DeepHelicon is a deep learning-based approach that accurately predicts inter-helical residue contacts in transmembrane proteins by utilising coevolutionary features and a two-stage deep learning framework to refine contact maps.
Please check https://2003100127.github.io/deephelicon for its usage.
-
(https://pypi.org/project/deephelicon)
conda create --name deephelicon python=3.11 conda activate deephelicon pip install deephelicon --upgrade
Sun, J., & Frishman, D. (2020). DeepHelicon: Accurate prediction of inter-helical residue contacts in transmembrane proteins by residual neural networks. Journal of Structural Biology, 212(1), 107574. `10.1016/j.jsb.2020.107574
In .bib form:
@article{deephelicon2020,
title = {DeepHelicon: Accurate prediction of inter-helical residue contacts in transmembrane proteins by residual neural networks},
author = {Jianfeng Sun and Dmitrij Frishman},
journal = {Journal of Structural Biology},
doi = {https://doi.org/10.1016/j.jsb.2020.107574},
issn = {1047-8477},
issue = {1},
pages = {107574},
volume = {212},
url = {http://www.sciencedirect.com/science/article/pii/S1047847720301477},
year = {2020},
}
