Incorporating structural characteristics for identification of protein methylation sites

@article{Shien2009IncorporatingSC,
  title={Incorporating structural characteristics for identification of protein methylation sites},
  author={Dray-Ming Shien and Tzong-Yi Lee and Wen-Chi Chang and Justin Bo-Kai Hsu and Jorng-Tzong Horng and Po-Chiang Hsu and Ting-Yuan Wang and Hsien-Da Huang},
  journal={Journal of Computational Chemistry},
  year={2009},
  volume={30}
}
Studies over the last few years have identified protein methylation on histones and other proteins that are involved in the regulation of gene transcription. Several works have developed approaches to identify computationally the potential methylation sites on lysine and arginine. Studies of protein tertiary structure have demonstrated that the sites of protein methylation are preferentially in regions that are easily accessible. However, previous studies have not taken into account the solvent… 
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Prediction of protein methylation sites using conditional random field.
TLDR
Methcrf, a computational predictor based on conditional random field (CRF) for predicting protein methylation sites limit to lysine and arginine residues due to the absence of enough experimentally verified data for other residues is proposed.
Predicting protein lysine methylation sites by incorporating single-residue structural features into Chou's pseudo components
Computational prediction of methylation types of covalently modified lysine and arginine residues in proteins
TLDR
It is anticipated that this study provides a new lead for future computational analysis of protein methylation, and the prediction of methylation types of covalently modified lysine and arginine residues can generate more useful information for further experimental manipulation.
Position-specific prediction of methylation sites from sequence conservation based on information theory
TLDR
A novel method based only on sequence conservation to predict protein methylation sites that yielded a promising result on both the benchmark dataset and independent test set and indicates that this method can serve as a useful supplement to elucidate the mechanism ofprotein methylation and facilitate hypothesis-driven experimental design and validation.
Identification and characterization of lysine-methylated sites on histones and non-histone proteins
PLMLA: prediction of lysine methylation and lysine acetylation by combining multiple features.
TLDR
This work presents a method called PLMLA that incorporates protein sequence information, secondary structure and amino acid properties to predict methylation and acetylation of lysine residues in whole protein sequences and reveals that methyllysine is likely to occur at the coil region and acetyllysine prefers to occurs at the helix region of protein.
PMeS: Prediction of Methylation Sites Based on Enhanced Feature Encoding Scheme
TLDR
A method called PMeS is developed to improve the prediction of protein methylation sites based on an enhanced feature encoding scheme and support vector machine that provides better predictive performance and greater robustness.
Proteome-wide Prediction of Lysine Methylation Reveals Novel Histone Marks and Outlines the Methyllysine Proteome
TLDR
MethylSight, a program that predicts Kme events solely on physicochemical and biochemical properties of putative methylation sites, which can be validated by targeted mass spectrometry, has been developed and identified 70 new histone Kme marks with a 90% validation rate.
Progress and challenges in predicting protein methylation sites.
TLDR
This review summarizes the progress in the prediction of protein methylation sites from the dataset, feature representation, prediction algorithm and online resources in the past ten years and discusses the challenges that are faced while developing novel predictors in the future.
N‐Ace: Using solvent accessibility and physicochemical properties to identify protein N‐acetylation sites
TLDR
The proposed method, N‐Ace, is evaluated using independent test sets in various acetylated residues and predictive accuracies of 90% were achieved, indicating that the performance of N-Ace is comparable with that of other acetylation prediction methods.
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