ArticlePloS one2012
PMeS: prediction of methylation sites based on enhanced feature encoding scheme.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Leveraging learned representations and multitask learning for lysine methylation site discovery.Scientific reports · 2026Article
- Leveraging learned representations and multitask learning for lysine methylation site discovery.bioRxiv : the preprint server for biology · 2025Article
- A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery.Medical oncology (Northwood, London, England) · 2022Review
- Integrated analysis to study the interplay between post-translational modifications (PTM) in hepatitis C virus proteins and hepatocellular carcinoma (HCC) development.Scientific reports · 2022Article
- CNNArginineMe: A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence.Frontiers in genetics · 2022Article
- The investigation of nonsynonymous SNPs of humanHeliyon · 2021Article
- SSMFN: a fused spatial and sequential deep learning model for methylation site prediction.PeerJ. Computer science · 2021Article
- DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins.Molecular omics · 2020Article
- Exploring the effect of nsSNPs in human YPEL3 gene in cellular senescence.Scientific reports · 2020Article
- Two-Level Protein Methylation Prediction using structure model-based features.Scientific reports · 2020Article
- Chemical Decorations of "MARs" Residents in Orchestrating Eukaryotic Gene Regulation.Frontiers in cell and developmental biology · 2020Review
- Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix.BMC molecular and cell biology · 2019Article
- Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.Briefings in bioinformatics · 2019Review
- iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAACCurrent genomics · 2019Article
- An Information Entropy-Based Approach for Computationally Identifying Histone Lysine Butyrylation.Frontiers in genetics · 2019Article
- Molecular mechanisms of regulation of sphingosine kinase 1.Biochimica et biophysica acta. Molecular and cell biology of lipids · 2018Review
- The FCS-like zinc finger scaffold of the kinase SnRK1 is formed by the coordinated actions of the FLZ domain and intrinsically disordered regions.The Journal of biological chemistry · 2018Article
- Cold and Heat Stress Diversely Alter Both Cauliflower Respiration and Distinct Mitochondrial Proteins Including OXPHOS Components and Matrix Enzymes.International journal of molecular sciences · 2018Article
- A non-canonical function of Ezh2 preserves immune homeostasis.EMBO reports · 2017Article
- Mutation-Structure-Function Relationship Based Integrated Strategy Reveals the Potential Impact of Deleterious Missense Mutations in Autophagy Related Proteins on Hepatocellular Carcinoma (HCC): A Comprehensive Informatics Approach.International journal of molecular sciences · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Protein methylation is predominantly found on lysine and arginine residues, and carries many important biological functions, including gene regulation and signal transduction. Given their important involvement in gene expression, protein methylation and their regulatory enzymes are implicated in a variety of human disease states such as cancer, coronary heart disease and neurodegenerative disorders. Thus, identification of methylation sites can be very helpful for the drug designs of various related diseases. In this study, we developed a method called PMeS to improve the prediction of protein methylation sites based on an enhanced feature encoding scheme and support vector machine. The enhanced feature encoding scheme was composed of the sparse property coding, normalized van der Waals volume, position weight amino acid composition and accessible surface area. The PMeS achieved a promising performance with a sensitivity of 92.45%, a specificity of 93.18%, an accuracy of 92.82% and a Matthew's correlation coefficient of 85.69% for arginine as well as a sensitivity of 84.38%, a specificity of 93.94%, an accuracy of 89.16% and a Matthew's correlation coefficient of 78.68% for lysine in 10-fold cross validation. Compared with other existing methods, the PMeS provides better predictive performance and greater robustness. It can be anticipated that the PMeS might be useful to guide future experiments needed to identify potential methylation sites in proteins of interest. The online service is available at http://bioinfo.ncu.edu.cn/inquiries_PMeS.aspx.
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