iGEM 2021 — Team TJUSLS_China

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# Team:TJUSLS China/Notebook

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# Team:TJUSLS China/Engineering

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**DESCRIPTION**

# Overview

**Good engineering** is an essential part of every research group in synthetic biology.
However, we can not design and predict living organisms like architecture designs exact
buildings, or aerospace engineers predict stars. So, other than top-down and bottom-up, biology
scientists usually use “middle-out” approach to do the engineering. We, here, also introduce
**a testing-learning cycle** to help us to check the result.  Here shows the diagram of our
engineering cycle.

![](https://static.igem.org/mediawiki/2021/f/f1/T--TJUSLS_China--description-6567.jpg)

**The main purpose** of our project is to create a stable enzyme that can decompose the high
crystalline PET.
We first **investigate** the PET enzyme. And then do **research and interviews** to get
more information about it, to check out what is known and what is unknown. We found that the
wild type PETase can degrade PET **5.5 times** more efficiently than LCC but the **thermal
stability is quite bad**. It is not able to survived under 40°C for 24h. Accordingly, we
aimed to increase its thermostability.
We made **assumptions** and **built up models** according to the information we got during
investigating. The knowledge we learnt from classes and books also helped a lot. We
**tested** those models and improved them by doing further research and **learning from the
result** we got. Keep doing the testing-learning cycles can help us to reach the final
success in engineering.

# Doing Investigation

At the very beginning, we **read some literature** to decide what enzyme should be chosen to
use in our project. Then we found **PETase**, an enzyme that can degrade the high crystalline
PET. But PETase is **not quite stable, nor effective**. We found that the structure of it has
been resolved and published. There were some research groups tried to improve the stability
already, by different re-designing strategy. But none of them gets a satisfactory result.
We read the papers published by those research groups who wanted to **improve the stability of
PETase** and learnt from them. Also, we did more reading and **consulted some experts in
bio-engineering** to study how to change the features of proteins and enzymes.

# Making assumptions

We applied the methods and principles, provided by the literature and the experts, such as adding
hydrogen bonds, salt bridges, enhancing hydrophobic interactions, proline mutations and adding
disulfide bonds, when making assumptions.

# 1st iteration

## Modelling

Modelling is important in synthetic biology. A successful model can guarantee the success in
implementation, the summary and feedback of the project. Here we used FoldX, SWISS-MODEL and
other software to **build up those models of the mutants we designed**. It really helps us
with the test of the stability of these mutants.
Our group considered combining the sites with the best single point effect and **combining the
structure for analysis**.

## Testing

1. **ddG-Testing**

FoldX, MD, Docking, PyMOL, Fireprot, CUPSAT, I-Mutant, etc. were used to **predict the
thermal stability** of mutants. **ddG**, which is an important parameter to
evaluate protein thermal stability was determined as well. Some of these methods gave the
value of ddG of the protein directly, and others gave the instability index.  We also turned
ddG to dTm which characterized the thermal stability of the protein.

2. **Structure verification**

After making thermostability predictions, we used AlphaFold to **predict the
structures**. We found that the structure of some single point mutations met our
expectations, but many other mimicking models were not good enough. We found that the
**modeling structure** of these 20 mutants is **different from what we expected**.

3. **Lab-Testing**

We **collected 20 mutants**, which act **the best in this computer simulation**, to go
to the lab and tried to test their exact performance. We used affinity chromatography and
gel filtration purification methods to separate and purify these mutant proteins, and use
SDS-PAGE to verify the purity of the purified protein. All of these exist as **inclusion
bodies**.

**Problem we met:**

* We found that the modeling structure of these 20 mutants is different from what we
  expected.
* All of these exist as inclusion bodies. So, we failed to test the exact performance and
  get more information about them.

## Learning & Improving

The function of different mutations may influence one another to give a better result. That’s
what we wanted. But unfortunately we found that the **increase of mutation sites** may can
**silent the single point mutations**. Things may act differently as the general theories
suggested, nor the result of simulation experiments, especially in biology. So, we need more
practice and find a better way to design the protein. We collected the differences between the
real proteins and the simulated mutants and we got help from the group of University Toronto. We
finally **found the rule in the combination of different kinds of mutations**. We also summed
up **the influence of the distance** between different mutation sites. Accordingly, We
carried out **a redesigned combination** of the single mutations

# 2nd iteration

## Researching and Modelling

* After investigating the ways that can improve the expression of proteins, we decide to use
  different vectors that carry **hydrophilic tags**, which can **auxiliary
  dissolvingauxiliary dissolving**.
* We carried out **a redesigned combination** of the single mutations according to the
  **rules we summarized from the first interation**. We still used FoldX, Fireprot, CUPSAT,
  I-Mutant as simulating software to build up those models of the mutants we designed.

## Testing

We still used the same way to predict the thermal stability of mutants and the same way to test the
performance of these proteins. But we added a model of the relationship between UV260 and HPLC to
**simplify the test** of performance **according to some literature**. We finally got some
great **ones with much higher thermostability**.

**Results we got:**

* We got the mutant number 20 (namely super4) as a great result, the optimal temperature
  of which is 50°C.

## Learning & Improving

After we made this mutant with a degree of 50 degrees, we **calculated all the 11 positions of
it**, and then found that one of the points, **A80V, had not very good data**, so we
decided to **replace this point** with another mutation. To see if it can rise to a higher
temperature, we have replaced six other combinations

# 3rd iteration

## Modelling

After finding the problematic sites through calculations, we simulated **six possible
combinations** which have **similar mutation sites** with mutant number 20 **except
A80V**.

## Test & Result

We still used the same method for testing and obtained very satisfactory results. The maximum
optimum temperature of our mutant has reached **60 degrees** and has a **163-Times higher
degradation**.

**Results we got:**

* We got the mutant number 24 (namely super5) as a great result, the optimal temperature
  of which is 60°C.

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# Team:TJUSLS China/Experiments

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# Team:TJUSLS China/Results

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