期刊
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
卷 20, 期 -, 页码 1993-2000出版社
ELSEVIER
DOI: 10.1016/j.csbj.2022.04.024
关键词
Transmembrane protein; Topology prediction; Transfer learning
资金
- National Natural Science Foundation of China [61772217, 62172172]
- Scientific Research Start-up Foundation of Binzhou Medical University [BY2020KYQD01]
- Fundamental Research Funds for the Central Universities [2016YXMS104, 2017KFYXJJ225]
DeepTMpred is a method for alpha-TMP topology prediction that utilizes pre-trained language model ESM, convolutional neural networks, attentive neural networks, and conditional random fields. It demonstrates superior predictive performance and rapid prediction speed.
Transmembrane proteins (TMPs) are essential for cell recognition and communication, and they serve as important drug targets in humans. Transmembrane proteins' 3D structures are critical for determining their functions and drug design but are hard to determine even by experimental methods. Although some computational methods have been developed to predict transmembrane helices (TMHs) and orientation, there is still room for improvement. 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. Compared with the current state-of-the-art tools on a non-redundant dataset of TMPs, DeepTMpred demonstrated superior predictive performance in most evaluation metrics, especially at the TMH level. Furthermore, DeepTMpred could also obtain reliable prediction results for TMPs without much evolutionary feature in a few seconds. A tutorial on how to use DeepTMpred can be found in the colab notebook (https://colab.research.google.com/github/ISYSLAB-HUST/DeepTMpred/blob/master/notebook/test.ipynb). (C) 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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