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.