iGEM 2022 — Team sjtu-software


Engineering| SJTU-software - iGEM 2022

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![](https://static.igem.wiki/teams/4328/wiki/pictures/eng-new.png)

# Sidebars examples

* Research

  + [Medical Needs](#part1-1)
  + [Experimental Needs](#part1-2)
* Design

  + [Pre-experiment Data Gathering](#part2-1)
  + [Key enzymes positioning](#part2-2)
  + [Modification sites positioning](#part2-3)
  + [Back End](#part2-4)
  + [Front End](#part2-5)
* Build

  + [From gem to ecgem](#part3-1)
  + [From ecGEM to DL-ecGEM](#part3-2)
  + [Locating Key Enzymes](#part3-3)
  + [Giving protein modification solutions](#part3-4)
  + [Closed-loop Structure](#part3-5)
* Test

  + [Dry-Lab validation](#part4-1)
  + [Wet-Lab validation](#part4-2)
  + [User's Feedbacks](#part4-3)
* Learn

  + [Data](#part5-1)
  + [Deep Learning Model](#part5-2)
  + [User Interactivity](#part5-3)
  + [Further Improvement](#part5-4)

To guarantee the successfulness of our
project, we follow strictly with the standard
engineering cycle: **Design → Build → Test → Learn → Design...**

We performed at least **three iterations of engineering loops**, which are summarized in the TEST and LEARN section.

![](https://static.igem.wiki/teams/4328/wiki/pictures/engineeing.jpeg)

# Research

We began the project by extensively searching
the literature, and we also consulted doctors
and professors in related fields.

### Medical needs

At the same
time, we posted questionnaire to investigate the
researchers about problems that need to be solved in
the Enzyme Engineering and Genetic Engineering Lab.

As an effective drug for the treatment of type
Ⅱ diabetes, the huge demand of acarbose is increasing
year by year. Therefore, we need to find a cell
transformation scheme to expand the production of
acarbose by actinomycetes.

### Experimental needs

At the same
time, we posted questionnaire to investigate the
researchers about problems that need to be solved in
the Enzyme Engineering and Genetic Engineering Lab.

![](https://static.igem.wiki/teams/4328/wiki/pictures/wet-lab.jpg)

* In the wet-lab, over-expression or knockout of gene
  targets is usually used to improve the production of
  acarbose at the system level. Therefore, researchers
  need to have a systematic understanding of the
  metabolic process of acarbose.
* In the enzyme modification project, the positioning
  of key enzymes and the screening of all possible
  enzyme mutation
  sites require a lot of experimental costs and a
  long experimental period. They have reached the
  limit at present.

# Design

When **building our model**, we consider
the following requirements for **interactive platform design**.

### Pre-experiment Data Gathering

Metabolism
is the superposition of many chemical reactions.
There are multiple pathways in Actinoplanes sp.
SE50/110 to produce and consume acarbose.
For wet-lab researchers, it takes up a great
deal of time to obtain a comprehensive understanding
of the metabolic pathways.

### Key enzymes positioning

The metabolism of acarbose
involves dozens
of chemical reactions and a large number of enzymes,
and any reaction flux (or metabolic rate) has a basic
constraint: the flux cannot exceed the maximum rate of the
reaction (vmax), and vmax is equal to the intracellular
concentration of the corresponding enzyme multiplied by
the enzyme's conversion coefficient (Kcat value).
Therefore, it is important to find key enzymes that have
a greater role in the reaction.

### Modification sites positioning

The chemical nature of most enzymes
are proteins, therefore they have complex high-level
structures. Also chemical substance molecules
have a spatial structure, and enzymes and
substrates react in three-dimensional coordinates.
The effect of active site alteration on the
catalytic function of the enzyme may be
significant. However, this requires a large
number of pilot experiments, each of which requires
a long experimental period, and the results are easily
affected by the experimental procedure.

When designing our **software platform**, we consider
the following tools as **technical support**.

### Back end

We used pytorch as the framework of deep
learning, with the help of numpy, rdkit to
perform auxiliary operations. We also use MySQL and MariaDB as database analysis tools.

![](https://static.igem.wiki/teams/4328/wiki/pictures/backend.png)

### Front-End

We use HTML, CSS and Javascript language, with the
help of Bootstrap framework to complete the structure
of the web page, page rendering and other work.

![](https://static.igem.wiki/teams/4328/wiki/pictures/front-end.jpg)

# Build

In order to help biological researchers better
design experiment protocols,we built a universal model
framework. Here we provide two practical and powerful model
in our software to help researchers better locate key enzymes and provide the retrofitting recommendations.

