Ahmad Waseem2018-11-262018-11-262018https://escholar.umt.edu.pk/handle/123456789/3447Supervised by: Dr. Yaseer Daniyaal KhanClosely 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 posttranslational modification in proteins. Inside a cell, such modifications occur accurately by the action of sophisticated cellular machinery. This task is accomplished by specific enzymes present in endoplasmic reticulum. The identification of potential tyrosine residues in a protein primary sequence which can be nitrated is a challenging task. To counter the prevailing, laborious and time-consuming experimental approaches, here we introduce a novel computational model. Based on experimentally verified tyrosine nitration sites, they are transformed to their feature vectors. An adaptive training algorithm is then 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 which led to a promising accuracy of 88%, a sensitivity of 85% and a specificity of 89.18% and Mathew correlation coefficient of 0.627. We believe that this computational model may provide foundation for further investigation and can be used deal with the other PTM sites in proteins.enModifications occur accurately, Potential tyrosine residuesTyrosine nitration sites, Verification and validation testsThesis