ArticleFrontiers in microbiology2022
iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy.
Article in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- ESM-PsyPred: Leveraging Protein Language Models for Accurate Prediction of Psychrophilic Proteins.Interdisciplinary sciences, computational life sciences · 2026Article
- An artificial intelligence-based approach for identifying the proteins regulating liquid-liquid phase separation.Briefings in bioinformatics · 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
- Guiding questions to avoid data leakage in biological machine learning applications.Nature methods · 2024Review
- TemStaPro: protein thermostability prediction using sequence representations from protein language models.Bioinformatics (Oxford, England) · 2024Article
- TemBERTure: advancing protein thermostability prediction with deep learning and attention mechanisms.Bioinformatics advances · 2024Article
- Superior protein thermophilicity prediction with protein language model embeddings.NAR genomics and bioinformatics · 2023Article
- A First Computational Frame for Recognizing Heparin-Binding Protein.Diagnostics (Basel, Switzerland) · 2023Article
- Data-driven strategies for the computational design of enzyme thermal stability: trends, perspectives, and prospects.Acta biochimica et biophysica Sinica · 2023Review
- DeepTP: A Deep Learning Model for Thermophilic Protein Prediction.International journal of molecular sciences · 2023Article
- Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods.Computational and structural biotechnology journal · 2023Review
- Discrimination of psychrophilic enzymes using machine learning algorithms with amino acid composition descriptor.Frontiers in microbiology · 2023Article
- A Statistical Analysis of the Sequence and Structure of Thermophilic and Non-Thermophilic Proteins.International journal of molecular sciences · 2022Article
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Authors and funding
8 authors.
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Abstract
Thermophilic proteins have important application value in biotechnology and industrial processes. The correct identification of thermophilic proteins provides important information for the application of these proteins in engineering. The identification method of thermophilic proteins based on biochemistry is laborious, time-consuming, and high cost. Therefore, there is an urgent need for a fast and accurate method to identify thermophilic proteins. Considering this urgency, we constructed a reliable benchmark dataset containing 1,368 thermophilic and 1,443 non-thermophilic proteins. A multi-layer perceptron (MLP) model based on a multi-feature fusion strategy was proposed to discriminate thermophilic proteins from non-thermophilic proteins. On independent data set, the proposed model could achieve an accuracy of 96.26%, which demonstrates that the model has a good application prospect. In order to use the model conveniently, a user-friendly software package called iThermo was established and can be freely accessed at http://lin-group.cn/server/iThermo/index.html. The high accuracy of the model and the practicability of the developed software package indicate that this study can accelerate the discovery and engineering application of thermally stable proteins.
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