Abstract
In biological systems, Nitration is a crucial post-translational modification which occurs on various amino acids. Nitration of Tyrosine is regarded as nitorsative stress biomarker resulting in the formation of peroxynitrite and other reactive and harmful nitrogen species. NitroTyrosine is closely related to Carcinogenesis, tumor growth progression and other major pathological conditions including systemic autoimmune diseases, inflammation, neurodegeneration and cardiovascular disorders. Additionally, the alteration in Nitrotyrosine profile occurs well before appearance of any symptoms of aforementioned diseases making nitrotyrosine a biomarker and potential target for early prognosis of aforementioned diseases. The wet lab identification of potential nitrotyrosine sites is laborious, time-taking and costly due to challenges of in vitro, ex vivo and in vivo identification processes. To supplement wet lab identification of nitrotyrosine, we proposed, implemented and evaluated a different approach to develop tyrosine nitration site predictors using pseudo amino acid compositions (PseAAC) and deep neural networks (DNNs). Proposed approach does not require any feature extraction and uses DNNs for learning a feature representation of peptide sequences and classification thereof. Validation of proposed approach is done using well-known model evaluation measures. Among different deep neural networks, convolutional neural network-based predictor achieved best scores on independent dataset with accuracy of 87.2%, matthew’s correlation coefficient score of 0.74 and AuC score of 0.91 which outperforms the previous reported scores of Nitrotyrosine predictors.
Highlights
Cells are constantly exposed to diverse stressors under physiological conditions, which leads to dynamic changes in cellular functions
By combining the Pseudo Amino Acid Composition (PseAAC) [42] with deep neural networks, we propose an improved predictor for identifying NitroY sites in proteins, which does not require feature extraction and removes the need for human intervention and domain expertise
The nearest contender is FCN which lags behind the convolutional neural network (CNN) from point (0.9, y) to (0.3, y) in precision-recall space but surpasses the CNN model from (0.3, y) to (0,y)
Summary
Cells are constantly exposed to diverse stressors under physiological conditions, which leads to dynamic changes in cellular functions. Several mechanisms are used by cells to respond to these dynamic changes including regulation of energy producing pathways, alterations of epigenetic marks, modulation of metabolic enzymes activities using metabolites and protein post translational modifications (PTMs) [1] One such PTM is 3-nitrotyrosine (NitroY) which is formed. By the substitution of a hydrogen atom with nitro group (-NO2) in any of the two carbon atoms of the phenolic ring of the amino acid Tyrosine [2] This process is called nitration of Tyrosine and several research publications have extensively discussed biological importance of nitration mechanisms under different conditions and pathophysiological relevance thereof in pathological settings from acute to chronic diseases [3]–[5]. Increased levels of NitroY have been reported in numerous pathological
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