ArticleInternational journal of molecular sciences2023
DeepTP: A Deep Learning Model for Thermophilic Protein Prediction.
Article in International journal of molecular sciences, 2023. 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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Who cites it
14 citing papers in PubMed.
- PepLM-GNN: A graph neural network framework leveraging pre-trained language models for peptide-protein binding prediction.PLoS computational biology · 2026Article
- ESM-PsyPred: Leveraging Protein Language Models for Accurate Prediction of Psychrophilic Proteins.Interdisciplinary sciences, computational life sciences · 2026Article
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- Review
- Prediction and design of thermostable proteins with a desired melting temperature.Scientific reports · 2025Article
- ProCeSa: Contrast-Enhanced Structure-Aware Network for Thermostability Prediction with Protein Language Models.Journal of chemical information and modeling · 2025Article
- Accurately predicting optimal conditions for microorganism proteins through geometric graph learning and language model.Communications biology · 2024Article
- TPGPred: A Mixed-Feature-Driven Approach for Identifying Thermophilic Proteins Based on GradientBoosting.International journal of molecular sciences · 2024Article
- Prediction of leukemia peptides using convolutional neural network and protein compositions.BMC cancer · 2024Article
- TemStaPro: protein thermostability prediction using sequence representations from protein language models.Bioinformatics (Oxford, England) · 2024Article
- Superior protein thermophilicity prediction with protein language model embeddings.NAR genomics and bioinformatics · 2023Article
- Homologous Pairs of Low and High Temperature Originating Proteins Spanning the Known Prokaryotic Universe.Scientific data · 2023Article
- Improving the Thermostability of Serine Protease PB92 fromFoods (Basel, Switzerland) · 2023Article
- DeepSTABp: A Deep Learning Approach for the Prediction of Thermal Protein Stability.International journal of molecular sciences · 2023Article
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3 authors.
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Abstract
Thermophilic proteins have important value in the fields of biopharmaceuticals and enzyme engineering. Most existing thermophilic protein prediction models are based on traditional machine learning algorithms and do not fully utilize protein sequence information. To solve this problem, a deep learning model based on self-attention and multiple-channel feature fusion was proposed to predict thermophilic proteins, called DeepTP. First, a large new dataset consisting of 20,842 proteins was constructed. Second, a convolutional neural network and bidirectional long short-term memory network were used to extract the hidden features in protein sequences. Different weights were then assigned to features through self-attention, and finally, biological features were integrated to build a prediction model. In a performance comparison with existing methods, DeepTP had better performance and scalability in an independent balanced test set and validation set, with AUC values of 0.944 and 0.801, respectively. In the unbalanced test set, DeepTP had an average precision (AP) of 0.536. The tool is freely available.
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