iGEM 2023 — Team ustc-software

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{% block title %}Notebook{% endblock %}
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[Notebook](#Earlier)

## Earlier

## 3.20-4.5

We made posters and prepared a PPT for the iGEMer recruitment presentation. We recruited 19 students from different disciplinary backgrounds and different grades. We are passionate about iGEM, but the road ahead is also full of challenges.

## 4.8

We held the first meeting of all members and made an overall introduction to the whole process of iGEM. Each member also introduced themselves and explained their respective strengths to facilitate the subsequent division of labor arrangement. At the same time, the biology group started to research the project and formulate the topic.

## 4.8—4.22

Within two weeks, the biology group researched previous years' iGEM projects for inspiration and determined our initial idea: using computer knowledge to assist in solving biological problems. The biology group consulted with seniors to collect the difficulties they encountered, and under the leadership of Zhuofan Cui, we determined our initial topic: exploring the construction of a bioinformatics tool in order to achieve a one-stop service for protein stability optimization.

## 4.22

We held a second all-members meeting, where Zhuofan Cui introduced our preliminary program to all members and determined the next steps for the members of different groups. Students from the Biology group started to investigate bioinformatics tools. It was obvious that it was quite difficult to get so many students from different disciplinary backgrounds to work together to accomplish something, but we still believed that we could overcome the difficulties.

## Late April

USTC-software participated in the Science and Technology Week organized by the University of Science and Technology of China (USTC), where we set up a booth on campus and talked about synthetic biology and helped children to complete some hands-on experiments. Primary and secondary school students from all over China visited our booth and showed great enthusiasm for synthetic biology.

## 5.10

We held the third meeting of all members, and unanimously approved Cui Zhuofan's proposal. The art team started to make the promotional posters and the project logo, the front-end team and the back-end team started to work together to learn the specific ways of calling the source website and to confirm their understanding, ready to start the writing work, and everyone in the team actively prepared for the work.

## 5.10-6.5

The front-end and back-end investigated the few software identified even further, analyzing SWISS-MODEL, PROSS, AlphaFold2, Jalview, PyMoL, PLIP, NovoPro, and Uniprot for their functionality, open-source status, ability to be deployed locally, and input/output feedback methods. The difficulty of the task was identified: the lack of specific ways of using these software and the trade-offs between the relevant output information.

## 6.5-7.16

The back-end team started investigating Zero-shot Learning, which includes both local and global evolutionary models.

## Mid-term

## 7.16-7.25

The front-end team used the wiki source code to familiarize themselves with the GitLab development operation, understood the basic structure of the source code, learned how to refer to external libraries and images with the reminder of USTC-Software2022, and divided the tasks for each team member. The back-end team went a step further and tried to deploy open source code. In this process, we frequently encountered problems such as old code versions, incomplete code, and effects that did not meet the requirements. In the end, only the EVMutation and Multi-Sequence Comparison sections were successfully completed. After hitting the wall several times, we discussed the feasibility of the current solution with the biology group, and finally we decided to go back to the original solution.

## 7.16-8.5

After the midterm exams, we visited our PI, Mr. Hong Jiong, and explained to him our problem that it was difficult for the Biology group to work together with the front and back-end students because of the different knowledge backgrounds. Mr. Hong gave a lot of practical advice. After that, we decided to divide the whole team into several groups, each including two students from the biology group and two students from the back-end group, and each group was responsible for a portion of the research and development work, in order to promote the communication between students. Finally, our team functioned organically.

## 8.5-8.11

Students in the front-end group applied for our cloud server on the Tencent cloud platform, investigated how to use Flask to build our software website, and made a preliminary attempt to build the website. Students in the back-end group deployed the code to the cloud server and actively cooperated with the front-end group to debug the program.

## 8.18-9.6

We collaborated with iGEM-USTC to provide a duplex codon-based optimization algorithm for codon optimization. The back-end group has completed single codon optimization, and the back-end group tries to improve the performance of the existing single codon optimization with the duplex codon algorithm. Since there is no readily available data on the frequency of duplex codon distribution in different species, the back-end group, after consulting with iGEM-USTC and the biology group, wrote a script to count the distribution of duplex codons on the National Library of Medicine.

