Prediction and analysis of protein methylarginine and methyllysine based on Multisequence features

Abstract

Protein methylation, one of the most important post-translational modifications, typically takes place on arginine or lysine residue. The reversible modification involves a series of basic cellular processes. Identification of methyl proteins with their sites will facilitate the understanding of the molecular mechanism of methylation. Besides the experimental methods, computational predictions of methylated sites are much more desirable for their convenience and fast speed. Here, we propose a method dedicated to predicting methylated sites of proteins. Feature selection was made on sequence conservation, physicochemical/biochemical properties, and structural disorder by applying maximum relevance minimum redundancy and incremental feature selection methods. The prediction models were built according to nearest the neighbor algorithm and evaluated by the jackknife cross-validation. We built 11 and 9 predictors for methylarginine and methyllysine, respectively, and integrated them to predict methylated sites. As a result, the average prediction accuracies are 74.25%, 77.02% for methylarginine and methyllysine training sets, respectively. Feature analysis suggested evolutionary information, and physicochemical/biochemical properties play important roles in the recognition of methylated sites. These findings may provide valuable information for exploiting the mechanisms of methylation. Our method may serve as a useful tool for biologists to find the potential methylated sites of proteins.

iLM-2L: A two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou׳s general PseAAC

“…Therefore, the comparison was only limited in the 1st level prediction of iLM-2L. However, the models proposed in (Daily et al, 2005) and (Hu et al, 2011) did not have web-server at all, and the web-server of CKSAAP_Methsite (Zhang et al, 2013) did not work. Hence we compared the 1st level of iLM-2L with five existing predictors: MeMo, MASA, BPB-PPMS, PMeS, and iMethyl-PseAAC.…”

“…Using binary coding, accessible surface areas and second structural feature extraction approach, Shien et al (2009) developed an online tool called MASA for the prediction of methylation sites. Hu et al (2011) proposed a novel algorithm for predicting methylation sites, which used amino acid factors, position specific scoring matrix and disorder score feature extraction based on nearest neighbor algorithm. Shi et al (2012) constructed an online server called PMeS to predict protein methylation sites using an enhanced feature encoding scheme and SVMs algorithm.…”

Progress and challenges in predicting protein methylation sites

“…51 Statistical analysis of flanking regions also suggests that there are significant differences in position-specific amino acid preferences around methylated and non-methylated sites, especially for methylarginine. 19,23,38,39 Therefore protein sequence information is informative for determining methylation status.…”

“…Evolutionary information is an important characteristic of protein, for the conserved residues at specific sequence sites are under strong selective pressure and therefore are always functional relevant. 39 Based on biological feature analysis, Hu et al pointed out that evolutionary information plays important roles in the recognition of methylated sites. 39 In the field of methylation site prediction, BLOSUM62 matrix 28,29,37 and position specific score matrices (PSSM) 30,35,38,39 were used to quantify the conservative status for a specific residue and find evolutionary dependencies.…”

“…Except Plewczynski et al 37 defined the negative instances that were chosen randomly from those that do not include experimentally verified PTM of any type, almost all researchers of methylation prediction used a strategy in which arginine or lysine that is not annotated as methylated sites in the experimentally validated methylated proteins is defined as a non-methylation site. 19,[21][22][23][24]28,[29][30][31][32][33][34][35]38,39 The rational of this assumption is that as proteins with known methylation sites have been well studied with respect to methylation, sites in these proteins that are not known to be methylated are more likely to be true negative sites than those obtained by random. This strategy has been widely used in predicting other types of PTM, such as phosphorylation, 40 ubiquitination 41 and acetylation, 42 etc.…”