Publication: CryoProtect: A Web Server for Classifying Antifreeze Proteins from Nonantifreeze Proteins
Issued Date
2017-01-01
Resource Type
ISSN
20909071
20909063
20909063
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2-s2.0-85013322071
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Mahidol University
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SCOPUS
Bibliographic Citation
Journal of Chemistry. Vol.2017, (2017)
Suggested Citation
Reny Pratiwi, Aijaz Ahmad Malik, Nalini Schaduangrat, Virapong Prachayasittikul, Jarl E.S. Wikberg, Chanin Nantasenamat, Watshara Shoombuatong CryoProtect: A Web Server for Classifying Antifreeze Proteins from Nonantifreeze Proteins. Journal of Chemistry. Vol.2017, (2017). doi:10.1155/2017/9861752 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/42276
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Title
CryoProtect: A Web Server for Classifying Antifreeze Proteins from Nonantifreeze Proteins
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Abstract
© 2017 Reny Pratiwi et al. Antifreeze protein (AFP) is an ice-binding protein that protects organisms from freezing in extremely cold environments. AFPs are found across a diverse range of species and, therefore, significantly differ in their structures. As there are no consensus sequences available for determining the ice-binding domain of AFPs, thus the prediction and characterization of AFPs from their sequence is a challenging task. This study addresses this issue by predicting AFPs directly from sequence on a large set of 478 AFPs and 9,139 non-AFPs using machine learning (e.g., random forest) as a function of interpretable features (e.g., amino acid composition, dipeptide composition, and physicochemical properties). Furthermore, AFPs were characterized using propensity scores and important physicochemical properties via statistical and principal component analysis. The predictive model afforded high performance with an accuracy of 88.28% and results revealed that AFPs are likely to be composed of hydrophobic amino acids as well as amino acids with hydroxyl and sulfhydryl side chains. The predictive model is provided as a free publicly available web server called CryoProtect for classifying query protein sequence as being either AFP or non-AFP. The data set and source code are for reproducing the results which are provided on GitHub.