S-nitrosylation is one of the most prominent posttranslational modification among proteins.
It involves the addition of nitrogen oxide group to cysteine thiols forming S-nitrosocysteine. Evidence
suggests that S-nitrosylation plays a foremost role in numerous human diseases and disorders. The incorporation
of techniques for robust identification of S-nitrosylated proteins is highly anticipated in biological
research and drug discovery. The proposed system endeavors a novel strategy based on a statistical
and computational intelligent methods for the identification of S-nitrosocystiene sites within a
given primary protein sequence. For this purpose, 5-step rule was approached comprising of benchmark
dataset creation, mathematical modelling, prediction, evaluation and web-server development.
For position relative feature extraction, statistical moments were used and a multilayer neural network
was trained adapting Gradient Descent and Adaptive Learning algorithms. The results were comparatively
analyzed with existing techniques using benchmark datasets. It is inferred through conclusive
experimentation that the proposed scheme is very propitious, accurate and exceptionally effective for
the prediction of S-nitrosocystiene in protein sequences.
Keywords: Nitrosocystiene, prediction model, neural network, statistical moments, 5-step rule, ribosome.
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