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Prediction of Nitrosocysteine Sites Using Position and Composition Variant Features

[ Vol. 16 , Issue. 4 ]

Author(s):

Yaser Daanial Khan, Aroosa Batool, Nouman Rasool, Sher Afzal Khan* and Kuo-Chen Chou   Pages 283 - 293 ( 11 )

Abstract:


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.

Affiliation:

Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Department of Life Sciences, School of Science, University of Management and Technology, Lahore, Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Jeddah, 21577, Gordon Life Science Institute, Boston, MA 02478

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