The front-end group designed and developed basic UI interfaces for each part of the software based on the research work of their respective groups, and began to try to develop the front-end and back-end interactions. In the process of interaction development, due to the lack of experience, there was a mismatch between the send destination address and the receive address in the message transmission. Under the guidance of the senior of USTC-Software2022, we gradually solved the problem and finally found and solved the problem, and realized the unification of the form upload function and the address based on the ajax tool.

## 9.1-9.12

The back-end team completed the testing of EVMutation multilocus mutation and created a mutation cloud based on the mutation results.

The art team successfully completed the production of the promotional video for Prot-DAP.

The front-end started to beautify the user interface of the software with the design participation of the art team. In the process of interface beautification, in the spirit of striving for excellence, the front-end team and the art team reached a consensus on the first draft of our software page design after many discussions and comparisons of different actual effect diagrams, and after many revisions.

## 9.7-9.16

The team organized and participated in the Science and Technology Week volunteer activities, as well as community volunteer activities, explaining synthetic biology to children and parents.

The front-end team carried out the deployment of the back-end code on the cloud server, realized the front-end and back-end interactive functions of protein search, multiple sequence comparison, structure prediction, and primer design, and verified the feasibility of its operation. Subsequently, the development of the output visualization page and articulation of the software began. In the process of development, the students of the front-end group had an in-depth and extensive communication with the biology group. During the exchanges, the front-end group gradually grasped the biological significance of the inputs and outputs of each part of the process and how they were connected, determined the format of the visual outputs of each part as well as the articulation hints, and also understood some of the shortcomings of the current website's design, added file uploading functions, and finally established a preliminary and usable website.

## Later

## 9.18

Led by Mr. Desmond Hong, we visited Huaheng Bio-Research Institute and talked with the head of the research institute, who gave us a lot of meaningful suggestions.

## 9.23-10.13

We solved the algorithm problem of codon optimization and added the function of PSSM matrix construction to the software, and organically connected the software of each link. With user-friendliness as an important indicator, we further beautified the interface of our product by adding process advancing arrows and input prompts, adding a loading animation, providing check buttons for site mutation to directly modify the corresponding sites, and realizing the guidance of the whole process to make it more suitable for the public to use and more user-friendly.

We completed the content writing of the wiki according to the task assignment, and cooperated with the artist to beautify the wiki interface.

## 10.20

We completed the team's introductory video

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[Engineering](#Design)

## Preliminary Design

After recognizing the plethora and complexity of platforms in the protein optimization and expression process, with no one-stop service and unfriendliness to beginners, we embarked on the integration of various platforms. Our initial idea is as follows:

* Step1: Users input protein sequence.
* Step2: The backend optimizes the protein sequence for users to download.
* Step3: Display optimization sites to the user.
* Step4: Show the potential structure of the optimized protein sequence to the user.
* Step5: Users choose the vector and insertion site.
* Step6: According to the homologous recombination vector construction method, primers are designed for the optimized protein sequence and vector respectively to obtain a complete expression system.

The advantages of this design compared to other software are:

1. Higher integration and user-friendliness. Compared to other bioinformatics tools with singular functionalities, this design realizes the full process of protein sequence optimization, structural display, and final expression.
2. More valuable information is provided to users. Not only can the platform optimize the protein sequence, but it can also display the optimization results and predict the protein structure. This allows users to use basic biochemical knowledge to target the optimized protein's properties.

## Build

### Model Selection

#### PROSS

In the domain of protein optimization, we initially contemplated approaching it from a unique optimization perspective. Many natural and designed proteins exhibit only marginal stability, which limits their practicality in both research and diverse applications. So we turned to PROSS, an automated structure and sequence-based design method for optimizing protein stability and heterologous expression levels. Using position-specific substitution matrix (PSSM), PROSS can select “allowed” mutation. Then by calculating the energy difference between the original sequence and the sequence with “allowed” mutation, Pross can generate a new sequence that will be more stable and having higher heterologous expression level.

