Analysis of the Sequence Characteristics of Antifreeze Protein

Life

Tao Zeng¹  Tao Huang¹  Hao Li²  Zhandong Li²  Lei Chen³  Lin Lu⁴  Yu-Dong Cai⁵  Yu-Hang Zhang⁵ 

[1] Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China;College of Food Engineering, Jilin Engineering Normal University, Changchun 130052, China;College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China;Department of Radiology, Columbia University Medical Center, New York, NY 10032, USA;School of Life Sciences, Shanghai University, Shanghai 200444, China;

关键词: antifreeze protein;    protein domain;    minimum redundancy maximum relevance;    random forest;   

DOI  :  10.3390/life11060520

来源: DOAJ

【 摘 要 】

Antifreeze protein (AFP) is a proteinaceous compound with improved antifreeze ability and binding ability to ice to prevent its growth. As a surface-active material, a small number of AFPs have a tremendous influence on the growth of ice. Therefore, identifying novel AFPs is important to understand protein–ice interactions and create novel ice-binding domains. To date, predicting AFPs is difficult due to their low sequence similarity for the ice-binding domain and the lack of common features among different AFPs. Here, a computational engine was developed to predict the features of AFPs and reveal the most important 39 features for AFP identification, such as antifreeze-like/N-acetylneuraminic acid synthase C-terminal, insect AFP motif, C-type lectin-like, and EGF-like domain. With this newly presented computational method, a group of previously confirmed functional AFP motifs was screened out. This study has identified some potential new AFP motifs and contributes to understanding biological antifreeze mechanisms.

【 授权许可】

Unknown