Incorporating Structural Characteristics for Identification of Protein Methylation Sites

[[abstract]]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-accessible surface area (ASA) that surrounds the methylation sites. This work presents a method named MASA that combines the support vector machine with the sequence and structural characteristics of proteins to identify methylation sites on lysine, arginine, glutamate, and asparagine. Since most experimental methylation sites are not associated with corresponding protein tertiary structures in the Protein Data Bank, the effective solvent-accessible prediction tools have been adopted to determine the potential ASA values of amino acids in proteins. Evaluation of predictive performance by cross-validation indicates that the ASA values around the methylation sites can improve the accuracy of prediction. Additionally, an independent test reveals that the prediction accuracies for methylated lysine and arginine are 80.8 and 85.0%, respectively. Finally, the proposed method is implemented as an effective system for identifying protein methylation sites. The developed web server is freely available at http:/IMASA.mbc.nctu.edu.tw/. (C) 2009 Wiley Periodicals, Inc. J Comput Chem 30: 1532-1543, 200

Incorporating structural characteristics for identification of protein methylation sites

[[abstract]]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-accessible surface area (ASA) that surrounds the methylation sites. This work presents a method named MASA that combines the support vector machine with the sequence and structural characteristics of proteins to identify methylation sites on lysine, arginine, glutamate, and asparagine. Since most experimental methylation sites are not associated with corresponding protein tertiary structures in the Protein Data Bank, the effective solvent-accessible prediction tools have been adopted to determine the potential ASA values of amino acids in proteins. Evaluation of predictive performance by cross-validation indicates that the ASA values around the methylation sites can improve the accuracy of prediction. Additionally, an independent test reveals that the prediction accuracies for methylated lysine and arginine are 80.8 and 85.0%, respectively. Finally, the proposed method is implemented as an effective system for identifying protein methylation sites. The developed web server is freely available at http://MASA.mbc.nctu.edu.tw/. © 2009 Wiley Periodicals, Inc

Principles for the post-GWAS functional characterisation of risk loci

Several challenges lie ahead in assigning functionality to susceptibility SNPs. For example, most effect sizes are small relative to effects seen in monogenic diseases, with per allele odds ratios usually ranging from 1.15 to 1.3. It is unclear whether current molecular biology methods have enough resolution to differentiate such small effects. Our objective here is therefore to provide a set of recommendations to optimize the allocation of effort and resources in order maximize the chances of elucidating the functional contribution of specific loci to the disease phenotype. It has been estimated that 88% of currently identified disease-associated SNP are intronic or intergenic. Thus, in this paper we will focus our attention on the analysis of non-coding variants and outline a hierarchical approach for post-GWAS functional studies

The Role of Long Noncoding RNAs in Gene Expression Regulation

Accumulating evidence highlights that noncoding RNAs, especially the long noncoding RNAs (lncRNAs), are critical regulators of gene expression in development, differentiation, and human diseases, such as cancers and heart diseases. The regulatory mechanisms of lncRNAs have been categorized into four major archetypes: signals, decoys, scaffolds, and guides. Increasing evidence points that lncRNAs are able to regulate almost every cellular process by their binding to proteins, mRNAs, miRNA, and/or DNAs. In this review, we present the recent research advances about the regulatory mechanisms of lncRNA in gene expression at various levels, including pretranscription, transcription regulation, and posttranscription regulation. We also introduce the interaction between lncRNA and DNA, RNA and protein, and the bioinformatics applications on lncRNA research

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

M6AMRFS: Robust Prediction of N6-Methyladenosine Sites With Sequence-Based Features in Multiple Species

As one of the well-studied RNA methylation modifications, N6-methyladenosine (m6A) plays important roles in various biological progresses, such as RNA splicing and degradation, etc. Identification of m6A sites is fundamentally important for better understanding of their functional mechanisms. Recently, machine learning based prediction methods have emerged as an effective approach for fast and accurate identification of m6A sites. In this paper, we proposed “M6AMRFS”, a new machine learning based predictor for the identification of m6A sites. In this predictor, we exploited a new feature representation algorithm to encode RNA sequences with two feature descriptors (dinucleotide binary encoding and Local position-specific dinucleotide frequency), and used the F-score algorithm combined with SFS (Sequential Forward Search) to enhance the feature representation ability. To predict m6A sites, we employed the eXtreme Gradient Boosting (XGBoost) algorithm to build a predictive model. Benchmarking results showed that the proposed predictor is competitive with the state-of-the art predictors. Importantly, robust predictions for multiple species by our predictor demonstrate that our predictive models have strong generalization ability. To the best of our knowledge, M6AMRFS is the first tool that can be used for the identification of m6A sites in multiple species. To facilitate the use of our predictor, we have established a user-friendly webserver with the implementation of M6AMRFS, which is currently available in http://server.malab.cn/M6AMRFS/. We anticipate that it will be a useful tool for the relevant research of m6A sites

Computational Identification and Modeling of Crosstalk between Phosphorylation, O-β-glycosylation and Methylation of FoxO3 and Implications for Cancer Therapeutics

FoxO3 is a member of the forkhead class of transcription factors and plays a major role in the regulation of diverse cellular processes, including cell cycle arrest, DNA repair, and protection from stress stimuli by detoxification of reactive oxygen species. In addition, FoxO3 is a tumor suppressor and has been considered as a novel target for cancer therapeutics. Phosphorylation of FoxO3 via the AKT, IKK, and ERK pathways leads to deregulation, cytoplasmic retention, degradation of FoxO3 and favors tumor progression. Identification of the amino acid residues that are the target of different posttranslational modifications (PTMs) provides a foundation for understanding the molecular mechanisms of FoxO3 modifications and associated outcomes. In addition to phosphorylation, serine and threonine residues of several proteins are regulated by a unique type of PTM known as O-β-glycosylation, which serves as a functional switch. We sought to investigate the crosstalk of different PTMs on the FoxO3 which leads to the onset/progression of various cancers and that could also potentially be targeted as a therapeutic point of intervention. A computational workflow and set of selection parameters have been defined for the identification of target sites and crosstalk between different PTMs. We identified phosphorylation, O-β-GlcNAc modification, and Yin Yang sites on Ser/Thr residues, and propose a potential novel mechanism of crosstalk between these PTMs. Furthermore, methylation potential of human FoxO3 at arginine and lysine residues and crosstalk between methylation and phosphorylation have also been described. Our findings may facilitate the study of therapeutic strategies targeting posttranslational events