DeepTMInter is a deep learning framework for accurately predicting interaction sites in α-helical transmembrane proteins using sequence-derived features, enabling large-scale annotation of membrane protein interactions and analysis of drug targets.
Whether you’re working in computational drug discovery, bioinformatics, or protein science, DeepTMInter provides a ready-to-use solution for protein interactions.
Please check https://2003100127.github.io/deeptminter for its usage.
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(https://pypi.org/project/deeptminter)
conda create --name deeptminter python=3.11 conda activate deeptminter pip install deeptminter --upgrade
Sun, J., & Frishman, D. (2021). Improved sequence-based prediction of interaction sites in α-helical transmembrane proteins by deep learning. Computational and structural biotechnology journal, 19, 1512–1530. https://doi.org/10.1016/j.csbj.2021.03.005
In .bib form.
@article{deeptminter2021,
title = {Improved sequence-based prediction of interaction sites in α-helical transmembrane proteins by deep learning},
author = {Jianfeng Sun and Dmitrij Frishman},
journal = {Computational and Structural Biotechnology Journal},
volume = {19},
pages = {1512-1530},
year = {2021},
issn = {2001-0370},
doi = {https://doi.org/10.1016/j.csbj.2021.03.005},
url = {https://www.sciencedirect.com/science/article/pii/S2001037021000775},
}
