Methylation site detection software tools | Post-translational modification data analysis

A web tool for identifying the protein methylation site based on the accessible surface area (ASA) which is surrounding the modification site. MASA combines the support vector machine with the sequence and structural characteristics of proteins to identify methylation sites on lysine, arginine, glutamate, and asparagine.

An in silico online tool for identification of potential methylation sites from protein sequences. The computational methodology is based on Bi-profile Bayes combined with support vector machines (SVMs). BPB-PPMS yields, on average, a sensitivity of 74.71%, a specificity of 94.32% and an accuracy of 87.98% for arginine as well as a sensitivity of 70.05%, a specificity of 77.08% and an accuracy of 75.51% for lysine in the case of 5-fold cross validation, together with the results of the case study, suggesting that BPB-PPMS presented here can facilitate the identification of potential methylation sites and more confident annotation of protein methylation.

Identifies methylation sites based on an enhanced feature encoding scheme for extracting the most informative amino acids features. PMes is an enhanced feature encoding scheme that incorporates the amino acid sequence, position information, physicochemical properties of residues with structural characteristic to improve the prediction of protein methylation sites. It was composed of sparse property coding (SPC), normalized van der Waals volume (VDWV), position weight amino acid composition (PWAA) and solvent accessible surface area (ASA).

An online tool for protein methylation site prediction employing the algorithm of support vector machines (SVMs). Due to the data limitation our system focuses on methylated arginine and lysine sites. The accuracies for lysine and arginine methylation reach 67.1 and 86.7%, respectively.

Predicts lysine methylation and lysine acetylation only from the protein primary sequence. PLMLA is an algorithm implemented in a web app that consider not only protein sequence information but also physicochemical properties of amino acids and residue secondary structure within the lysine regions. The prediction model achieved a promising performance and outperformed other prediction tools. This resource could help to understand the lysine methylation and acetylation mechanism and guide the related experimental validation.

A tool based on information gain (IG) feature optimization method for species-specific methylation site prediction. The IG method was adopted to analyze the importance and contribution of each feature, then select the valuable dimension feature vectors to reconstitute a new orderly feature, which was applied to build the finally prediction model. Finally, PSSMe improves prediction performance of accuracy about 15% comparing with single features. Furthermore, our species-specific model significantly improves the predictive performance compare with other general methylation prediction tools.

A plugin implemented with the commonly used visualization software PyMOL. PyTMs enables users to introduce a set of common post-translational modifications (PTMs) into protein/peptide models and can be used to address research questions related to PTMs. Ten types of modification are currently supported, including acetylation, carbamylation, citrullination, cysteine oxidation, malondialdehyde adducts, methionine oxidation, methylation, nitration, proline hydroxylation and phosphorylation.