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Samman, Nour, Mohabatkar, Hassan, Rabiei, Parisa. (1402). Using several pseudo amino acid composition types and different machine learning algorithms to classify and predict archaeal phospholipases. , 12(3), 117-126. doi: 10.22099/mbrc.2023.47756.1845
Nour Samman; Hassan Mohabatkar; Parisa Rabiei. "Using several pseudo amino acid composition types and different machine learning algorithms to classify and predict archaeal phospholipases". , 12, 3, 1402, 117-126. doi: 10.22099/mbrc.2023.47756.1845
Samman, Nour, Mohabatkar, Hassan, Rabiei, Parisa. (1402). 'Using several pseudo amino acid composition types and different machine learning algorithms to classify and predict archaeal phospholipases', , 12(3), pp. 117-126. doi: 10.22099/mbrc.2023.47756.1845
Samman, Nour, Mohabatkar, Hassan, Rabiei, Parisa. Using several pseudo amino acid composition types and different machine learning algorithms to classify and predict archaeal phospholipases. , 1402; 12(3): 117-126. doi: 10.22099/mbrc.2023.47756.1845

Using several pseudo amino acid composition types and different machine learning algorithms to classify and predict archaeal phospholipases

Molecular Biology Research Communications
دوره 12، شماره 3، 2023، صفحه 117-126    اصل مقاله (200.8 K)
نوع مقاله: Original article
شناسه دیجیتال (DOI): 10.22099/mbrc.2023.47756.1845
نویسندگان
Nour Samman؛ Hassan Mohabatkar* ؛ Parisa Rabiei
Department of Biotechnology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
چکیده
Phospholipases, as important lipolytic enzymes, have diverse industrial applications. Regarding the stability of extremophilic archaea’s proteins in harsh conditions, analyses of unusual features of their proteins are significantly important for their utilization. This research was accomplished to in silico study of archaeal phospholipases’ properties and to develop a pioneering method for distinguishing these enzymes from other archaeal enzymes via machine learning algorithms and Chou’s pseudo-amino acid composition concept. The non-redundant sequences of archaeal phospholipases were collected. BioSeq-Analysis sever was used with Support Vector Machine (SVM), Random Forests (RF), Covariance Discrimination (CD), and Optimized Evidence-Theoretic K-nearest Neighbor (OET-KNN) as powerful machine learnings algorithms. Also, different Chou’s pseudo-amino acid composition modes were performed and then, 5-fold cross-validation was applied to the sequences. Based on our results, the OET-KNN predictor, with 96% accuracy, yields the best performance in SC-PseAAC mode by 5-fold cross-validation. This predictor also achieved very high values of specificity (95%), sensitivity (96%), Matthews’s correlation coefficient (0.92), and accuracy (96%). The present investigation yielded a robust anticipatory model for the archaeal phospholipase prediction utilizing the tenets PseAAC and OET-KNN machine learning algorithm.
کلیدواژه‌ها
Archaea؛ Phospholipases؛ Machine learning؛ Chou’s PseAAC
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