Details of the modelling principles and process
can be found on the modelling page.
[click here!](https://2022.igem.wiki/sjtu-software/model)

### 1. From gem to ecgem

The genome-wide metabolic model iYLW1028 of
Actinoplanes sp. SE50/110 has been developed.
In order to quantitatively analyze acarbose
production while incorporating information
on the catalytic functions of different types
of enzymes, we introduce the GECKO [2] (GEM with
Enzymatic Constraints using Kinetic and Omics data)
method to add enzymes constrain in GEM, so we can
limit the spatial variability and improve the
prediction results.

In this way, we considere the expectation
constraints for each reaction flux and extend
the GEM of Actinomycetes to ecGEM.

### 2. From ecGEM to DL-ecGEM

When filling in the parameter Kcat values for the
ecGEM model of Actinomyces, a large number of
parameters are missing from the database.
To predict vitro kcat value, method of Deep
Learning is developed by combining a graph
neural network (GNN) for substrates and a
convolutional neural network (CNN) for proteins.
After obtaining the substrate molecular vector
representation and protein sequence vector
representation, we concatenate them with the
unique output Kcat value to train the deep
learning model. This allows us to use the
substrate structure, substrate name and
protein sequence as inputs to predict their
corresponding unique Kcat values.

Therefore, we optimized the ecGEM to DL-ecGEM
to make the model simulation more accurate.

### 3. Locating Key Enzymes

With a more complete metabolic model, we can observe
the metabolic fluxes of cells through simulations.
To assess the degree of influence of individual
enzymes in ecGEM on the target reaction,
the flux control coefficients (FCC) were
defined to correspond to the degree of limitation
of each enzyme in the system [6], which is expressed
as the ratio of the relative change in the target
flux to the 0.1% change in the corresponding Kcat
value of the enzyme and can be expressed as:

![](https://static.igem.wiki/teams/4328/wiki/pictures/fcc.png)

By calculating the FCC values of each enzyme in the simulation,
we can find the key enzyme classes in the acarbose
metabolic pathway to be targeted for modification.

### 4. Giving protein modification solutions

After finding the key enzyme, our model applies the toolkit
Hotspot Wizard 2.0, which enables users to target and
select mutational hotspots that affect protein stability
and catalytic activity. Users can select suitable
substitutions for individual amino acid sites based
on predictions of highly similar homologous protein
tolerance amino acids or amino acid distributions.

![](https://static.igem.wiki/teams/4328/wiki/pictures/pro3d-1.png)

For multiple modification targets given by a single enzyme,
we will re-predict the Kcat values for all possible mutant
proteins and give the modification site with the
greatest change in enzyme activity and the corresponding
Kcat value.

## Closed-loop Structure

In this way, our software becomes a self-validating and
self-optimising closed-loop structure:
We use a deep learning network to give missing
parameters in ecGEM, to build a reliable and
complete metabolic network model for Actinomycetes.
With it, we can carry out simulations with acarbose
production as the target function, to find this
target reaction limiting enzyme. We set this
key enzyme as a target for modification,
combine with a protein hotspot identification
tool to find the modification solutions.
And then, we can update our deep learning
network and metabolic network with the
modification scheme to see if the acarbose
production could be significantly improved,
thus verifying the feasibility of our recommended scheme.

In the process of optimising the model parameters,
we can use experimental data to bring the model
performance closer to reality.

![](https://static.igem.wiki/teams/4328/wiki/pictures/xunhuan.png)

# Test

## Dry-Lab Validation

We used the test set to measure the predictive ability of our model, the size of which is 17010 .

We perform at least **three updated versions** of our DL-ec
GEM model in our engineering circle. The results are presented below:

### Model version.1

![](https://static.igem.wiki/teams/4328/wiki/pictures/fig-a-1-00.png)

### Model version.2

![](https://static.igem.wiki/teams/4328/wiki/pictures/fig-c-1-00.png)

### Model version.3

![](https://static.igem.wiki/teams/4328/wiki/pictures/fig-5-00.png)

From the results, the predictive ability and robustness
of our model have been improved, which proves the
effectiveness of our model. For more details,
 [click here](https://2022.igem.wiki/sjtu-software/proof-of-concept)
to
Proof Of Concept.

## Wet-Lab Validation

Since the wet-lab experiment conditions are limited
due to the epidemic, we use our optimized DL-ecGEM
for flux analysis to simulate the wet-lab experiment
and obtain satisfactory results.

