Predicts phosphoglycerylation sites from protein sequences using composition of k-spaced amino acid pairs (CKSAAP). CKSAAP_PhoglySite is a predictor that outperforms the existing phosphoglycerylation sites predictor Phogly-PseAAC significantly. The package also contains an imbalanced fuzzy support vector machine (SVM) algorithm developed to construct a stable classifier for predicting phosphoglycerylation sites. This method can also be applied to predict the other types of post-translational modification sites.
Predicts lysine sites in proteins. iPGK-PseAAC is a web app developed to identify lysine phosphoglycerylation sites in proteins by incorporating with four different tiers of amino acid pairwise coupling information, where tiers 1, 2, 3, and 4 refer to the amino acid pairwise couplings between all the 1st, 2nd, 3rd, and 4th most contiguous residues along a protein segment, respectively.
A predictor for identifying the lysine phosphoglycerylated sites in proteins. The benchmark dataset S was entirely derived from experiments. The peptides after deleting homology ones in the benchmark dataset were formulated into 14-D feature vectors and the predictor Phogly–PseAAC based on center nearest neighbor algorithm has shown excellent performance. Meanwhile an online webserver was developed for the predictor which would facilitate the use for the biologists.