tris-glycine buffer, to 100 μl of solution with 200 μl of TG for the undiluted
In case there are some bacteria left in the well, we also put 200 μl of TG - not sure) into it and incubate at 37C for 1 hour. Then do the same, using serial dilution to make 10^-1, 10^-2 solutions.

3. Colony counting

  a. For the 10^-2 on plate, it was 1.99 x 10^5 cfu/ml. For the 10^-2 elute, 5 x 10^2 cfu/ml. We picked 15 colonies from each plate and swirled it into each well in a 96-well plate as well as streaking it onto a plate of agar, also called masterplating.

  b. ![](https://static.igem.org/mediawiki/2020/c/cc/T--ICS_BKK--wetlabpic4.jpg)

4. Superinfection with helper phage KM13

  a. Added 60 microliter of each well into each tube of LB agar with 100 microgram/ml ampicillin. Put tubes in the incubator shaker at 37C and 180 rpm and measure the OD until it is at 2 McFarland. After that, we put in the KM13 helper phage at a concentration of 10^8 pfu/ml. Incubate at 37C for one hour with no shaking. Add 50 microgram/ml of kanamycin since the helper phage has a kanamycin resistant gene. Add IPTG as an inducer that overexpresses the gene function that targets S. aureus. Let the tubes incubate at 30C overnight with shaking.

5. Phage precipitation

  a. We took the 30 tubes and centrifuged them at 8000 rpm for 5 minutes. After the centrifuge, we collected 1 ml of the supernatant. Then we added PEG6000 and 250 l of 2.5M NaCl into each of the tubes, and incubated them on ice for 1 hour. After the incubation, we centrifuged it again for 30 minutes at 3200g. After the centrifuge, we collected the pellet, and resuspended the pellet by 1 ml of PBS buffer. Repeating a step, we added PEG6000 and 250 l of 2.5M NaCl then incubated the tube with ice for 30 minutes. Following the ice incubation, we collected the pellet once again, resuspend by 1ml of PBS buffer and centrifuged it at 3200g for 10 mins. Lastly, we measured dsDNA by using a nanodrop spectrophotometer.

ELISA
After Biopanning (Prepare for ELISA) and biopanning

  1. Coat plate: coat antigen (S. aureus)

   - Culture S. aureus to achieve OD value of 0.65, which is mid-log phase

   - Calculate concentration of bacteria in \_\_\_ CFU/mL

   - Add 50 microliters of culture S. aureus to 96-well plate

   - Incubate to evaporate water for only bacteria

   - Wash 3 times by PBST to get only the bacteria that was immobilized

   - Blocking with 5% BSA 100 microliters because BSA is a protein which will block non-specific surface, surfaces without antigen and will prevent non-specific binding

Put 50 microliters of culture S. aureus into each well in a 96-well plate. Next incubate the culture to remove all the water from the bacteria. After incubation, wash each well with PBST to only get the bacteria that was immobilized remaining. Wash each well 3 times, taking out the PBST after every wash. For the final wash, wait 5 minutes before taking the PBST out. Next, put 100 milliliters of 5% BSA, a protein that will prevent non-specific binding, into each well. Then, wait 40 minutes before repeating the step of washing each well 3 times with PBST.

  2. Add phage

   - Add individual phage clone into each plate (10^9 pfu/mL) incubate at 4 degrees C overnight

Put 10^9 pfu/mL of phage clones from the tubes into each well, with each tube’s phages being put into 4 of the wells. Incubate the plate overnight at 4 celsius.

   - 3 populations:

     1. Bound phage S. aureus

     2. Contamination from the lab that does not display the binder molecules (unbound phage)

     3. Display nanobodies but cannot bind with the bacteria

  3. Wash

   - Remove weak bound phage to S. aureus with PBST (PBS and 0.1% Tween; tween will remove the weak hydrophobic binding between phage and bacteria) by washing thrice

  4. Anti m13

   - Add anti-M13 antibody (1st antibody), which will bind with pVIII (coat protein) of M13 phage

     - Will bind to all M13 phages. Therefore you need to wash well in order to avoid false positives

   - Add Goat-anti mouse HRP conjugate (horseradish peroxidase, enzyme, 2nd antibody) which will bind to the 1st antibody

   - Incubate for 1hr at 4 degrees C to have them bind

   - Wash 3 times by PBST to remove unbound 2nd antibody

   - The amount of HRP will tell you about the amount of high affinity

  5. Add TMB substrate 50 microliters

   - When the TMB substrate has been changed via HRP conjugate (oxidation reaction), it will change color to blue

  6. Stop reaction

   - If the OD is more than 2.0, it will not be reliable

   - Have to stop reaction

   - Add hydrochloric acid to increase hydrogen ions, stop reaction, solution becomes yellow

## **REFERENCES**

[1] Morris, G. M., Goodsell, D. S., Halliday, R.S., Huey, R., Hart, W. E., Belew, R. K. and Olson, A. J. (1998), Automated Docking Using a Lamarckian Genetic Algorithm and Empirical Binding Free Energy Function J. Computational Chemistry, 19: 1639-1662.