#### SWISS-MODEL

During the expression process, proteins may encounter issues such as abnormal folding, making the prediction information of protein structure crucial for users. There is a principle in structural biology: if the amino acid sequences of proteins are similar, their three-dimensional structures are likely to be similar. The higher the similarity of the amino acid sequences, the higher the similarity of the three-dimensional structures, generally speaking.

SWISS-MODEL, based on the aforementioned principle, predicts the three-dimensional structure of proteins whose sequences are known but structures are unknown.

Using the structures of proteins that are highly similar (highly homologous) in sequence to the unknown protein, and whose structures are already known, as templates, it is possible to make relatively accurate predictions about the unknown protein structure.

#### BLAST & Primer3

When we aim to construct a protein expression system using the homologous recombination method, the commonly utilized technique in the lab is the Gibson Assembly. We employ Primer3 to design primers for both the vector and the protein and then assemble them together.

BLAST (Basic Local Alignment Search Tool) is an algorithm used for sequence alignment. In this context, BLAST is employed to inspect whether there are any mismatches in the protein and vector sequences at sites other than the termini, which could result in assembly failures.

### Tools

#### Back-end

![](https://static.igem.wiki/teams/4659/wiki/tencent-cloud-logo.png)

Tencent Cloud

Tencent Cloud is a product of Tencent, providing developers and enterprises with cloud services, cloud data, cloud operations and other integrated one-stop service solutions. We use TencentCloud Lighthouse to deploy our back-end applications which is Easier to use and closer to the application.

#### Front-end

![](https://static.igem.wiki/teams/4659/wiki/flask-logo.png)
[Flask](https://icons8.com/icon/MHcMYTljfKOr/flask) icon by [Icons8](https://icons8.com)

Flask

Flask is a Web microframework written in Python, which is based on the robust foundation of the Jinja2 template engine and Werkzeug's comprehensive WSGI web application library, allowing us to quickly implement a website or Web service using the Python language.

![](https://static.igem.wiki/teams/4659/wiki/nginx-logo.png)
[Nginx](https://icons8.com/icon/t2x6DtCn5Zzx/nginx) icon by [Icons8](https://icons8.com)

Nginx

Nginx is an open source reverse proxy server for HTTP, HTTPS, SMTP, POP3, and IMAP protocols, as well as a load balancer, HTTP cache, and a web server (origin server). The nginx project started with a strong focus on high concurrency, high performance and low memory usage. It is licensed under the 2-clause BSD-like license and it runs on Linux, BSD variants, Mac OS X, Solaris, AIX, HP-UX, as well as on other \*nix flavors. It also has a proof of concept port for Microsoft Windows. We deploy our Flask project with Nginx.

## Test

1. During testing, PROSS performed well in enhancing protein stability, but its challenge to the server made it difficult for us to achieve its full efficacy. At the same time, PROSS must receive structure files in PDB format as input, preferably X-ray parsing structure, and such format files are not friendly to inexperienced workers. We wanted to find a model that was more general and more friendly to novice users.
2. When students in the wet lab used our prot-DAG codon optimization, they found that the results of single codon optimization did not get good results by comparing with the actual experimental results.
3. After switching to EVmutation based on unsupervised learning, in the course of the initial test, we found that the protein input EVmutation had a mismatch in sequence length. At the same time, the students who participated in the test reported that when preparing the source data for mutation site prediction, it was necessary to manually add protein sequences one by one, which greatly increased the complexity of the user's operation.
4. After obtaining the correct results of EVmutation operation, the test students said that the tabular output results were not very helpful for the intuitive analysis of the mutation site, but the heat map drawn by the delta\_E table can provide intuitive and effective guidance on the selection of mutation sites. At the same time, the test students recommended Jalview, a 2D visualization tool, whose embedded JalviewJS may provide some help for us.

