RhoFold: Fast and Accurate RNA 3D Structure Prediction with Deep Learning

This is the opensource code for RhoFold.

Citation

@article{shen2022e2efold,
  title={E2Efold-3D: End-to-End Deep Learning Method for accurate de novo RNA 3D Structure Prediction},
  author={Shen, Tao and Hu, Zhihang and Peng, Zhangzhi and Chen, Jiayang and Xiong, Peng and Hong, Liang and Zheng, Liangzhen and Wang, Yixuan and King, Irwin and Wang, Sheng and others},
  journal={arXiv preprint arXiv:2207.01586},
  year={2022}
}

Table of contents

Recent Updates
Online Server
Local Environment Setup
- For Linux Users
- Download Pre-trained Model
Usage
Training Data
Citations
License

Updates

10/10/2022

Initial commits:

Pretrained model is provided.

Online Server

No need to create the environment locally, you can also access RhoFold easily through its online server: https://proj.cse.cuhk.edu.hk/aihlab/rhofold/

Local Environment Setup

Create Environment with Conda First, download the repository and create the environment.

Linux Users

(MacOS is currently not supported)

git clone https://github.com/RFOLD/RhoFold.git 
cd ./RhoFold
conda env create -f ./envs/environment_linux.yaml

Then, activate the "rhofold" environment.

conda activate rhofold
python setup.py install

Download pre-trained model

cd ./pretrained
wget https://proj.cse.cuhk.edu.hk/aihlab/rhofold/api/download?filename=rhofold_pretrained.pt -O rhofold_pretrained.pt
cd ../

Usage

Input Arguments

python inference.py

  --input_fas INPUT_FAS
                        Path to the input fasta file. Valid nucleic acids in RNA sequence: A, U, G, C. Input of sequence standalone is in testing. It's not as accurate as inputs of sequences combined with MSA. The former is only for the user to generate a quick reference structure.
  --input_a3m INPUT_A3M
                        Path to the input msa file, default None.
                        If --input_a3m is not given (set to None), MSA will be generated automatically.
  --output_dir OUTPUT_DIR
                        Path to the output dir. 
                        Tertiary Structure prediction is saved in .pdb format (pLDDT score is recorded in the B-factor column). 
                        Distogram prediction is saved in .npz format.
                        Secondary structure prediction is save in .ct format.     
  --device DEVICE       
                        Default cpu. If GPUs are available, you can set --device cuda:<GPU_index> for faster prediction.
  --ckpt CKPT           
                        Path to the pretrained model. Default ./pretrained/model_20221010_params.pt
  --relax_steps RELAX_STEPS
                        Num of steps for structure refinement, default 1000.
  --single_seq_pred 
                        Default False.
                        If --single_seq_pred is set to True, the modeling will run using single sequence only (input_fas)
  --database_dpath      
                        Path to the sequence database for MSA construction. Default ./database
  --binary_dpath
                        Path to the executable. Default ./rhofold/data/bin

Output Files

The outputs will be saved in the directory provided via the --output_dir flag of inference.py. The outputs include the unrelaxed structures, relaxed structures, prediction metadata, and running log. The --output_dir directory will have the following structure:

<--output_dir>/
    results.npz
    ss.ct
    unrelaxed_model.pdb
    relaxed_{relax_steps}_model.pdb
    log.txt

The contents of each output file are as follows:

results.npz – A .npz file containing the distogram prediction of RhoFold in NumPy arrays.
ss.ct – A .ct format text file containing the predicted secondary structure.
unrelaxed_model.pdb – A PDB format file containing the predicted structure from deep learning.
relaxed_{relax_steps}_model.pdb – A PDB format file containing the amber relaxed structure from unrelaxed_model.pdb.
log.txt – A txt file containing the running log.

Examples

Below are examples on how to use RhoFold in different scenarios.

Folding with sequence and given MSA as input

python inference.py --input_fas ./example/input/3owzA/3owzA.fasta --input_a3m ./example/input/3owzA/3owzA.a3m --output_dir ./example/output/3owzA/ --ckpt ./pretrained/rhofold_pretrained.pt

Folding with single sequence as input

1.Sequence standalone
This function is in testing. It's not as accurate as the MSA version. It's only for the user to generate a quick reference structure.

python inference.py --input_fas ./example/input/3owzA/3owzA.fasta --single_seq_pred True --output_dir ./example/output/3owzA/ --ckpt ./pretrained/rhofold_pretrained.pt

2.With our constructed MSA (Full version of RhoFold)

To support MSA construction, 3 sequence databases (RNAcentral, Rfam, and nt) totaling about 900GB need to be downloaded.

Warning: you should ensure that there are adequate spaces for saving the data! Otherwise you can directly utilize our online server, or download our off-the-shelf MSAs instead of regenerating them.

./database/bin/builddb.sh

Then you can run the following command lines:

python inference.py --input_fas ./example/input/3owzA/3owzA.fasta --output_dir ./example/output/3owzA/ --ckpt ./pretrained/rhofold_pretrained.pt

Training Data

You can access training data (13.86G) from the google drive link. The file includes the off-the-shelf MSAs of training data, which can be fed into RhoFold directly.

Citations

@article{shen2022e2efold,
  title={E2Efold-3D: End-to-End Deep Learning Method for accurate de novo RNA 3D Structure Prediction},
  author={Shen, Tao and Hu, Zhihang and Peng, Zhangzhi and Chen, Jiayang and Xiong, Peng and Hong, Liang and Zheng, Liangzhen and Wang, Yixuan and King, Irwin and Wang, Sheng and others},
  journal={arXiv preprint arXiv:2207.01586},
  year={2022}
}

License

This source code is licensed under the Apache license found in the LICENSE file in the root directory of this source tree.

phosseini/RhoFold