Prediction of Nitrotyrosine Sites Based On Composition and Position Based Features.
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Date
2018
Authors
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Publisher
University of Management and Technology
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.
Description
Supervised by: Dr. Yaseer Daniyaal Khan
Keywords
Modifications occur accurately, Potential tyrosine residues, Tyrosine nitration sites, Verification and validation tests