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Ni Huang

Publications and source records attributed to Ni Huang.

3 recordsLinked to original sources

MeMo: a web tool for prediction of protein methylation modifications.

Protein methylation is an important and reversible post-translational modification of proteins (PTMs), which governs cellular dynamics and plasticity. Experimental identification of the methylation site is labor-intensive and often limited by the availability of reagents, such as methyl-specific antibodies and optimization of enzymatic reaction. Computational analysis may facilitate the identification of potential methylation sites with ease and provide insight for further experimentation. Here we present a novel protein methylation prediction web server named MeMo, protein methylation modification prediction, implemented in Support Vector Machines (SVMs). Our present analysis is primarily focused on methylation on lysine and arginine, two major protein methylation sites. However, our computational platform can be easily extended into the analyses of other amino acids. The accuracies for prediction of protein methylation on lysine and arginine have reached 67.1 and 86.7%, respectively. Thus, the MeMo system is a novel tool for predicting protein methylation and may prove useful in the study of protein methylation function and dynamics. The MeMo web server is available at: http://www.bioinfo.tsinghua.edu.cn/~tigerchen/memo.html.

Arginine↗

CTKPred: an SVM-based method for the prediction and classification of the cytokine superfamily.

Cell proliferation, differentiation and death are controlled by a multitude of cell-cell signals and loss of this control has devastating consequences. Prominent among these regulatory signals is the cytokine superfamily, which has crucial functions in the development, differentiation and regulation of immune cells. In this study, a support vector machine (SVM)-based method was developed for predicting families and subfamilies of cytokines using dipeptide composition. The taxonomy of the cytokine superfamily with which our method complies was described in the Cytokine Family cDNA Database (dbCFC) and the dataset used in this study for training and testing was obtained from the dbCFC and Structural Classification of Proteins (SCOP). The method classified cytokines and non-cytokines with an accuracy of 92.5% by 7-fold cross-validation. The method is further able to predict seven major classes of cytokine with an overall accuracy of 94.7%. A server for recognition and classification of cytokines based on multi-class SVMs has been set up at http://bioinfo.tsinghua.edu.cn/~huangni/CTKPred/.

Algorithms↗