Machine learning techniques for differentiating psychrophilic and non-psychrophilic bacterial α/β hydrolase enzymes

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dc.contributor.authorVardar-Yel, Nurcan
dc.date.accessioned2025-07-07T14:08:03Z
dc.date.available2025-07-07T14:08:03Z
dc.date.issued2025
dc.departmentMeslek Yüksekokulları, Sağlık Hizmetleri Meslek Yüksekokulu, Tıbbi Laboratuvar Teknikleri Programı
dc.description.abstractPsychrophilic 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.
dc.identifier.citationVardar-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
dc.identifier.doi10.1007/s11756-025-01920-9
dc.identifier.endpage1564
dc.identifier.issn0006-3088
dc.identifier.issue6
dc.identifier.scopusqualityQ2
dc.identifier.startpage1557
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5795
dc.identifier.volume80
dc.identifier.wosWOS:001461145900001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakScopus
dc.institutionauthorVardar-Yel, Nurcan
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofBiologia
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectMachine Learning
dc.subjectPsychrophilic
dc.subjectRandom Forest
dc.subjectα/β Hydrolase
dc.titleMachine learning techniques for differentiating psychrophilic and non-psychrophilic bacterial α/β hydrolase enzymes
dc.typeArticle

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