Prediction Of Methylation Sites Research Articles

Multifactorial feature extraction and site prognosis model for protein methylation data.

Integrated studies (multi-omics studies) comprising genetic, proteomic and epigenetic data analyses have become an emerging topic in biomedical research. Protein methylation is a posttranslational modification that plays an essential role in various cellular activities. The prediction of methylation sites (arginine and lysine) is vital to understand the molecular processes of protein methylation. However, current experimental techniques used for methylation site predictions are tedious and expensive. Hence, computational techniques for predicting methylation sites in proteins are necessary. For predicting methylation sites, various computational methods have been proposed in recent years. Most existing methods require structural and evolutionary information for retrieving features, acquiring this information is not always convenient. Thus, we proposed a novel method, called multi-factorial feature extraction and site prognosis model (MufeSPM), for the prediction of protein methylation sites based on information theory features (Renyi, Shannon, Havrda-Charvat and Arimoto entropy), amino acid composition and physicochemical properties acquired from protein methylation data. A random forest algorithm was used to predict methylation sites in protein sequences. This paper also studied the impact of different features and classifiers on arginine and lysine methylation data sets. For the R methylation data set, MufeSPM yielded 82.45%($\pm $ 3.47) accuracy, and for the K methylation data set, it provided an average accuracy of 71.94%($\pm $ 2.12). Additionally, the area under the receiver operating characteristic curve for different classifiers in predicting methylation site was provided. The experimental results signify that MufeSPM performs better than the state-of-the-art predictors.

Computational prediction of methylation types of covalently modified lysine and arginine residues in proteins.

Protein methylation is an essential posttranslational modification (PTM) mostly occurs at lysine and arginine residues, and regulates a variety of cellular processes. Owing to the rapid progresses in the large-scale identification of methylation sites, the available data set was dramatically expanded, and more attention has been paid on the identification of specific methylation types of modification residues. Here, we briefly summarized the current progresses in computational prediction of methylation sites, which provided an accurate, rapid and efficient approach in contrast with labor-intensive experiments. We collected 5421 methyllysines and methylarginines in 2592 proteins from the literature, and classified most of the sites into different types. Data analyses demonstrated that different types of methylated proteins were preferentially involved in different biological processes and pathways, whereas a unique sequence preference was observed for each type of methylation sites. Thus, we developed a predictor of GPS-MSP, which can predict mono-, di- and tri-methylation types for specific lysines, and mono-, symmetric di- and asymmetrical di-methylation types for specific arginines. We critically evaluated the performance of GPS-MSP, and compared it with other existing tools. The satisfying results exhibited that the classification of methylation sites into different types for training can considerably improve the prediction accuracy. Taken together, we anticipate that our 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.

Protein methylation plays vital roles in many biological processes and has been implicated in various human diseases. To fully understand the mechanisms underlying methylation for use in drug design and work in methylation-related diseases, an initial but crucial step is to identify methylation sites. The use of high-throughput bioinformatics methods has become imperative to predict methylation sites. In this study, we developed a novel method that is based only on sequence conservation to predict protein methylation sites. Conservation difference profiles between methylated and non-methylated peptides were constructed by the information entropy (IE) in a wider neighbor interval around the methylation sites that fully incorporated all of the environmental information. Then, the distinctive neighbor residues were identified by the importance scores of information gain (IG). The most representative model was constructed by support vector machine (SVM) for Arginine and Lysine methylation, respectively. This model yielded a promising result on both the benchmark dataset and independent test set. The model was used to screen the entire human proteome, and many unknown substrates were identified. These results indicate that our method can serve as a useful supplement to elucidate the mechanism of protein methylation and facilitate hypothesis-driven experimental design and validation.

