Please use this identifier to cite or link to this item: https://doi.org/10.1021/pr050110a
Title: Prediction of functional class of the SARS coronavirus proteins by a statistical learning method
Authors: Cai, C.Z. 
Han, L.Y. 
Chen, X.
Cao, Z.W.
Chen, Y.Z. 
Keywords: Distantly related protein
Protein functional characterization
SARS Coronuvirus
Support vector machines
SVMProt
Issue Date: Sep-2005
Citation: Cai, C.Z., Han, L.Y., Chen, X., Cao, Z.W., Chen, Y.Z. (2005-09). Prediction of functional class of the SARS coronavirus proteins by a statistical learning method. Journal of Proteome Research 4 (5) : 1855-1862. ScholarBank@NUS Repository. https://doi.org/10.1021/pr050110a
Abstract: The complete genome of severe acute respiratory syndrome coronavirus (SARS-CoV) reveals the existence of putative proteins unique to SARS-CoV. Identification of their function facilitates a mechanistic understanding of SARS infection and drug development for its treatment. The sequence of the majority of these putative proteins has no significant similarity to those of known proteins, which complicates the task of using sequence analysis tools to probe their function. Support vector machines (SVM), useful for predicting the functional class of distantly related proteins, is employed to ascribe a possible functional class to SARS-CoV proteins. Testing results indicate that SVM is able to predict the functional class of 73% of the known SARS-CoV proteins with available sequences and 67% of 18 other novel viral proteins. A combination of the sequence comparison method BLAST and SVMProt can further improve the prediction accuracy of SMVProt such that the functional class of two additional SARS-CoV proteins is correctly predicted. Our study suggests that the SARS-CoV genome possibly contains a putative voltage-gated ion channel, structural proteins, a carbon-oxygen lyase, oxidoreductases acting on the CH-OH group of donors, and an ATP-binding cassette transporter. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot. cgi. © 2005 American Chemical Society.
Source Title: Journal of Proteome Research
URI: http://scholarbank.nus.edu.sg/handle/10635/104846
ISSN: 15353893
DOI: 10.1021/pr050110a
Appears in Collections:Staff Publications

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