Introduction

LMetalSite is an alignment-free sequence-based metal ion-binding site predictor based on pretrained language model (LM) and multi-task learning. LMetalSite is easy to install and use, and is also accurate (surpassing the state-of-the-art structure-based methods) and really fast. Prediction of 500 sequences with an average length of 300 only takes about 2 minutes using GPU, or 20 minutes using CPU. If your input is small, you can also use our web server of LMetalSite.

System requirement

LMetalSite is developed under Linux environment with:
python 3.8.5
numpy 1.19.1
pandas 1.1.3
torch 1.7.1
sentencepiece 0.1.96
transformers 4.17.0
tqdm 4.59.0

Pretrained language model

You need to prepare the pretrained language model ProtTrans to run LMetalSite:

1. Download the pretrained ProtT5-XL-UniRef50 model (guide). # ~ 11.3 GB (download: 5.3 GB)
2. Set path variable ProtTrans_path in ./script/LMetalSite_predict.py.

Run LMetalSite for prediction

Simply run:

python ./script/LMetalSite_predict.py --fasta ./example/demo.fa --outpath ./example/

And the prediction results will be saved in ./example/demo_predictions.csv. We also provide the corresponding canonical prediction results in ./example/demo_predictions_ref.csv for your reference.

Other parameters:

--feat_bs       Batch size for ProtTrans feature extraction, default=10
--pred_bs       Batch size for LMetalSite prediction, default=16
--save_feat     Save intermediate ProtTrans features
--gpu           Use GPU for feature extraction and LMetalSite prediction

Dataset and model

We provide the datasets and the trained LMetalSite models here for those interested in reproducing our paper. The metal ion datasets used in this study are stored in ./datasets/ in fasta format.
The trained LMetalSite models from 5-fold cross-validation can be found under ./model/.

Citation and contact

Citation:

@article{10.1093/bib/bbac444,
    author = {Yuan, Qianmu and Chen, Sheng and Wang, Yu and Zhao, Huiying and Yang, Yuedong},
    title = "{Alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning}",
    journal = {Briefings in Bioinformatics},
    year = {2022},
    month = {10},
    issn = {1477-4054},
    doi = {10.1093/bib/bbac444},
    url = {https://doi.org/10.1093/bib/bbac444},
}

Contact:
Qianmu Yuan (yuanqm3@mail2.sysu.edu.cn)
Yuedong Yang (yangyd25@mail.sysu.edu.cn)