※ Computational resources of Protein Methylation sites:

Last updated: March 26th, 2016

Introduction :

   Protein methylation is an important post-translation modification (PTM), which was firstly discovered near half a century ago (1959), and was proved to be reversible later. Despite the long history of its discovery, the function and role it played in cellular remains to be elucidate. The foundation of function of protein methylation research is identification of protein methylated sites. The experimental identification of protein methylation sites is labor-extensive and expensive. Thus, a computational tool of prediction is needed.

    We apologized that the computational studies without any web links of databases or tools will not be included in this compendium, since it's not easy for experimentalists to use studies directly. We are grateful for users feedback. Please inform Wankun Deng, Yu Xue to add, remove or update one or multiple web links below.


Prediction of Protein Methylation Sites :

1. AutoMotif server (Service stopped, AMS replaced it now): prediction of single residue post-translational modifications in proteins. (Plewczynski et al, 2005, Basu et al., 2010).

2. MethylationPredictor: an SVM Predictor for Methylation (Kenneth et al., 2005).

3. MeMo:MeMo: a web tool for prediction of protein methylation modifications (Chen et al., 2006).

4. BPB-PPMS: Computational identification of protein methylation sites through bi-profile Bayes feature extraction. (Shao et al., 2009).

5. MASA: Incorporating structural characteristics for identification of protein methylation sites (Shien et al., 2009).

6. Methy SVMIACO (The Methy SVMIACO can be acquired freely on request from the authors): Identification of protein methylation sites by coupling improved ant colony optimization algorithm and support vector machine. (Li et al., 2011).

7. PLMLA: prediction of lysine methylation and lysine acetylation by combining multiple features (Shi et al., 2012).

8. PMeS: Prediction of Methylation Sites Based on Enhanced Feature Encoding Scheme (Shi et al., 2012).

9. Musite: A Tool for Global Prediction of General and Kinase-specific Phosphorylation Sites (Gao et al., 2010).