Structural classification of proteins using texture descriptors extracted from the cellular automata image

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• 作者：Hamidreza Kavianpour ; Mahdi Vasighi
• 关键词：Protein sequence classification ; Amino acid digital coding ; Cellular automata ; Texture features
• 刊名：Amino Acids
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：49
• 期：2
• 页码：261-271
• 全文大小：
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Biochemistry, general; Analytical Chemistry; Biochemical Engineering; Life Sciences, general; Proteomics; Neurobiology;
• 出版者：Springer Vienna
• ISSN：1438-2199
• 卷排序：49

文摘

Nowadays, having knowledge about cellular attributes of proteins has an important role in pharmacy, medical science and molecular biology. These attributes are closely correlated with the function and three-dimensional structure of proteins. Knowledge of protein structural class is used by various methods for better understanding the protein functionality and folding patterns. Computational methods and intelligence systems can have an important role in performing structural classification of proteins. Most of protein sequences are saved in databanks as characters and strings and a numerical representation is essential for applying machine learning methods. In this work, a binary representation of protein sequences is introduced based on reduced amino acids alphabets according to surrounding hydrophobicity index. Many important features which are hidden in these long binary sequences can be clearly displayed through their cellular automata images. The extracted features from these images are used to build a classification model by support vector machine. Comparing to previous studies on the several benchmark datasets, the promising classification rates obtained by tenfold cross-validation imply that the current approach can help in revealing some inherent features deeply hidden in protein sequences and improve the quality of predicting protein structural class.