Prediction of Nitrotyrosine Sites Based On Composition and Position Based Features.

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dc.contributor.authorAhmad Waseem
dc.date.accessioned2018-11-26T05:44:50Z
dc.date.available2018-11-26T05:44:50Z
dc.date.issued2018
dc.descriptionSupervised by: Dr. Yaseer Daniyaal Khanen_US
dc.description.abstractClosely 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.en_US
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/3447
dc.language.isoenen_US
dc.publisherUniversity of Management and Technologyen_US
dc.subjectModifications occur accurately, Potential tyrosine residuesen_US
dc.subjectTyrosine nitration sites, Verification and validation testsen_US
dc.titlePrediction of Nitrotyrosine Sites Based On Composition and Position Based Features.en_US
dc.titlePrediction of nitrotyrosine sites based on composition and position based features.en_us
dc.typeThesisen_US
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