[2] BIOVIA, D. S. (2015). Discovery studio modeling environment. San Diego, Dassault Systemes, Release, 4.

[3] A. Šali and T. L. Blundell. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779-815, 1993.

More information about the methods implemented in MODELLER, their use, applications, and limitations can be found in the papers listed on our web site at https://salilab.org/publications/. Here is a subset of these publications:

  1. A. Šali and T. L. Blundell. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779-815, 1993.

  2..A. Šali and J. P. Overington. Derivation of rules for comparative protein modeling from a database of protein structure alignments. Protein Science 3, 1582-1596, 1994.

  3. R. Sánchez and A. Šali. Comparative protein structure modeling: Introduction and practical examples with MODELLER. In Protein Structure Prediction: Methods and Protocols, D.M. Webster, editor, 97-129. Humana Press, 2000.

  4. M. A. Martı-Renom, A. Stuart, A. Fiser, R. Sánchez, F. Melo and A. Šali. Comparative protein structure modeling of genes and genomes. Ann. Rev. Biophys. Biomolec. Struct. 29, 291-325, 2000.

  5. A. Fiser, R. K. G. Do and A. Šali. Modeling of loops in protein structures. Protein Science 9, 1753-1773, 2000.

  6. F. Melo, R. Sánchez, A. Šali. Statistical potentials for fold assessment. Protein Science 11, 430-448, 2002.

  7. M. A. Martı-Renom, B. Yerkovich, and A. Šali. Comparative protein structure prediction. John Wiley & Sons, Inc. Current Protocols in Protein Science 1, 2.9.1 - 2.9.22, 2002.

  8. U. Pieper, N. Eswar, A. C. Stuart, V. A. Ilyin and A. Šali. MODBASE, a database of annotated comparative protein structure models. Nucleic Acids Research 30, 255-259, 2002.

  9. A. Fiser and A. Šali. MODELLER: generation and refinement of homology-based protein structure models. In Methods in Enzymology, C.W. Carter and R.M. Sweet, eds. Academic Press, San Diego, 374, 463-493, 2003.

  10. N. Eswar, B. John, N. Mirkovic, A. Fiser, V. A. Ilyin, U. Pieper, A. C. Stuart, M. A. Martı-Renom, M. S. Madhusudhan, B. Yerkovich and A. Šali. Tools for comparative protein structure modeling and analysis. Nucleic Acids Research 31, 3375-3380, 2003.

[4] Fukunaga, K., & Taki, M. (2012). Practical Tips for Construction of Custom Peptide Libraries and Affinity Selection by Using Commercially Available Phage Display Cloning Systems. Journal of Nucleic Acids, 2012(295719), 1-9. https://doi.org/10.1155/2012/295719

[5] Lee, C., Iorno, N., Sierro, F. et al. (2017). Selection of human antibody fragments by phage display. Nat Protoc 2, 3001–3008 https://doi.org/10.1038/nprot.2007.448

[6] Nian, S., Wu, T., Ye, Y. et al. (2016). Development and identification of fully human scFv-Fcs against Staphylococcus aureus. BMC Immunol 17, 8. https://doi.org/10.1186/s12865-016-0146-z

[7] Gagic, D., Ciric, M., Wen, W. X. et al. (2016). Exploring the Secretomes of Microbes and Microbial Communities Using Filamentous Phage Display. Frontiers in Microbiology. 7. https://doi.org/10.3389/fmicb.2016.00429

[8] Harada, L. K., Silva, E. C., Campos, W. F., et al. (2018). Biotechnological applications of bacteriophages: State of the art. Microbiological Research, 212-213(7), 38-58. https://doi.org/10.1016/j.micres.2018.04.007.

