A Machine Learning Approach for the Prediction of Thermostable β-Glucosidases
Thermostable β-glucosidases (E.C. 3.2.1.21) are essential enzymes used in second-generation biofuel production. However, little is known about the structural characteristics that lead to their thermostability. In this study, I used graph-based structural signatures to represent three-dimensional str...
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MDPI AG
2025-04-01
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| author | Diego Mariano |
| author_facet | Diego Mariano |
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| description | Thermostable β-glucosidases (E.C. 3.2.1.21) are essential enzymes used in second-generation biofuel production. However, little is known about the structural characteristics that lead to their thermostability. In this study, I used graph-based structural signatures to represent three-dimensional structures of β-glucosidase enzymes extracted from thermophilic organisms. I collected 1717 structures from thermophilic (<i>n</i> = 890) and non-thermophilic (<i>n</i> = 827) organisms and divided them into two datasets: training (<i>n</i> = 1134) and test (<i>n</i> = 583). I then used seven machine learning algorithms to classify them. The best model achieved 77.1% accuracy using logistic regression in training with 10-fold cross-validation and 81.6% accuracy in testing using the CatBoost algorithm. I hypothesize that the signature model proposed here can help understand the structural patterns in thermostable enzymes and shed light on the design of more efficient enzymes for biofuel production. |
| format | Article |
| id | doaj-art-9e0e8267f4794cdba01705f51325e01b |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-9e0e8267f4794cdba01705f51325e01b2025-08-20T03:52:57ZengMDPI AGApplied Sciences2076-34172025-04-01159483910.3390/app15094839A Machine Learning Approach for the Prediction of Thermostable β-GlucosidasesDiego Mariano0Department of Computer Science (DCC), Universidade Federal de Minas Gerais (UFMG), Belo Horizonte 31270-901, BrazilThermostable β-glucosidases (E.C. 3.2.1.21) are essential enzymes used in second-generation biofuel production. However, little is known about the structural characteristics that lead to their thermostability. In this study, I used graph-based structural signatures to represent three-dimensional structures of β-glucosidase enzymes extracted from thermophilic organisms. I collected 1717 structures from thermophilic (<i>n</i> = 890) and non-thermophilic (<i>n</i> = 827) organisms and divided them into two datasets: training (<i>n</i> = 1134) and test (<i>n</i> = 583). I then used seven machine learning algorithms to classify them. The best model achieved 77.1% accuracy using logistic regression in training with 10-fold cross-validation and 81.6% accuracy in testing using the CatBoost algorithm. I hypothesize that the signature model proposed here can help understand the structural patterns in thermostable enzymes and shed light on the design of more efficient enzymes for biofuel production.https://www.mdpi.com/2076-3417/15/9/4839β-glucosidasesmachine learninggraph-based structural signatures |
| spellingShingle | Diego Mariano A Machine Learning Approach for the Prediction of Thermostable β-Glucosidases Applied Sciences β-glucosidases machine learning graph-based structural signatures |
| title | A Machine Learning Approach for the Prediction of Thermostable β-Glucosidases |
| title_full | A Machine Learning Approach for the Prediction of Thermostable β-Glucosidases |
| title_fullStr | A Machine Learning Approach for the Prediction of Thermostable β-Glucosidases |
| title_full_unstemmed | A Machine Learning Approach for the Prediction of Thermostable β-Glucosidases |
| title_short | A Machine Learning Approach for the Prediction of Thermostable β-Glucosidases |
| title_sort | machine learning approach for the prediction of thermostable β glucosidases |
| topic | β-glucosidases machine learning graph-based structural signatures |
| url | https://www.mdpi.com/2076-3417/15/9/4839 |
| work_keys_str_mv | AT diegomariano amachinelearningapproachforthepredictionofthermostablebglucosidases AT diegomariano machinelearningapproachforthepredictionofthermostablebglucosidases |