Prediction of nitrotyrosine sites based on composition and position based features
| dc.contributor.author | Ahmad Waseem | |
| dc.date.accessioned | 2025-12-03T14:43:04Z | |
| dc.date.available | 2025-12-03T14:43:04Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | 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 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. | |
| dc.identifier.uri | https://escholar.umt.edu.pk/handle/123456789/14912 | |
| dc.language.iso | en | |
| dc.publisher | UMT.Lahore | |
| dc.title | Prediction of nitrotyrosine sites based on composition and position based features | |
| dc.type | Thesis |