ArticleComputational and structural biotechnology journal2022
Improving the topology prediction of α-helical transmembrane proteins with deep transfer learning.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 20 citations in OpenAlex.
- Integrative structural interactomics reveals protein organization and structure in a giant virus.Nature communications · 2026Article
- Selenoprotein K Is a Peripheral ER Membrane Protein.Biochemistry · 2025Article
- NeuroScale: evolutional scale-based protein language models enable prediction of neuropeptides.BMC biology · 2025Article
- Deep Learning Approaches for the Prediction of Protein Functional Sites.Molecules (Basel, Switzerland) · 2025Review
- A Survey of Current Status in AI-Based Topology Prediction of Transmembrane Proteins.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Proteome-wide analysis reveals G protein-coupled receptor-like proteins in rice (Plant signaling & behavior · 2024Article
- DeepNeuropePred: A robust and universal tool to predict cleavage sites from neuropeptide precursors by protein language model.Computational and structural biotechnology journal · 2024Article
- Molecular dynamics of the human RhD and RhAG blood group proteins.Frontiers in chemistry · 2024Article
- Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications.Computational and structural biotechnology journal · 2023Review
- Predicting the Assembly of the Transmembrane Domains of Viral Channel Forming Proteins and Peptide Drug Screening Using a Docking Approach.Biomolecules · 2022Article
- Modification of phosphoinositides by theFrontiers in cellular and infection microbiology · 2022Review
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Authors and funding
4 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
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).
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