A Brief Review of the Computational Identification of Antifreeze Protein

Author(s): Fang Wang, Zheng-Xing Guan, Fu-Ying Dao, Hui Ding*.

Journal Name: Current Organic Chemistry

Volume 23 , Issue 15 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


Lots of cold-adapted organisms could produce antifreeze proteins (AFPs) to counter the freezing of cell fluids by controlling the growth of ice crystal. AFPs have been found in various species such as in vertebrates, invertebrates, plants, bacteria, and fungi. These AFPs from fish, insects and plants displayed a high diversity. Thus, the identification of the AFPs is a challenging task in computational proteomics. With the accumulation of AFPs and development of machine meaning methods, it is possible to construct a high-throughput tool to timely identify the AFPs. In this review, we briefly reviewed the application of machine learning methods in antifreeze proteins identification from difference section, including published benchmark dataset, sequence descriptor, classification algorithms and published methods. We hope that this review will produce new ideas and directions for the researches in identifying antifreeze proteins.

Keywords: Antifreeze protein, classification, machine learning, computational proteomics, cold-adapted organisms, cell fluids.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2019
Page: [1671 - 1680]
Pages: 10
DOI: 10.2174/1385272823666190718145613
Price: $58

Article Metrics

PDF: 10