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International Community School of Bangkok

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# Team:ICS BKK/Results

Team:ICS BKK/Results - 2020.igem.org

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[**Identification of Properties of Bound P2 Phage to *S. Pyogenes***](#Computational-Work)
[***S. Pyogenes* Growth Curve**](#Wet-Lab)
[**Bound M13 Phage to *S. Pyogenes***](#Wet-Lab1)
[**Next Steps**](#Next-Steps)
[**References**](#References)

# **PROJECT RESULTS**

## **COMPUTATIONAL WORK**

### IDENTIFICATION OF PROPERTIES OF BOUND P2 PHAGE TO *S. PYOGENES*

  We utilized three different softwares: The Scripps Research Insitute’s Autodock Vina [8], BIOVIA Discovery Studio [2], and MODELLER [1]. Autodock Vina is the main tool we used to predict the binding affinity between the phage and the bacteria. Therefore, the first step was to validate the software as well as having the binding affinity for the control group.

  The Scripps Research Insitute’s Autodock Vina was validated as we docked Phage P2 with the receptor of its natural host, *Lactococcus lactis*. This also serves as our control group. However, the 3D model of the macromolecule on *L. lactis* in which Phage P2 binds onto wasn’t available. Therefore, utilizing previous research and 2D structures of the macromolecule from *A dual-chain assembly pathway generates the high structural diversity of cell-wall polysaccharides in Lactococcus lactis* [12], we were able to model the receptor by utilizing BIOVIA Discovery Studio to predict possible bond angles. Hence, we were able to computationally model a three-dimensional structure of the *L. lactis* receptor.

![](https://static.igem.org/mediawiki/2020/d/d3/T--ICS_BKK--resultspic1.png)

  Using Autodock Vina, we then docked this modelled macromolecule with the Phage P2’s PDB structure [11], which can be found in RCSB Protein Data Bank. The binding affinity given by Autodock Vina was -11.38 kcal/mol. From our experience, Autodock Vina’s binding affinity score varies +/- 1.0 kcal/mol.

![](https://static.igem.org/mediawiki/2020/d/db/T--ICS_BKK--resultspic2.png)

  Thereafter, we used Autodock Vina to dock the PDB format of Phage P2 with the receptor of *S. pyogenes*, which is peptidoglycan [7], in which its three-dimensional structure can be found on PubChem [9]. We found the binding affinity to be -4.50 kcal/mol, indicating that Phage P2 and *S. pyogenes* are unlikely to bind with one another as expected.This value of -4.50 kcal/mol will serve as our baseline.

![](https://static.igem.org/mediawiki/2020/2/2a/T--ICS_BKK--resultspic3.png)

  In an attempt to decrease the binding affinity value between Phage P2 and *S. pyogenes*, we used MODELLER to model 450 different versions of Phage P2. MODELLER is used to predict a protein’s three-dimensional structures when the program is provided with an alignment of known sequences. The program then calculates many models that satisfies its restraints. MODELLER created 450 different probable models of the mutated Phage P2. Thereafter, we docked all 450 versions with the receptor of *S. pyogenes* via Autodock Vina. The average binding affinity was -4.90 kcal/mol, the maximum was -3.51 kcal/mol, and the minimum was -6.60 kcal/mol; the minimum occurred at Phage P2 Mutant #327, indicating that though it may not bind to *S. pyogenes* as well as it would to *L. lactis*, it has significantly improved its binding affinity from the natural form of Phage P2.

![](https://static.igem.org/mediawiki/2020/b/b2/T--ICS_BKK--resultspic4.png)

  Moreover, to create another control group, we decided to dock Phage A25, a bacteriophage that naturally binds to *S. pyogenes*, to *S. pyogenes*. The binding affinity calculated by Autodock Vina was -7.54 kcal/mol. This indicates that Phage P2 Mutant #327 may not bind to *S. pyogenes* as well as it would to *L. lactis*, nor does it bind to *S. pyogenes* as well as Phage A25. However, the value of -7.54 kcal/mol is close to -6.6 kcal/mol to suggest that it is probable that the Phage Mutant #327 will bind to *S. pyogenes*. Phage Mutant #327 also has significantly improved its binding affinity from the natural form of Phage P2, since the original Phage P2 with *S. pyogenes* resulted in -4.5kcal/mol as its binding affinity.

