Machine learning techniques for differentiating psychrophilic and non-psychrophilic bacterial α/β hydrolase enzymes
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Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Science and Business Media Deutschland GmbH
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Psychrophilic enzymes represent a category of macromolecules that have acquired specific properties that enable these enzymes to perform their catalytic activity at low temperatures with high efficiency. One of the factors contributing to their adaptation is increased active site flexibility. Psychrophilic enzymes are of significant industrial interest due to their applications in food production, environmental remediation, pharmaceuticals, textiles, and detergents. Despite growing interest, the molecular mechanisms underlying the adaptation of psychrophilic enzymes to low temperatures remain largely unexplored. This study aims to investigate the differences between psychrophilic and non-psychrophilic bacterial α/β hydrolase enzymes. 464 psychrophilic and 562 non-psychrophilic α/β hydrolase enzymes were retrieved from the UniProt database. Further classification of these enzymes based on amino acid composition was performed using a set of machine learning algorithms such as Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). Ten variables, including the contents of Ala, Gly, Ser, Thr, charged, aliphatic, aromatic, and hydrophobic amino acids, as well as the aliphatic index and the grand average of hydropathy (GRAVY), were analyzed. The Random Forest algorithm achieved the highest classification rate with an accuracy of 77%. Further analyses showed that the amino acid threonine and serine played the most important role in determining psychrophilic traits. This suggests that these amino acids play a significant role in enhancing the enzyme's hydrogen-bonding capacity, thereby contributing to its structural flexibility and stability under cold conditions. This study confirms that some amino acids, especially serine and threonine, are generally involved in the cold adaptation of psychrophilic α/β hydrolase enzymes and may provide an interesting platform from a biotechnological point of view.
Açıklama
Anahtar Kelimeler
Machine Learning, Psychrophilic, Random Forest, α/β Hydrolase
Kaynak
Biologia
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
80
Sayı
6
Künye
Vardar-Yel, N. (2025). Machine learning techniques for differentiating psychrophilic and non-psychrophilic bacterial α/β hydrolase enzymes. Biologia, 80(6), 1557-1564. 10.1007/s11756-025-01920-9
