semvec/pypi-packages huggingface dataset:
rna-torsionbert 0.1.4
Add your description here
Requires Python: >=3.10

# RNA-TorsionBERT

`RNA-TorsionBERT` is a 86.9 MB parameter BERT-based language model that predicts RNA torsional and pseudo-torsional angles from the sequence.

![](./img/dnabert_architecture_final.png)


`RNA-TorsionBERT` is a DNABERT model that was pre-trained on ~4200 RNA structures.

It provides improvement of [MCQ](https://github.com/tzok/mcq4structures) over the previous state-of-the-art models like 
[SPOT-RNA-1D](https://github.com/jaswindersingh2/SPOT-RNA-1D) or inferred angles from existing methods, on the Test Set (composed of RNA-Puzzles and CASP-RNA).

## Installation

To install RNA-TorsionBERT and it's dependencies following commands can be used in terminal:

```bash
pip install -r requirements.txt 
```


## RNA-TorsionBERT usage

To run the RNA-TorsionBERT, you can use the following command line:
```bash
python -m src.rna_torsionBERT_cli [--seq_file] [--in_fasta] [--out_path]
```

The arguments are the following:
- `--seq_file`: RNA Sequence. 
- `--in_fasta`: Path to the input sequence fasta file. 
- `--out_path`: Path to a `.csv` file where the output will be saved. 

You can also import in your python code the class `RNATorsionBERTCLI` from `src.rna_torsionBERT_cli`. 


## TB-MCQ

TB-MCQ stands for TorsionBERT-MCQ, which is a scoring function to assess the quality of a predicted structure in torsional angle space.
Given the inferred angles from the structures and the predicted angles from the model, TB-MCQ computes the quality of the predicted angles using 
the [MCQ](https://github.com/tzok/mcq4structures) (mean of circular quantities) metric.

![](./img/torsion_bert_mcq_T.png)

To run the TB-MCQ scoring function, you can use the following command line:
```bash
python -m src.rna_torsion_cli [--in_pdb] [--out_path]
```
with:

- `--in_pdb`: Path to the input PDB file.
- `--out_path`: Path to a .csv file where the output will be saved.


## Docker 
To run the code using `Docker`, you can use the following command line:
```bash
docker build -t rna_torsionbert .
docker run -it rna_torsionbert 
```

It will enter into a bash console where you could execute the previous commands with all the installations done. 

To have example of commands, you can look at the `Makefile`.


## Citation

```bibtex
@article {rna_torsionbert,
	author = {Bernard, Clement and Postic, Guillaume and Ghannay, Sahar and Tahi, Fariza},
	title = {RNA-TorsionBERT: leveraging language models for RNA 3D torsion angles prediction},
	elocation-id = {2024.06.06.597803},
	year = {2024},
	doi = {10.1101/2024.06.06.597803},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2024/07/05/2024.06.06.597803},
	journal = {bioRxiv}
}
```