## Learn

### Iterations

1. **Iterations in protein optimization model: enhance versatility, adapt to more scenarios, and facilitate easy invocation:**

   PROSS presents challenges in direct deployment to servers, leading to a subpar integrated user experience. Moreover, the application scope of PROSS is limited to enhancing protein stability. In order to increase the universality of our tool, we explored the use of models trained via machine learning for protein optimization. After trialing multiple models, we selected EVmutation, an unsupervised learning-based protein optimization model. Compared to other models, it offers higher efficiency and produces more consistent results.

   For codon optimization, we collaborated with the experimental team. Based on recent research indicating that optimizing based on two consecutive codons yields better results than single codon optimization, we integrated a di-codon-based codon optimization algorithm co-authored by both the software and experimental teams.
2. **Iterations in Integration: Enhance integration and increase convenience:**

   We've incorporated the Uniprot protein query module, allowing for the direct input of multiple protein results into EVmutation. This eliminates the need to manually search and compile proteins from the same family as datasets for EVmutation.

   In our expression system, we've added data for several commonly used plasmids, relieving a significant portion of users from the hassle of searching for and entering vector sequences themselves.
3. **Iterations in user-friendliness: Enhance output information for a deeper user understanding:**

   We've incorporated Jalview to provide a 2-D visualization of mutation sites, allowing users to more easily identify the mutation locations. We've visualized the saturation mutation prediction results from EVmutation and displayed them to users as a heatmap. This presentation allows users to gain a clearer insight into whether a specific mutation at a particular site might lead to protein optimization.

### Improvement of build

1. **Build a user-friendly GUI interface:**

   Our interface design aims to minimize cognitive load for the user. Through a clear and straightforward layout, consistent color coding, and intuitive icons, users can quickly become familiar with the platform upon their first visit. We also provide detailed hints and documentation to ensure users never feel lost or confused during their experience
2. **Suitable for a wide range of users, from beginners to seasoned researchers:**

   Recognizing the diversity of user backgrounds, skills, and experiences, our interface was rebuilt to offers a high degree of customization. Whether a beginner or an expert, everyone can adjust the tool's parameters and display according to their needs and preferences.

## Reference

[1] Jonathan Jacob Weinstein, Adi Goldenzweig, ShlomoYakir Hoch, Sarel Jacob Fleishman, PROSS 2: a new server for the design of stable and highly expressed protein variants, Bioinformatics, Volume 37, Issue 1, January 2021, Pages 123-125, <https://doi.org/10.1093/bioinformatics/btaa1071>

[2]Goldenzweig A, Goldsmith M, Hill SE, Gertman O, Laurino P, Ashani Y, Dym O, Unger T, Albeck S, Prilusky J, Lieberman RL, Aharoni A, Silman I, Sussman JL, Tawfik DS, Fleishman SJ. Automated Structure- and Sequence-Based Design of Proteins for High Bacterial Expression and Stability. Mol Cell. 2016 Jul 21;63(2):337-346. doi: 10.1016/j.molcel.2016.06.012. Epub 2016 Jul 14. Erratum in: Mol Cell. 2018 Apr 19;70(2):380. PMID: 27425410; PMCID: PMC4961223.

[3] Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, Heer FT, de Beer TAP, Rempfer C, Bordoli L, Lepore R, Schwede T. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018 Jul 2;46(W1):W296-W303. doi: 10.1093/nar/gky427. PMID: 29788355; PMCID: PMC6030848.

[4] Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, Rozen SG. Primer3--new capabilities and interfaces. Nucleic Acids Res. 2012 Aug;40(15):e115. doi: 10.1093/nar/gks596. Epub 2012 Jun 22. PMID: 22730293; PMCID: PMC3424584.

[5] Hopf TA, Ingraham JB, Poelwijk FJ, Schärfe CP, Springer M, Sander C, Marks DS. Mutation effects predicted from sequence co-variation. Nat Biotechnol. 2017 Feb;35(2):128-135. doi: 10.1038/nbt.3769. Epub 2017 Jan 16. PMID: 28092658; PMCID: PMC5383098.

