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Using ProtTrans protein language model to get dyhedrals

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Project: Martini Dyhedrals powered by AI

The aim of this repo is to be able to predict the probability of secondary structure conformation based on the sequence of amino acid only with a Artifical intelligence model: ProtTrans. Once these probabilities computed, give personalised dyhedral angle potentials for each amino acid based on sequence information. This will make it possible to make proteins more flexible/regionally oriented and thus ensure better folding.

Requirements

Before using this code, ensure that you have the following prerequisites in place:

  • Python 3.7 or later
  • PyTorch
  • Transformers library
  • H5py
  • Numpy
  • Pandas

Please find all information in requirements

If you plan to run this code locally, you need access to a GPU with at least 5.2GB of memory.

How to Run Localy

Command Line

  1. Clone the repository to your local machine.

    git clone https://github.com/Romumrn/martini_AI
    cd martini_AI
  2. Install the required Python libraries using pip.

pip install -r requirements.txt
  1. Run the code for protein secondary structure prediction. You can provide a sequence as a command-line argument.
python run_martini_AI.py --sequence "SEQUENCE" --sequence_id "ID"
#or
python run_martini_AI.py --fasta protain.fasta

Run in Google Colab

You can also try the test case provided on google collab

Run in Jupyter notebook

(Not tested yes)

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Using ProtTrans protein language model to get dyhedrals

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