The data, supplementary information and standalone program of ESM-NBR.
- Python(3), numpy, pytorch, esm(https://github.com/facebookresearch/esm)
- Linux system (suggested CentOS 7)
$ python prediction.py your_fasta_path device save_dir model_type split_len dna_threshold rna_threshold
- your_fasta_path: path of input protein sequences in fasta format. (support multi-sequence)
- device: cpu or cuda
- save_dir: The directory where the results are generated
- model_type: YK17 or DRNA1068
- split_len: Long sequences are cut into short sequences to generate ESM feature representations.
- dna_threshold: 0.8 is recommended.
- rna_threshold: 0.1 is recommended.
- It will take some time to download the ESM2 model for the first time, so please be patient.
[1] Wenwu Zeng, Dafeng Lv, Wenjuan Liu, and Shaoliang Peng. ESM-NBR: fast and accurate nucleic acid-binding residue prediction via protein language model feature representation and multi-task learning. Submitted.