Abstract
Protein methylation is an important Post-Translational Modification (PTMs) of proteins. Arginine methylation carries out and regulates several important biological functions, including gene regulation and signal transduction. Experimental identification of arginine methylation site is a daunting task as it is costly as well as time and labour intensive. Hence reliable prediction tools play an important task in rapid screening and identification of possible methylation sites in proteomes. Our preliminary assessment using the available prediction methods on collected data yielded unimpressive results. This motivated us to perform a comprehensive data analysis and appraisal of features relevant in the context of biological significance, that led to the development of a prediction tool PRmePRed with better performance. The PRmePRed perform reasonably well with an accuracy of 84.10%, 82.38% sensitivity, 83.77% specificity, and Matthew’s correlation coefficient of 66.20% in 10-fold cross-validation. PRmePRed is freely available at http://bioinfo.icgeb.res.in/PRmePRed/
Highlights
Protein arginine methylation (PRme) is an abundant post-translational modification (PTM) which affects several major cellular processes in eukaryotes
For the arginine methylation prediction problem, best accuracy achieved by window length 19 with a subset of 150 features (Fig 1A), best sensitivity achieved by window length 19 with subset of 100 features (Fig 1B), best specificity achieved by window length 35 with subset of 100 features (Fig 1C) and the best Matthews Correlation Coefficient (MCC) achieved by window length 19 with a subset of 100 features (Fig 1D)
We have developed an arginine methylation predictor based on sequence and structure derived features, using support vector machine (SVM)
Summary
Protein arginine methylation (PRme) is an abundant post-translational modification (PTM) which affects several major cellular processes in eukaryotes. Any biological question which aims to investigate the role of PRme in a protein’s function, stability, localization and its interactions initiates with steps that lead to prior identification and validation of the methylation event. In this regard, large-scale proteomics, bolstered by recent advancements in PRme labeling, enrichment, and mass spectrometry (MS) techniques, have contributed significantly towards identification of experimentally verified repertoire of arginine methylated proteins. It is a tedious task to confirm each methylation site independently from the thousands of sites identified from a label-free MS experiment Another option is to go for high throughput screening in vitro
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