[6] Chung BK, Lee DY. Computational codon optimization of synthetic gene for protein expression. BMC Syst Biol. 2012 Oct 20;6:134. doi: 10.1186/1752-0509-6-134. PMID: 23083100; PMCID: PMC3495653.

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[Experiments](#Overview)

##

In this section, we will showcase the integrated protein design and development platform developed by our team. We will provide a brief overview of how to use the platform. Currently, the platform is not perfect, with many features temporarily unavailable. We welcome your valuable suggestions after using it to help us improve the platform.

##

## Protein Searching

Enter the name of the gene of interest to search for the target protein series.(Based on the UniProt Knowledgebase)

![Protein search demonstration](https://static.igem.wiki/teams/4659/wiki/demonstration1.png)

Fig.1 Protein Search Demonstration

## Multiple Sequence Alignment

Upload your protein series from previous interface to generate alignments.

![MSA demonstration](https://static.igem.wiki/teams/4659/wiki/demonstration2.png)

Fig.2 MSA Demonstration

## Mutation Site Prediction

Enter your MSA results to predict possible mutation sites based on EVmutation.

**Attention: Make sure your target sequence is at the top!**

By checking the selected boxes in the result table, you're able to change the corresponding sites in Structure Prediction and Codon Optimization.

![Mutation Site Prediction Demonstration](https://static.igem.wiki/teams/4659/wiki/demonstration3.png)

Fig.3 Mutation Site Prediction Demonstration

## PSSM

Use Position-Specific Scoring Matrix to predict possible mutation sites.

![PSSM Demonstration](https://static.igem.wiki/teams/4659/wiki/demonstration4.png)

Fig.4 PSSM Demonstration

## Structure Prediction

If you have used the Mutation Site Prediction, you can change your input accordingly checking the selected boxes. Or you can type your interested sequence here independently.

**Then downloading the .pdb file and viewing its structure on PyMol !**
![Structure Prediction Demonstration](https://static.igem.wiki/teams/4659/wiki/demonstration5.png)

Fig.5 Structure Prediction Demonstration

## Codon Optimization

If you have used the Mutation Site Prediction, you can change your input accordingly checking the selected boxes.

![Codon Optimization Demonstration1](https://static.igem.wiki/teams/4659/wiki/demonstration6.png)

Fig.6 Codon Optimization Demonstration.1

Codon type selection

![Codon Optimization Demonstration2](https://static.igem.wiki/teams/4659/wiki/demonstration7.png)

Fig.7 Codon Optimization Demonstration.2

## Primer Design

Enter the DNA sequence from the previous section, select one of the typical vectors and indicate your anticipated insert position.

![Primer Design Demonstration](https://static.igem.wiki/teams/4659/wiki/demonstration8.png)

Fig.8 Primer Design Demonstration

![Primer Design Demonstration](https://static.igem.wiki/teams/4659/wiki/demonstration9.png)

Fig.8 Primer Design Demonstration(selections of the typical vectors)

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[Results](#Results)

## Project Test Examples and Output Results

1. Query GSTP1 as the test target Protein in Protein Search.

![First Step](https://static.igem.wiki/teams/4659/wiki/result1.png)

2. Import the Protein Search file into MSA.

![Second Step](https://static.igem.wiki/teams/4659/wiki/result2.png)

3. Download the MSA result, adjust the target sequence GFP\_AEQVI to the top, copy all sequences and enter Mutation Site Prediction to get the heat map and recommended mutation site table; Import the sequence file into Jalview to provide a 2-D visualization of mutation sites

![Third Step](https://static.igem.wiki/teams/4659/wiki/result3-1.png)
![Third Step](https://static.igem.wiki/teams/4659/wiki/result3-2.png)

4. Copy all sequences and input PSSM module to obtain corresponding PSSM.

![Fourth Step](https://static.igem.wiki/teams/4659/wiki/result4.png)

5. Site No. 38 was selected for mutation from V to T, and the mutant sequence was automatically filled into Structure Prediction and Codon Optimization.