![](https://static.igem.org/mediawiki/2020/5/50/T--ICS_BKK--resultspic5.png)

  Now remember, we wanted for this Phage P2 Mutant to be able to infect both *S. pyogenes* and *L. lactis*. Therefore, we tested this by docking the two together and found a binding affinity of -10.01 kcal/mol, which can be compared to the original Phage P2 binding with *L. lactis*’s binding affinity, which is -11.38 kcal/mol. This is as expected that Phage P2 Mutant will bind slightly worse than the original Phage P2. However, the close to similar value of binding affinity still means that Phage P2 Mutant #327 is still most likely to be able to still bind to *L. lactis*. Hence, Phage P2 Mutant #327’s unique properties of most likely being able to bind to both *S. pyogenes* and *L. lactis* means its sequence is ready to be compared with the phage from the wet lab, so we may study what makes a bacteriophage able to bind to *S. pyogenes*.

![](https://static.igem.org/mediawiki/2020/0/03/T--ICS_BKK--resultspic6.png)

## **WET LAB**

### *S. PYOGENES* GROWTH CURVE

![](https://static.igem.org/mediawiki/2020/2/2e/T--ICS_BKK--resultspic8.png)

  The scatterplot above represents the growth curve of *S. aureus*. We placed a solution of *S. aureus* into a shaking incubator, and every 30 minutes we sampled it and used a spectrophotometer to check its OD600 value. The OD600 value represents the concentration of *S. aureus* in our liquid sample. The difference in OD600 value between two points (two time intervals) represents the growth of *S. aureus*. We calculated the greatest difference between two points, which amounted to 0.5985, from 0.4789 to 1.0774. This point of the greatest growth of *S. aureus* is called the mid log phase, where the bacteria is the most productive.

## **WET LAB**

### BOUND M13 PHAGE TO *S. PYOGENES*

  Using ultraviolet–visible spectroscopy, the optical densities of each selected phage clone were determined and graphed. The clones were named after the wells they were assigned to. The optical density indicates the concentration of M13 phages that successfully bound to *S. aureus*.

![](https://static.igem.org/mediawiki/2020/2/2e/T--ICS_BKK--resultspic7.png)

  The results of ELISA show us that there was one phage clone, C3, that has a high OD value, meaning that this phage has a high binding affinity with S. aureus. However, we see that the error bars are high as well. This does not give us confirmation that this phage had truly bound to *S. pyogenes*, but it is a promising phage candidate. Currently, the plasmid is being sequenced. To better our results in the future, we can propagate the C3 phage and repeat ELISA multiple times.

  In conclusion, our team is able to select for a specific nanobody that binds to a bacteria. Besides engineering the nanobody to decrease *S. pyogenes* populations, phage therapy can also be used to target other infectious bacteria, because identifying and isolating the nanobody requires a short period of time.

  This method of phage therapy can also be used to combat drug resistance to antibiotics. Since antibodies are much more specific that antibiotics, phage therapy can be used to fight infectious bacteria before it develops antibiotic resistance.

## **NEXT STEPS**

  The next steps that we hope to continue for next year’s project, and we encourage others to also take on this challenge, is to use *S. pyogenes* for the wet lab instead of *S. aureus*, as we did due to the unforeseen circumstances of COVID-19. This will then allow us to compare the structures or properties of the bacteriophages that allows it to bind to *S. pyogenes*. After this analysis, we hope to manually engineer the computational model of Phage P2 to better its binding affinity even further.

  After testing this phage’s binding abilities computationally, we can then utilize lab techniques to engineer the phage in the wet lab, such as using the CRISPR-Cas system. After that, we would incorporate the bacteriophage into *Lactococcus lactis*, and finally incorporate this engineering probiotic into mouthwash.

## **REFERENCES**

[1] A. Šali and T. L. Blundell. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779-815, 1993.

[2] BIOVIA, D. S. (2015). Discovery studio modeling environment. San Diego, Dassault Systemes, Release, 4.

[3] Fukunaga, K., & Taki, M. (2012). Practical Tips for Construction of Custom Peptide Libraries and Affinity Selection by Using Commercially Available Phage Display Cloning Systems. Journal of Nucleic Acids, 2012(295719), 1-9. https://doi.org/10.1155/2012/295719

[4] Gagic, D., Ciric, M., Wen, W. X. et al. (2016). Exploring the Secretomes of Microbes and Microbial Communities Using Filamentous Phage Display. Frontiers in Microbiology. 7. https://doi.org/10.3389/fmicb.2016.00429

[5] Harada, L. K., Silva, E. C., Campos, W. F., et al. (2018). Biotechnological applications of bacteriophages: State of the art. Microbiological Research, 212-213(7), 38-58. https://doi.org/10.1016/j.micres.2018.04.007.

