DeepSIRT: A deep neural network for identification of sirtuin targets and their subcellular localizations. (August 2021)

• Record Type: Journal Article
• Authors: Shah, Syed Muazzam Ali; Taju, Semmy Wellem; Dlamini, Bongani Brian; Ou, Yu-Yen
• Is Part Of: Computational biology and chemistry. Volume 93(2021)
• Publication Date: 2021-08
• Subjects: Deep neural network -- Convolutional neural network -- Sirtuin proteins -- Subcellular localization -- Aging/longevity; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2021.107514
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Sirtuin proteins are involved in diversified biological processes. Mammalian sirtuin protein family comprises seven members. Most sirtuin proteins are characterized for their role in lifespan extension. They are vital energy sensors and regulators for many cellular processes. DeepSIRT assist biologists to identify age and cancer-related sirtuin proteins. Abstract: Sirtuins are a family of proteins that play a key role in regulating a wide range of cellular processes including DNA regulation, metabolism, aging/longevity, cell survival, apoptosis, and stress resistance. Sirtuins are protein deacetylases and include in the class III family of histone deacetylase enzymes (HDACs). The class III HDACs contains seven members of the sirtuin family from SIRT1 to SIRT7. The seven members of the sirtuin family have various substrates and are present in nearly all subcellular localizations including the nucleus, cytoplasm, and mitochondria. In this study, a deep neural network approach using one-dimensional Convolutional Neural Networks (CNN) was proposed to build a prediction model that can accurately identify the outcome of the sirtuin protein by targeting their subcellular localizations. Therefore, the function and localization of sirtuin targets were analyzed and annotated to compartmentalize into distinct subcellular localizations. We further reduced the sequence similarity between protein sequences and three feature extraction methods were applied in datasets. Finally, the proposed method has been tested and compared with various machine-learning algorithms. The proposed method is validated on two independent datasets and showed an average of up to 85.77 % sensitivity, 97.32 % specificity, and 0.82 MCC for seven members of the sirtuin family of proteins.