iMethyl-PseAAC: Identification of Protein Methylation Sites via a Pseudo Amino Acid Composition Approach

Abstract

Before becoming the native proteins during the biosynthesis, their polypeptide chains created by ribosome's translating mRNA will undergo a series of “product-forming” steps, such as cutting, folding, and posttranslational modification (PTM). Knowledge of PTMs in proteins is crucial for dynamic proteome analysis of various human diseases and epigenetic inheritance. One of the most important PTMs is the Arg- or Lys-methylation that occurs on arginine or lysine, respectively. Given a protein, which site of its Arg (or Lys) can be methylated, and which site cannot? This is the first important problem for understanding the methylation mechanism and drug development in depth. With the avalanche of protein sequences generated in the postgenomic age, its urgency has become self-evident. To address this problem, we proposed a new predictor, called iMethyl-PseAAC. In the prediction system, a peptide sample was formulated by a 346-dimensional vector, formed by incorporating its physicochemical, sequence evolution, biochemical, and structural disorder information into the general form of pseudo amino acid composition. It was observed by the rigorous jackknife test and independent dataset test that iMethyl-PseAAC was superior to any of the existing predictors in this area.

iTIS-PseTNC: A sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition

“…As demonstrated by a series of recent publications [9][10][11][12][13][14][15][16][17][18] and summarized in a comprehensive review [19], to develop a really useful statistical predictor for a biological system, one needs to go through the following five steps: (i) select or construct a valid benchmark dataset to train and test the predictor; (ii) represent the samples with an effective formulation that can truly reflect their intrinsic correlation with the target to be predicted; (iii) introduce or develop a powerful algorithm to conduct the prediction; (iv) properly perform cross-validation to objectively evaluate the anticipated prediction accuracy; (v) establish a user-friendly web server for the predictor that is accessible to the public. Below, let us elaborate how to deal with these steps one by one.…”

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

“…Compared with other learning machines, SVM boasts its structural risk minimization (RSM) principle and has desirable generalization performance. It has demonstrated good performance in model estimation problems by numerous successful applications [12][13][14][15][16]. The basic theory of SVM will be briefly reviewed in the following.…”

“…The prediction accuracies of methylation sites were improved due to the participation of some features of amino acid residues in predictor modeling. By introducing physicochemical, sequence evolution, biochemical, amino acid composition and structural disorder information of protein sequences into a SVM model, Chou et al developed a predictor for arginine and lysine methylation site prediction with improved performance [16].…”

Improving N6-methyladenosine site prediction with heuristic selection of nucleotide physical–chemical properties

“…Till now, several computational methods have been developed to predict post-translational modification sites, such as lysine ubiquitination sites [25], enzymes catalytic sites [26], lysine succinylation sites [27], and methylation sites [28][29][30][31][32], from primary sequences. Among these methods, Chen et al performed a pioneering work and developed a first m 6 A sites predictor called iRNA-Methyl [29].…”