NDLI: Prediction of Seven Protein Structural Classes by Fusing Multi-Feature Information Including Protein Evolutionary Conservation Information

Content Provider	IEEE Xplore Digital Library
Author	Wei Chen Shao-Wu Zhang Hui-Fang Yang Kun Zhao Kuo-Chen Chou
Copyright Year	2008
Description	Author affiliation: Coll. of Autom., Northwestern Polytech. Univ., Xi'an (Wei Chen; Shao-Wu Zhang; Hui-Fang Yang; Kun Zhao)
Abstract	Determination of protein structural class is a quite meaningful topic in protein science, because a priori knowledge of a protein structural class can provide useful information about its overall structure. The results of most previous studies used high homologous dataset with four structural classes should not be perceived as reliable, because the sequence homology has very significant impact on the prediction accuracy. Using a rigorous dataset with only less than 20% sequence identity to each other, this paper developed a novel pseudo amino acid composition method (PseAA) approach by incorporating protein evolutionary conservation information, amino acid physicochemical properties and statistical information to predict seven structural classes. Comparing with another PseAA method, the overall accuracy of our multi-feature information fusion method is 4.6% higher than that of the method of autocorrelation function of amino acid RICJ880103 in jackknife test. The results indicate that multi-feature information fusion including evolutionary information is effective and robust for the prediction of protein structural class with low sequence identity dataset, and can be effectively complemented with functional domain composition method.
Starting Page	17
Ending Page	20
File Size	185493
Page Count	4
File Format	PDF
ISBN	9781424417476
DOI	10.1109/ICBBE.2008.11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Protein engineering Sequences Accuracy Automation Databases Amino acids Educational institutions Robustness Autocorrelation Testing
Content Type	Text
Resource Type	Article

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