Methylation site prediction bioinformatics tools

1 - 17 of 17 results

PSSMe / Prediction of Species-Specific Methylation sites

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.

iPTM-mLys

Identifies multiple lysine post-translational modification (PTM) sites and their different types. iPTM-mLys represents the first multi-label PTM predictor ever established. The novel predictor is featured by incorporating the sequence-coupled effects into the general PseAAC, and by fusing an array of basic random forest classifiers into an ensemble system. Rigorous cross-validations via a set of multi-label metrics indicate that the first multi-label PTM predictor is very promising and encouraging.

GPS-MSP / GPS-Methyl-group Specific Predictor

Provides a useful method for the computational analysis of protein methylation. GPS-MSP is a computational tool for the prediction of general protein methylation sites and methylation types of methyl-lysines and methyl-arginines. This method is available under an online service and a standalone package.

MethylQuant

Detects peptides associated with putative methyl-SILAC pairs. MethylQuant provides measures for users to assess the validity of detected peptide pairs, and generates relative quantification information for them. It can be used to validate arginine methylation sites in human T cells and Saccharomyces cerevisiae and can produce false positive and false negative rates of methylpeptide validation for samples of varying complexities.

PTMProber

A homology-based pipeline that allows identification of potential modification sites for most of the proteomes lacking post-translational modifications (PTMs) data. PTMProber provides a unique functionality for constructing customized models (such as organism-specific and modification-specific models) from user-provided data sets. Cross-promotion E-value (CPE) as stringent benchmark has been used in PTMProber to evaluate homology to known modification sites. Independent-validation tests show that PTMProber achieves over 58.8% recall with high precision by CPE benchmark. Comparisons with other machine-learning tools show that PTMProber pipeline performs better on general predictions.

PMes

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).

PLMLA

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.

PRmePRed / Protein aRginine methylation PRediction

Predicts arginine methylation sites in proteins. PRmePRed is a predictor based on sequence and structure derived features, using support vector machines (SVMs).

iMethyl-PseAAC

Predicts methylation sites in proteins. With the assistance of SVM, the highlight of iMethyl-PseAAC is to employ amino acid sequence features extracted from the sequence evolution information via grey system model (Grey-PSSM). In the prediction system, a peptide sample was formulated by a 346-dimensional vector, formed by incorporating its physicochemical, sequence evolution, biochemical, and structural disorder information into the general form of pseudo amino acid composition.

PyTMs

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.

MethK

Identifies lysine-methylated sites on histones and non-histone proteins. MethK is a web server that provides a user-friendly interface and predictive results. Users can submit a protein sequence and select the protein type to identify potential lysine-methylated sites. It was developed using the support vector machine (SVM) models with amino acid composition (AAC) and accessible surface area (ASA) features for the histone model, and amino acid (AA) and amino acid pair composition (AAPC) features for the non-histone model.

MePred-RF

Predicts and analyzes protein methylation sites, particularly for large-scale genome projects. MePred-RF is a machine-learning-based method for protein methylation site prediction. This approach exploits sequence information alone in its feature representation of proteins. This prediction procedure has four phases: 1) data pre-processing, 2) feature representation, 3) feature selection, and 4) prediction engine.

MethylSight

Identifies methylation sites. MethylSight is composed of two main modules: (i) the “lysine methylation predictor” allows users to browse a foresees repository dealing about lysine in humans or to submit their own structures for analysis and; (ii) a “structure visualization” function that displays methylase structures in 3D and permit to focus on regions of interest. The application aims to improve grasp about the methyllysine proteome and help for validation of new methylation sites.

iLM-2L

Predicts lysine methylation sites and their methylation degrees. iLM-2L is based on composition of k-spaced amino acid pairs feature coding scheme and support vector machine (SVM) algorithm. It outperforms five existing lysine methylation sites predictors: MeMo, MASA, BPBPPMS, PMeS, and iMethyl-PseAAC. The tool contributes to an understanding of the mechanisms of lysine methylation system.

MeMo / Protein Methylation Modification Prediction

Obsolete

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.

MASA / Methylation site based on the Accessible Surface Area

Obsolete

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.

BPB-PPMS

Obsolete

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.

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