by Sumit Tarafder and Debswapna Bhattacharya
- Use mamba to create a virtual environment and install dependencies for RNAbpFlow.
conda install -n base -c conda-forge mamba
mamba env create -f RNAbpFlow.yml
- Activate the virtual environment
conda activate RNAbpFlow
Typical installation time on a "normal" desktop computer should take a few minutes in a 64-bit Linux system.
Instructions for running RNAbpFlow:
- Place your FASTA sequences and base pair maps in the Inputs folder (examples are provided).
- Add a list of PDB IDs to list.txt inside the Inputs folder (an example is included).
- Each line in list.txt contains a target ID with the number of sample structures to generate separated by space. If not specified, RNAbpFlow will use the default value specified in the configuration file in "configs" folder.
- Download the trained checkpoints from here and place the checkpoint folder in this repository.
- The default checkpoint configured is : RNA3DB.ckpt
- For CASP15 evaluation, edit the configs/inference.yaml to configure the "ckpt_path" field with checkpoint/CASP15.ckpt
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Run this command to generate sample 3D structures.
python3 inference.py -
RNAbpFlow will generate 3D structures for each ID listed in "list.txt" and place them inside 'Prediction' folder.
- Inference time to sample 10 RNA 3D structures for a typical RNA (~70 nucleotides) should take ~25 seconds on 1 GPU.
- We have provided multi-GPU support for large scale sample generation, gpu usage can be configured in the configuration file (inference.yaml).
- For training and benchmarking we used the train-test split provided by RNA3DB available here. We downloaded the May 16, 2024 release.
- For sampling performance comparison with RNAJP, we downloaded their decoy set from here and the corresponding native structures from PDB.
- For CASP15 benchmarking, we collected the training set from trRosettaRNA.