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Nucleic Acids Research 2005 33(Web Server Issue):W184-W187; doi:10.1093/nar/gki393
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© The Author 2005. Published by Oxford University Press. All rights reserved
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Article

GPS: a comprehensive www server for phosphorylation sites prediction

Yu Xue1, Fengfeng Zhou2, Minjie Zhu1, Kashif Ahmed1, Guoliang Chen2 and Xuebiao Yao1,3,*

1School of Life Science, University of Science and Technology of China Hefei, Anhui 230027, P. R. China 2Department of Computer Science and Technology, University of Science and Technology of China Hefei, Anhui 230027, P. R. China 3Department of Physiology, Morehouse School of Medicine Atlanta, GA 30310, USA

*To whom correspondence should be addressed. Tel: +86 551 3606294; Fax: +86 551 3607141; Email: yaoxb{at}ustc.edu.cn

Received January 18, 2005. Revised March 1, 2005. Accepted March 11, 2005.

Protein phosphorylation plays a fundamental role in most of the cellular regulatory pathways. Experimental identification of protein kinases' (PKs) substrates with their phosphorylation sites is labor-intensive and often limited by the availability and optimization of enzymatic reactions. Recently, large-scale analysis of the phosphoproteome by the mass spectrometry (MS) has become a popular approach. But experimentally, it is still difficult to distinguish the kinase-specific sites on the substrates. In this regard, the in silico prediction of phosphorylation sites with their specific kinases using protein's primary sequences may provide guidelines for further experimental consideration and interpretation of MS phosphoproteomic data. A variety of such tools exists over the Internet and provides the predictions for at most 30 PK subfamilies. We downloaded the verified phosphorylation sites from the public databases and curated the literature extensively for recently found phosphorylation sites. With the hypothesis that PKs in the same subfamily share similar consensus sequences/motifs/functional patterns on substrates, we clustered the 216 unique PKs in 71 PK groups, according to the BLAST results and protein annotations. Then, we applied the group-based phosphorylation scoring (GPS) method on the data set; here, we present a comprehensive PK-specific prediction server GPS, which could predict kinase-specific phosphorylation sites from protein primary sequences for 71 different PK groups. GPS has been implemented in PHP and is available on a www server at http://973-proteinweb.ustc.edu.cn/gps/gps_web/.


The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors


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