Antifreeze proteins (AFPs), known as thermal hysteresis proteins, are ice-binding proteins. AFPs have been found in many fields such as in vertebrates, invertebrates, plants, bacteria, and fungi. Although the function of AFPs is common, the sequences and structures of them show a high degree of diversity. AFPs can be adsorbed in ice crystal surface and inhibit the growth of ice crystals in solution. However, the interaction between AFPs and ice crystal is not completely known for human beings. It is vitally significant to propose an automated means as a high-throughput tool to timely identify the AFPs. Analyzing physicochemical characteristics of AFPs sequences is very significant to understand the ice-protein interaction. In this manuscript, a predictor called “iAFP-Ense” was developed. The operation engine to run the AFPs prediction is an ensemble classifier formed by a voting system to fuse eleven different random forest classifiers based on feature extraction. We also compare our predictor with the AFP-PseAAC via the tenfold cross-validation on the same benchmark dataset. The comparison with the existing methods indicates the new predictor is very promising, meaning that many important key features which are deeply hidden in complicated protein sequences. The predictor used in this article is freely available at http://www.jci-bioinfo.cn/iAFP-Ense .
The Journal of Membrane Biology – Springer Journals
Published: Nov 3, 2016
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera