Lysine glycation site detection software tools | Post-translational modification data analysis
Similar to the regular enzymatic glycosylation, glycation also attaches a sugar molecule to a peptide, but does not need the help of an enzyme. Glycation may occur both inside and outside the host body, and will compete with the glycosylation procedure for functional regulation of mature protein products. The glycated residues do not show significant patterns, which make both in silico sequence-level predictors and wet-lab validations a major challenge.
Predicts glycation of ε amino groups of lysines in mammalian proteins. NetGlycate combines 60 artificial neural networks in a balloting procedure. It can be used by researchers to select relevant proteins for the investigation of glycation sites. The tool returns all predictions graphically illustrated. The user can specify not more than 2,000 sequences and 200,000 amino acids in one submission.
Consists of a protein lysine glycation site identification method. iProtGly‐SS is an online predictor based on a set of propensity-based features extracted from amino acid sequence, evolutionary physico-chemical properties and structural information. This method uses a selection of algorithm to find the optimal set of features and determine the optimal size of window for glycation detection.
Recognizes the lysine glycated in proteins. Gly-PseAAC is based on a Support Vector Machine (SVM) method. It consists of four main analytical processes: data pre-processing, feature encoding, model learning and evaluation and glycation prediction. This tool was used to generate effective mathematical features from the peptide samples based on the intrinsic target associations.
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