For more details,
 [click here](https://2022.igem.wiki/sjtu-software/proof-of-concept)
to
Proof Of Concept.

## User's Feedback

Once our modeling platform was initially built,
we contacted senior students in our cooperating
lab to get feedback on the trial.

The senior students gave feedback that for
researchers studying actinomycete, macroscopic
understanding of cellular metabolic growth becomes
more intuitive, daily use of basic functions
becomes easier and faster, and the ability to
give reference changes in modified proteins can
guide wet experiments.

But when they tried our software for retrofit
solution recommendation, only a single point mutation
can be checked at a time in the software for the effect
of kcat, but experiments often mutate amino acids at
multiple sites.and they wondered
if improvements could be made.

# Learn

**After the test of dry-lab、wet-lab and users,
we learn some beneficial information for the
following improvement of our software.**

## The improvement of data

When training our deep learning model, at first we
used a total of 401 piece of information
in actinomycete to predict the kcat value.
Due to the small number of training set,
as the epoch increases, the loss function
first increased and then decreased. The
overall prediction effect was not satisfactory.
After reading relevant materials and consulting
some experts ,the training set was expanded to a
total of 17,010 pieces of all species. The performance
was significantly improved.

## The improvement of deep learning model

![](https://static.igem.wiki/teams/4328/wiki/pictures/cnn-process.png)

### Protein Modeling

At first, we tried to train the model
by protein similarity and tried to use
sequence alignment to classify the learning data.
However, the time complexity was too high to draw
an evolutionary tree. Hence, the resulting alignment
score was low.
Then we used the protein length for group
training,but we did not get satisfactory results, either.

Finally, we chose CNN when modeling proteins.

### Substrate modeling

We have made some new attempts in molecular
fingerprinting. We use MACCS, but MACSS cannot
represent chemical substructures
that do not appear in the library.
We also try to use ECFP, but ECFP
cannot accurately represent the structural
information of substrate molecules.

Finally, we chose GNN when modeling substrates.

![](https://static.igem.wiki/teams/4328/wiki/pictures/gnn-process.png)

### Add species-related information

Since the number of species words is large, using one-hot vector coding will occupy a lot of memory, so we use word embedding method and introduce Embedding layer. We take out the words that characterize the species names in the dataset, add the start identifier "" and the end identifier "" before and after each species name, connect them into a continuous text, and use one word before and one word after each word to predict the idea of the word.

After training the data set, the constituent words of each species name and the start identifier "<start>" and the end identifier "<end>" can be represented by a vector, and all the constituent words of each species, the start identifier "<start>" and the end identifier "<end>" can be represented by a vector.

The vectors corresponding to all constituent words of each species, the vector corresponding to the start identifier "<start>", and the vector corresponding to the end identifier "<end>" are summed to obtain the vector representing each species.

We observed smaller values of the loss function than the original method when training on a dataset consisting of 401 randomly selected messages. This indicates that we have not only added material information but also optimized the original model.

![](https://static.igem.wiki/teams/4328/wiki/pictures/add.png)

### Network Hierarchy

After communicating with our PI Zhang Yue, we decided to use the
GAN algorithm to improve the accuracy of Kcat value
prediction. On the basis of the previous GNN and CNN,
we added the GAN part after merging the data. A Gaussian
distribution is used to generate a false vector according
to its mean and standard deviation after each new
vector is obtained. Both the true and false vectors
are used to calculate the value of the loss function.
We try to use multiple iterations to approximate
the real situation. However, it did not improve the
accuracy rate greatly. On the contray, it increased
the cost of training the model. So we abandoned this module.

## The improvement of user interactivity

In combination with user feedback, the software has
been developed with some new features to support mutating
amino acids at multiple sites of the enzyme and return
kcat values. Besides ,our software can also update
the GEM model in real time to see the effect of
mutation on flux. This greatly improves the usefulness
of the software and shortens the experimental cycle.

## Further Improvement

After summarizing the results of dry-lab and wet-lab
experiments and user's feedback, we have conducted
further research and make plans for the further
improvements of our software.

* At present, our software only uses the built-in
  GEM model of Actinoplanes sp. SE50/110 as the model
  organism, but in the future, we can design the
  GEM model of a certain organism input by the
  user automatically, implementing a series of
  prediction and validation functions of our software.
* The software can also be linked with large
  metabolomics databases and enzyme databases,
  using their updated metabolic reaction
  information and enzyme parameters to update
  GEM model in time.
* To be continued....

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