ArticleFrontiers in bioengineering and biotechnology2020
A Method for Prediction of Thermophilic Protein Based on Reduced Amino Acids and Mixed Features.
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 12 papers.
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12 citing papers in PubMed.
- Prediction and design of thermostable proteins with a desired melting temperature.Scientific reports · 2025Article
- Article
- TPGPred: A Mixed-Feature-Driven Approach for Identifying Thermophilic Proteins Based on GradientBoosting.International journal of molecular sciences · 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
- Thermo-L-Asparaginases: From the Role in the Viability of Thermophiles and Hyperthermophiles at High Temperatures to a Molecular Understanding of Their Thermoactivity and Thermostability.International journal of molecular sciences · 2023Review
- Discrimination of psychrophilic enzymes using machine learning algorithms with amino acid composition descriptor.Frontiers in microbiology · 2023Article
- NMR Structure and Biophysical Characterization of Thermophilic Single-Stranded DNA Binding Protein fromInternational journal of molecular sciences · 2022Article
- Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins.EXCLI journal · 2022Review
- iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy.Frontiers in microbiology · 2022Article
- A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides.Scientific reports · 2021Article
- Immunoglobulin Classification Based on FC* and GC* Features.Frontiers in genetics · 2021Article
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6 authors.
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Abstract
The thermostability of proteins is a key factor considered during enzyme engineering, and finding a method that can identify thermophilic and non-thermophilic proteins will be helpful for enzyme design. In this study, we established a novel method combining mixed features and machine learning to achieve this recognition task. In this method, an amino acid reduction scheme was adopted to recode the amino acid sequence. Then, the physicochemical characteristics, auto-cross covariance (ACC), and reduced dipeptides were calculated and integrated to form a mixed feature set, which was processed using correlation analysis, feature selection, and principal component analysis (PCA) to remove redundant information. Finally, four machine learning methods and a dataset containing 500 random observations out of 915 thermophilic proteins and 500 random samples out of 793 non-thermophilic proteins were used to train and predict the data. The experimental results showed that 98.2% of thermophilic and non-thermophilic proteins were correctly identified using 10-fold cross-validation. Moreover, our analysis of the final reserved features and removed features yielded information about the crucial, unimportant and insensitive elements, it also provided essential information for enzyme design.
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