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PalmPred: An SVM Based Palmitoylation Prediction Method Using Sequence Profile Information

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  • Bandana Kumari
  • Ravindra Kumar
  • Manish Kumar

Abstract

Protein palmitoylation is the covalent attachment of the 16-carbon fatty acid palmitate to a cysteine residue. It is the most common acylation of protein and occurs only in eukaryotes. Palmitoylation plays an important role in the regulation of protein subcellular localization, stability, translocation to lipid rafts and many other protein functions. Hence, the accurate prediction of palmitoylation site(s) can help in understanding the molecular mechanism of palmitoylation and also in designing various related experiments. Here we present a novel in silico predictor called ‘PalmPred’ to identify palmitoylation sites from protein sequence information using a support vector machine model. The best performance of PalmPred was obtained by incorporating sequence conservation features of peptide of window size 11 using a leave-one-out approach. It helped in achieving an accuracy of 91.98%, sensitivity of 79.23%, specificity of 94.30%, and Matthews Correlation Coefficient of 0.71. PalmPred outperformed existing palmitoylation site prediction methods – IFS-Palm and WAP-Palm on an independent dataset. Based on these measures it can be anticipated that PalmPred will be helpful in identifying candidate palmitoylation sites. All the source datasets, standalone and web-server are available at http://14.139.227.92/mkumar/palmpred/.

Suggested Citation

  • Bandana Kumari & Ravindra Kumar & Manish Kumar, 2014. "PalmPred: An SVM Based Palmitoylation Prediction Method Using Sequence Profile Information," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.
    • Handle: RePEc:plo:pone00:0089246
    DOI: 10.1371/journal.pone.0089246
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    References listed on IDEAS

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    1. Jiangan Xie & Zhiling Xu & Shangbo Zhou & Xianchao Pan & Shaoxi Cai & Li Yang & Hu Mei, 2013. "The VHSE-Based Prediction of Proteasomal Cleavage Sites," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-8, September.
    2. Yan Xu & Jun Ding & Ling-Yun Wu & Kuo-Chen Chou, 2013. "iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    3. Yang Jiang & Bi-Qing Li & Yuchao Zhang & Yuan-Ming Feng & Yu-Fei Gao & Ning Zhang & Yu-Dong Cai, 2013. "Prediction and Analysis of Post-Translational Pyruvoyl Residue Modification Sites from Internal Serines in Proteins," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-11, June.
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