Please use this identifier to cite or link to this item:
|Title:||Prediction of functional class of the SARS coronavirus proteins by a statistical learning method||Authors:||Cai, C.Z.
|Keywords:||Distantly related protein
Protein functional characterization
Support vector machines
|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|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 11, 2021
WEB OF SCIENCETM
checked on Jan 11, 2021
checked on Jan 9, 2021
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.