ArticleFrontiers in bioengineering and biotechnology2020
TMPSS: A Deep Learning-Based Predictor for Secondary Structure and Topology Structure Prediction of Alpha-Helical Transmembrane Proteins.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 26 citations in OpenAlex.
- Advanced electrochemical biosensing exploiting mussel-inspired polydopamine interfaces.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2026Review
- Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences.Methods in molecular biology (Clifton, N.J.) · 2025Review
- A Survey of Current Status in AI-Based Topology Prediction of Transmembrane Proteins.Methods in molecular biology (Clifton, N.J.) · 2025Article
- REMEMProt: a resource of membrane-enriched proteome profiles, their disease associations, and biomarker status.Life science alliance · 2024Article
- Computational drug development for membrane protein targets.Nature biotechnology · 2024Review
- Phospholipid transport to the bacterial outer membrane through an envelope-spanning bridge.bioRxiv : the preprint server for biology · 2023Article
- Lipophilic Anchors that Embed Bioconjugates in Bilayer Membranes: A Review.Bioconjugate chemistry · 2023Review
- Article
- Secondary and Topological Structural Merge Prediction of Alpha-Helical Transmembrane Proteins Using a Hybrid Model Based on Hidden Markov and Long Short-Term Memory Neural Networks.International journal of molecular sciences · 2023Article
- Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction.Neural computing & applications · 2023Article
- Evaluation of the Effectiveness of Derived Features of AlphaFold2 on Single-Sequence Protein Binding Site Prediction.Biology · 2022Article
- Deep learning for protein secondary structure prediction: Pre and post-AlphaFold.Computational and structural biotechnology journal · 2022Review
- Insight into maize gene expression profiles responses to symbiotic bacteria derived from Helicoverpa armigera and Ostrinia furnacalis.Archives of microbiology · 2021Article
- A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins.Journal of proteome research · 2021Article
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Authors and funding
6 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Alpha transmembrane proteins (αTMPs) profoundly affect many critical biological processes and are major drug targets due to their pivotal protein functions. At present, even though the non-transmembrane secondary structures are highly relevant to the biological functions of αTMPs along with their transmembrane structures, they have not been unified to be studied yet. In this study, we present a novel computational method, TMPSS, to predict the secondary structures in non-transmembrane parts and the topology structures in transmembrane parts of αTMPs. TMPSS applied a Convolutional Neural Network (CNN), combined with an attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) network, to extract the local contexts and long-distance interdependencies from primary sequences. In addition, a multi-task learning strategy was used to predict the secondary structures and the transmembrane helixes. TMPSS was thoroughly trained and tested against a non-redundant independent dataset, where the Q3 secondary structure prediction accuracy achieved 78% in the non-transmembrane region, and the accuracy of the transmembrane region prediction achieved 90%. In sum, our method showcased a unified model for predicting the secondary structure and topology structure of αTMPs by only utilizing features generated from primary sequences and provided a steady and fast prediction, which promisingly improves the structural studies on αTMPs.
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