afpCOOL: A tool for antifreeze protein prediction

Heliyon

Javad Zahiri¹  Reza H. Sajedi²  Zeinab Takalloo²  Morteza Eslami³  Ramin Shirali Hossein Zade⁴  Ghasem Mahdevar⁵  Abbasali Emamjomeh⁶ 

[1] Bioinformatics and Computational Omics Lab (BioCOOL), Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran;Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran;Department of Computer Engineering, Arak University, Arak, Iran;Department of Computer Engineering, Sharif University of Technology, Tehran, Iran;Department of Mathematics, Faculty of Sciences, University of Isfahan, Isfahan, Iran;Laboratory of Computational Biotechnology and Bioinformatics (CBB), Department of Plant Breeding and Biotechnology (PBB), Faculty of Agriculture, University of Zabol, Zabol, Iran;

关键词: Computer science;    Bioinformatics;    Computational biology;    Mathematical biosciences;    Biochemistry;   

DOI  :  

来源: DOAJ

【 摘 要 】

Various cold-adapted organisms produce antifreeze proteins (AFPs), which prevent the freezing of cell fluids by inhibiting the growth of ice crystals. AFPs are currently being recognized in various organisms, living in extremely low temperatures. AFPs have several important applications in increasing freeze tolerance of plants, maintaining the tissue in frozen conditions and producing cold-hardy plants by applying transgenic technology. Substantial differences in the sequence and structure of the AFPs, pose a challenge for researchers to identify these proteins. In this paper, we proposed a novel method to identify AFPs, using supportive vector machine (SVM) by incorporating 4 types of features. Results of the two used benchmark datasets, revealed the strength of the proposed method in AFP prediction. According to the results of an independent test setup, our method outperformed the current state-of-the-art methods. In addition, the comparison results of the discrimination power of different feature types revealed that physicochemical descriptors are the most contributing features in AFP detection. This method has been implemented as a stand-alone tool, named afpCOOL, for various operating systems to predict AFPs with a user friendly graphical interface.

【 授权许可】

Unknown