ASOptimizer

ASOptimizer consists of a database and two computational models: sequence engineering and chemical engineering.

• To access ASOptimizer, we have developed a web server that runs ASOptimizer on the backend and provided free access to it. You can access it through the following link: http://asoptimizer.s-core.ai/
• To access the figures mentioned in the paper, you can use the following code

0. Requirements

• Python 3.6
• Install required libraries using the following command:

pip3 install -r requirements.txt

• Please download the following data files and place npy files into the "/sequence_engineering/features" folder
  • https://drive.google.com/file/d/1RSdweiYhFJq6gs3M4ns2tH6OXzehAZZb/view?usp=sharing

1. Database

1.1 For sequence engineering

• /ido-patent/EFO21_experiments.csv, /ido-patent/SK0V3_experiments.csv

  • Database of experimental observations from the granted patent "Immunosuppression-reverting oligonucleotides inhibiting the expression of IDO"
  • We would like to express our gratitude to all contributors who provided valuable insights and resources for this research.

• /patent_experiments/aso_features_patent.csv

  • ASO candidates from the granted patent "Immunosuppression-reverting oligonucleotides inhibiting the expression of IDO" and corresponding features"

• /features/IDO1_features.csv

  • 19-mer ASO candidates for regulating IDO1 mRNA and corresponding features

2. Sequence Engineering

To perform sequence engineering, use the following command:

python3 main.py --mode 'train' --target 'ido1' --seq_len 19 --num_candidates 6 --rnastructure 'mfold' 

To perform sequence engineering with your chosen setting, use the following command:

python3 main.py --mode 'eval' --target 'ido1' --seq_len 19 --num_candidates 6 --rnastructure 'mfold' --a_star "1 1 1 1" 

Code

• Linear_regression.ipynb:

  • Displays a scatter plot comparing experimentally observed inhibition rates (x-axis) with their predicted values (y-axis) (Figure 1A and Figure 1B)
  • Comparison with sfold

• Plot_Contour.ipynb:

  • Displays a surface plot of Pearson correlation (ρ), represented by contour plots (Figure 1C and Figure 1D)

• Getting_ASOpt_top6.ipynb:

  • Shows a histogram depicting the predicted scores of complementary ASOs, each 19 nucleotides in length, targeting the IDO1 gene (Figure S2)

Citing this work

Please cite the following paper if you find the code and data useful:

@article{ASOptimizer2024, 
    title = {ASOptimizer: Optimizing antisense oligonucleotides through deep learning for IDO1 gene regulation}, 
    author = {Hwang, Gyeongjo and Kwon, Mincheol and Seo, Dongjin and Kim, Dae Hoon and Lee, Daehwan and Lee, Kiwon and Kim, Eunyoung and Kang, Mingeun and Ryu, Jin-Hyeob}, 
    journal = {Molecular Therapy - Nucleic Acids}, 
    volume = {35}, 
    number = {2}, 
    pages = {102186}, 
    year = {2024}, 
    month = jun, 
    DOI = {10.1016/j.omtn.2024.102186}, 
    ISSN = {2162-2531}, 
    publisher = {Elsevier BV}, 
    url = {http://dx.doi.org/10.1016/j.omtn.2024.102186}}   

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