Identifying protein arginine methylation sites using global features of protein sequence coupled with support vector machine optimized by particle swarm optimization algorithm

Protein methylation, which plays vital roles in signal transduction and many cellular processes, is one of the most common protein post-translation modifications. Identification of methylation sites is very helpful for understanding the fundamental molecular mechanism of the methylation related biological processes. In silico predictions of methylation sites have emerged to be a powerful approach for methylation identifying. They also facilitate the performance of downstream characterizations and site-specific investigations. Herein, we proposed a novel strategy for the prediction of methylation sites based on a combination of the pseudo amino acid composition (PseAAC) and protein chain description as global features of protein sequence. The global features of protein sequence comprehensively utilize amino acid composition information and sequence-order information, along with the physicochemical properties and structural characteristics of amino acid information. Support vector machine (SVM) is invoked to build the prediction model for methylation sites on the basis of the global features of protein sequence. Meanwhile, a global stochastic optimization technique, particle swarm algorithm (PSO) is employed for effectively searching the optimal parameters in SVM. The prediction accuracy, sensitivity, specificity and Matthew's correlation coefficient values of the independent prediction set are 98.11%, 96.23%, 100% and 96.30%, respectively. It obviously indicates that our method has sufficient prediction effect in identification of the protein arginine methylation sites. As a comparison, other predictors are also constructed based on different feature extracting and modeling strategies. The results show that the proposed method can greatly improve the performance of arginine methylation sites prediction.

Prediction of Methylation Sites Using the Composition of K-Spaced Amino Acid Pairs

Protein methylation is one of the most important post-translational modifications. Typically methylation occurs on arginine or lysine residues in the protein sequence. In the biological system, methylation is catalyzed by enzymes, and should be involved in modification of heavy metals, regulation of gene expression, regulation of protein function, and RNA metabolism. Thus the prediction of methylation sites plays a crucial role. As we know, traditional experiment approaches to predict the sites are accurate, but that are always labor-intensive and time-consuming. Thus, it is common to see that computational methods receive increasingly attentions due to their convenience and fast speed in recent years. In this study, we develop a computational approach to predict the performance of methylarginine and methyllysine sites. First, a new encoding schema as called the CKASSP is used in our method. Then, the support vector machine (SVM) algorithm is used as a predictor. Experimental results show that our method can obtain average prediction accuracy of 87.46%, sensitivity of 99.09%, specificity of 86.89% for arginine methylation sites, and average prediction accuracy of 88.78%, sensitivity of 93.75%, specificity of 81.79% for lysine methylation sites as well, which is better than those of other state-of-art predictors. The online service is implemented by java 1.4.2 and is freely available at http://202.198.129.219:8080/cksaap_methsite.

Prediction of Protein Methylation Sites Using Conditional Random Field

Protein methylation is an important and reversible post-translational modification which regulates diverse protein properties. Many methylation sites on arginine and lysine have been identification through experiments. However, experimental identification without prior knowledge is laborious and costly. Hence, there is interest in the development of computational methods for reliable prediction of methylation sites. Prediction of methylation sites may provide researches with useful information for further productivity in methylation candidate sites discovery. This work proposes 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. The approach is developed to consider combining protein sequence features with structural information such as solvent accessibility of amino acids that surround the methylation sites. In 10-fold cross validation Methcrf can achieve the area under receiver operating characteristic curve (AUC) of 0.85 and 0.80 for arginine and lysine, respectively. The proposed method has comparable performance with previous methods for accurately predicting methylation sites.

PMeS: Prediction of Methylation Sites Based on Enhanced Feature Encoding Scheme

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.

Computational Identification of Protein Methylation Sites through Bi-Profile Bayes Feature Extraction

Protein methylation is one type of reversible post-translational modifications (PTMs), which plays vital roles in many cellular processes such as transcription activity, DNA repair. Experimental identification of methylation sites on proteins without prior knowledge is costly and time-consuming. In silico prediction of methylation sites might not only provide researches with information on the candidate sites for further determination, but also facilitate to perform downstream characterizations and site-specific investigations. In the present study, a novel approach based on Bi-profile Bayes feature extraction combined with support vector machines (SVMs) was employed to develop the model for Prediction of Protein Methylation Sites (BPB-PPMS) from primary sequence. Methylation can occur at many residues including arginine, lysine, histidine, glutamine, and proline. For the present, BPB-PPMS is only designed to predict the methylation status for lysine and arginine residues on polypeptides due to the absence of enough experimentally verified data to build and train prediction models for other residues. The performance of BPB-PPMS is measured with 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 5-fold cross validation experiments. Results obtained from cross-validation experiments and test on independent data sets suggest that BPB-PPMS presented here might facilitate the identification and annotation of protein methylation. Besides, BPB-PPMS can be extended to build predictors for other types of PTM sites with ease. For public access, BPB-PPMS is available at http://www.bioinfo.bio.cuhk.edu.hk/bpbppms.