iAFP-Ense: An Ensemble Classifier for Identifying Antifreeze Protein by Incorporating Grey Model and PSSM into PseAAC

Abstract

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 .

Transcriptome analyses and weighted gene coexpression network analysis reveal key pathways and genes involved in the rapid cold resistance of the Chinese white wax scale insect

“…2.7 | afp gene identification and heterogeneous expression in Escherichia coli Rosetta afp genes were identified using the online software iAFP-Ense (Xiao et al, 2016; http://www.jci-bioinfo.cn/iAFP-Ense) and TargetFreeze (He et al, 2015;http://202.119.84.36:3079/TargetFreeze/) with the default parameter settings. The afp genes were identified in the annotation results of transcriptome and hub genes.…”

AFP-LSE: Antifreeze Proteins Prediction Using Latent Space Encoding of Composition of k-Spaced Amino Acid Pairs

“…Various encoding schemes that employ numerous protein features have been developed to extract diverse information from the protein sequences. As it was believed that an individual feature extraction strategy may only represent a partial target's knowledge 26 , in numerous studies, multiple feature extraction methods are combined to enhance the classification performance 23,24,26,27 . However, it has been observed in recent studies that a viable feature extraction method e.g., CKSAAP can equally contribute toward satisfactory prediction performances [43][44][45] .…”

“…As they performed the evaluation on a non-standard dataset, their results are not discussed in this study. Xiao et al developed a predictor named iAFP-Ense 27 by incorporating evolutionary information into PseAAC using RF classifiers; however, the classifier was not evaluated on an independent test dataset. Khan et al performed segmentation of protein sequences to divide them into two groups for amino acid composition (AAC) and di-peptide composition analyses 28 .…”

Antifreeze Proteins and Their Practical Utilization in Industry, Medicine, and Agriculture

“…Additionally, there are some user-friendly website servers that could analyze the secondary structures and easily predict their structural models [33]. Target Freeze and iAFP-Ense software (An Ensemble classifier for identifying of AFP) could be used to predict the presence of pseudo amino acids in AFPs [34,35], while homology modeling could be performed by using other servers [36,37]. Therefore, approaching various aims, experimental studies as well as bioinformatics tools could provide stronger support and further justification of the results besides being time-and cost-saving [38,39].…”