Improving the topology prediction of alpha-helical transmembrane proteins with transfer learning

We will release the latest version of DeepTMpred, refactor the entire code, and provide a web server!

Abstract

Considering that the pre-trained language model can make full use of massive unlabeled protein sequences to obtain latent feature representation for TMPs and reduce the dependence on evolutionary information, we proposed DeepTMpred, which used pre-trained self-supervised language models called ESM, convolutional neural networks, attentive neural network and conditional random fields for alpha-TMP topology prediction.

Dependencies

We used the following Python packages for core development. We tested on Python 3.7.

biopython
torch
scikit-learn
numpy

Dataset

Orientations of Proteins in Membranes (OPM) database: https://opm.phar.umich.edu/download

Pre-train model

We only provide the parameter files of the DeepTMpred-b.

sh ./script/download.sh

TMPs prediction script

python tmh_main.py tmh_model_path orientation_model_path your_fasta_file &

colab notebook

• Single Sequence Prediction
• Long Sequence Prediction(length>1024)
• Batch Sequence Prediction

Result

• Baidu disk：https://pan.baidu.com/s/16UG9vD94_kSkn7D7dZ5zdQ
• Password：rq3x

License

MIT

Contact

If you have any questions, comments, or would like to report a bug, please file a Github issue or contact me at wanglei94@hust.edu.cn.