A Treatise to Computational Approaches Towards Prediction of Membrane Protein and Its Subtypes

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• 作者：Ahmad Hassan Butt ; Nouman Rasool ; Yaser Daanial Khan
• 关键词：Amino acids ; Membrane proteins ; Support vector machine ; Probabilistic neural networks ; Pseudo amino acids compositions ; Position ; specific scoring matrix
• 刊名：The Journal of Membrane Biology
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：250
• 期：1
• 页码：55-76
• 全文大小：
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Biochemistry, general; Human Physiology;
• 出版者：Springer US
• ISSN：1432-1424
• 卷排序：250

文摘

Membrane proteins are vital mediating molecules responsible for the interaction of a cell with its surroundings. These proteins are involved in different functionalities such as ferrying of molecules and nutrients across membrane, recognizing foreign bodies, receiving outside signals and translating them into the cell. Membrane proteins play significant role in drug interaction as nearly 50% of the drug targets are membrane proteins. Due to the momentous role of membrane protein in cell activity, computational models able to predict membrane protein with accurate measures bears indispensable importance. The conventional experimental methods used for annotating membrane proteins are time-consuming and costly and in some cases impossible. Computationally intelligent techniques have emerged to be as a useful resource in the automation of prediction and hence the annotation process. In this study, various techniques have been reviewed that are based on different computational intelligence models used for prediction process. These techniques were formulated by different researchers and were further evaluated to provide a comparative analysis. Analysis shows that the usage of support vector machine-based prediction techniques bears more assiduous results.