[6] Lee, C., Iorno, N., Sierro, F. et al. (2017). Selection of human antibody fragments by phage display. Nat Protoc 2, 3001–3008 https://doi.org/10.1038/nprot.2007.448

[7] McShan WM, Nguyen SV. The Bacteriophages of Streptococcus pyogenes. 2016 Feb 10 [Updated 2016 Mar 25]. In: Ferretti JJ, Stevens DL, Fischetti VA, editors. Streptococcus pyogenes : Basic Biology to Clinical Manifestations [Internet]. Oklahoma City (OK): University of Oklahoma Health Sciences Center; 2016-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK333409/

[8] Morris, G. M., Goodsell, D. S., Halliday, R.S., Huey, R., Hart, W. E., Belew, R. K. and Olson, A. J. (1998), Automated Docking Using a Lamarckian Genetic Algorithm and Empirical Binding Free Energy Function J. Computational Chemistry, 19: 1639-1662.

[9] National Center for Biotechnology Information. "PubChem Compound Summary for CID 9816401, alpha-Muramic acid" PubChem, https://pubchem.ncbi.nlm.nih.gov/compound/alpha-Muramic-acid. Accessed 11 October, 2020.

[10] Nian, S., Wu, T., Ye, Y. et al. (2016). Development and identification of fully human scFv-Fcs against Staphylococcus aureus. BMC Immunol 17, 8. https://doi.org/10.1186/s12865-016-0146-z

[11] PDB ID: 1ZRU
Receptor-binding protein of Lactococcus lactis phages: identification and characterization of the saccharide receptor-binding site.
Tremblay, D.M., Tegoni, M., Spinelli, S., Campanacci, V., Blangy, S., Huyghe, C., Desmyter, A., Labrie, S., Moineau, S., Cambillau, C.
(2006) J Bacteriol 188: 2400-2410

[12] Theodorou I, Courtin P, Palussiere S, Kulakauskas S, Bidnenko E, Pechoux C, Fenaille F, Penno C, Mahony J, van Sinderen D, Chapot-Chartier MP. 2019. A dual-chain assembly pathway generates the high structural diversity of cell-wall polysaccharides in Lactococcus lactis. J Biol Chem 294:17612–17625. doi:10.1074/jbc.RA119.009957.

More information about the methods implemented in MODELLER, their use, applications, and limitations can be found in the papers listed on our web site at https://salilab.org/publications/. Here is a subset of these publications:

  1. A. Šali and T. L. Blundell. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779-815, 1993.

  2..A. Šali and J. P. Overington. Derivation of rules for comparative protein modeling from a database of protein structure alignments. Protein Science 3, 1582-1596, 1994.

  3. R. Sánchez and A. Šali. Comparative protein structure modeling: Introduction and practical examples with MODELLER. In Protein Structure Prediction: Methods and Protocols, D.M. Webster, editor, 97-129. Humana Press, 2000.

  4. M. A. Martı-Renom, A. Stuart, A. Fiser, R. Sánchez, F. Melo and A. Šali. Comparative protein structure modeling of genes and genomes. Ann. Rev. Biophys. Biomolec. Struct. 29, 291-325, 2000.

  5. A. Fiser, R. K. G. Do and A. Šali. Modeling of loops in protein structures. Protein Science 9, 1753-1773, 2000.

  6. F. Melo, R. Sánchez, A. Šali. Statistical potentials for fold assessment. Protein Science 11, 430-448, 2002.

  7. M. A. Martı-Renom, B. Yerkovich, and A. Šali. Comparative protein structure prediction. John Wiley & Sons, Inc. Current Protocols in Protein Science 1, 2.9.1 - 2.9.22, 2002.

  8. U. Pieper, N. Eswar, A. C. Stuart, V. A. Ilyin and A. Šali. MODBASE, a database of annotated comparative protein structure models. Nucleic Acids Research 30, 255-259, 2002.

  9. A. Fiser and A. Šali. MODELLER: generation and refinement of homology-based protein structure models. In Methods in Enzymology, C.W. Carter and R.M. Sweet, eds. Academic Press, San Diego, 374, 463-493, 2003.

  10. N. Eswar, B. John, N. Mirkovic, A. Fiser, V. A. Ilyin, U. Pieper, A. C. Stuart, M. A. Martı-Renom, M. S. Madhusudhan, B. Yerkovich and A. Šali. Tools for comparative protein structure modeling and analysis. Nucleic Acids Research 31, 3375-3380, 2003.

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International Community School of Bangkok

Contact:
[igem.icsbkk@gmail.com](igem.icsbkk%40gmail.com)