Incorporating Support Vector Machine for Identifying Protein Tyrosine Sulfation Sites

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題名:	Incorporating Support Vector Machine for Identifying Protein Tyrosine Sulfation Sites
作者:	Chang,WC;Lee,TY;Shien,DM;Hsu,JBK;Horng,JT;Hsu,PC;Wang,TY;Huang,HD;Pan,RL
貢獻者:	資訊工程研究所
關鍵詞:	O-SULFATION;ELECTROSTATIC POTENTIALS;MASS-SPECTROMETRY;PREDICTION;PHOSPHORYLATION;SEQUENCES;BINDING;RECOGNITION;RECEPTORS;PEPTIDES
日期:	2009
上傳時間:	2010-06-29 20:14:19 (UTC+8)
出版者:	中央大學
摘要:	Tyrosine sulfation is a post-translational modification of many secreted and membrane-bound proteins. It governs protein-protein interactions that are involved in leukocyte adhesion, hemostasis, and chemokine signaling. However, the intrinsic feature of sulfated protein remains elusive and remains to be delineated. This investigation presents SulfoSite, which is a computational method based on a support vector machine (SVM) for predicting protein sulfotyrosine sites. The approach was developed to consider structural information such as concerning the secondary structure and solvent accessibility of amino acids that surround the sulfotyrosine sites. One hundred sixty-two experimentally verified tyrosine sulfation sites were identified using UniProtKB/SwissProt release 53.0. The results of a five-fold cross-validation evaluation suggest that the accessibility of the solvent around the sulfotyrosine sites contributes substantially to predictive accuracy. The SVM classifier can achieve an accuracy of 94.2% in fivefold cross validation when sequence positional weighted matrix (PWM) is coupled with values of the accessible surface area (ASA). The proposed method significantly outperforms previous methods for accurately predicting the location of tyrosine sulfation sites. (C) 2009 Wiley Periodicals, Inc. J Comput Chem 30: 2526-2537, 2009
關聯:	JOURNAL OF COMPUTATIONAL CHEMISTRY
顯示於類別:	[資訊工程研究所] 期刊論文

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