![Fifth Step](https://static.igem.wiki/teams/4659/wiki/result5.png)

6. In the Codon Optimization module, select Rattus norvegicus option to submit and get the DNA sequence file after Codon Optimization.

![sixth Step](https://static.igem.wiki/teams/4659/wiki/result6.png)

7. Copy and paste the DNA sequence into Primer Design. Select PUC19 as the vector and insert position as 70.

![seventh Step](https://static.igem.wiki/teams/4659/wiki/result7.png)

8. Full results:

![eighth Step](https://static.igem.wiki/teams/4659/wiki/result8.png)

## Statistical Evaluation of Users

To better understand users' real needs and make our platform more practical and reliable, we conducted a trial activity at three universities: USTC, HFUT, and AHU. After trying out the platform, users could fill out our questionnaire, provide feedback, and suggest modifications. Over the three days from October 10th to October 12th, we collected a total of 146 questionnaires. Here is a statistical analysis of the questionnaire results:

In terms of personnel structure, out of the 146 participants, 136 were from USTC, and the remaining 10 were from HFUT and AHU. Most of them were first and second-year students, mainly from biology-related majors.

![Survey Result 1](https://static.igem.wiki/teams/4659/wiki/question1.png)
![Survey Result 2](https://static.igem.wiki/teams/4659/wiki/question2.png)
![Survey Result 3](https://static.igem.wiki/teams/4659/wiki/question3.png)

Fig.1 Personal Information of Participants

Regarding the trial experience, the majority of users found our interface design and feature guidance to be excellent, scoring 4.64 and 4.68, respectively.

![Survey Result 4](https://static.igem.wiki/teams/4659/wiki/question4.png)
![Survey Result 5](https://static.igem.wiki/teams/4659/wiki/question5.png)

Fig.2 Evaluation of Interface Design and Guidance

In terms of specific functionality, most users believed that our platform could help them in learning synthetic biology and designing protein sequences to some extent. However, many also felt that there is room for improvement in our platform.

![Survey Result 6](https://static.igem.wiki/teams/4659/wiki/question6.png)
![Survey Result 7](https://static.igem.wiki/teams/4659/wiki/question7.png)

Fig.3 Benefit Evaluation

Of course, we understand that conducting simple online trials may not capture more professional guidance. Therefore, we also reached out to several peers who conducted in-depth trials and evaluations of our platform. For more details, please click on the link:
[Website Usage Experience](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27human-practices%27%29%20%7D%7D)

If you wish to access the raw data of our statistical table, please click Platform\_usage\_feedback\_survey.xlsx.

## Failed Attempts Before

At first, we considered modeling and experimental verification of some metabolic pathways in biology based on machine learning and deep learning. However, for a period of time, we couldn't find suitable metabolic pathways, so we temporarily shelved this direction. Later, when a senior student introduced some issues encountered in his research topic, we learned that the upstream design for solubility optimization required back-and-forth jumps and multiple iterations, resulting in significant time costs. Thus, we considered designing a comprehensive and user-friendly platform to accomplish this task.

![](https://static.igem.wiki/teams/4659/wiki/attempts1.png)

After our preliminary design, we successfully implemented the basic framework. However, challenges soon emerged. There were issues with the invocation of Pross on the server, which meant we could not provide a stable and convenient channel for users. In the end, to ensure the user experience, we decided to abandon the use of PROSS. After referring to a review in this year's Cell System, while retaining the basic design process, we changed the solubility optimization module to a more general rational design of proteins, or simply put, protein sequence design.

![](https://static.igem.wiki/teams/4659/wiki/attempts2.png)

We extensively researched protein sequence design models. Among them, the semi-supervised model caught our attention due to its superior performance. However, another challenge arose: the semi-supervised model required downstream lab validation and database construction. Although we made contact with the USTC's IGEM experimental team, cooperation couldn't be realized due to synchronization issues in our schedules. Consequently, we had no choice but to abandon this option.

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