Abstract

Background: Closely related to causes of various diseases such as rheumatoid arthritis, septic shock, and coeliac disease; tyrosine nitration is considered as one of the most important post-translational modification in proteins. Inside a cell, protein modifications occur accurately by the action of sophisticated cellular machinery. Specific enzymes present in endoplasmic reticulum accomplish this task. The identification of potential tyrosine residues in a protein primary sequence, which can be nitrated, is a challenging task.

Methods: To counter the prevailing, laborious and time-consuming experimental approaches, a novel computational model is introduced in the present study. Based on data collected from experimentally verified tyrosine nitration sites feature vectors are formed. Later, an adaptive training algorithm is used to train a back propagation neural network for prediction purposes. To objectively measure the accuracy of the proposed model, rigorous verification and validation tests are carried out.

Results: Through verification and validation, a promising accuracy of 88%, a sensitivity of 85%, a specificity of 89.18% and Mathew’s Correlation Coefficient of 0.627 is achieved.

Conclusion: It is concluded that the proposed computational model provides the foundation for further investigation and be used for the identification of nitrotyrosine sites in proteins.

Keywords: Nitrotyrosine, PseAAC, statistical moments, 5-step rule, prediction, rheumatoid arthritis.