Computational Methods for Predicting Post-Translational Modification Sites

<p>Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling.-&nbsp;PLDMS: Phosphopeptide Library Dephosphorylation followed by Mass Spectrometry Analysis to Determine the Specificity of Phosphatases for Dephosphorylation Site Sequences.-&nbsp;FEPS: A tool for Feature Extraction from Protein Sequence.-&nbsp;A pre-trained ELECTRA model for Kinase-specific Phosphorylation Site Prediction.-&nbsp;&nbsp;iProtGly-SS: A Tool to Accurately Predict Protein Glycation Site Using structural-based Features.-&nbsp;Functions of Glycosylation and Related Web Resources for its Prediction.-&nbsp;Analysis of Post-Translational Modifications in Arabidopsis Proteins and Metabolic Pathways using the FAT-PTM Database.-&nbsp;Bioinformatic Analyses of Peroxiredoxins and RF-Prx: A RANDOM FOREST-BASED PREDICTOR and classifier for Prxs.-&nbsp;Computational prediction of N- and O-linked glycosylation sites for human and mouse proteins.-&nbsp;iPTMnet RESTful API for Post-Translational Modification Network Analysis.-&nbsp;Systematic Characterization of Lysine Post-Translational Modification Sites using MUscADEL.-&nbsp;Enhancing the Discovery of Functional Post-Translational Modification Sites with Machine Learning Models – Development, Validation, and Interpretation.-&nbsp;Exploration of Protein Post-Translational Modification Landscape and Crosstalk with CrossTalkMapper.-&nbsp;PTM-X: Prediction of Post-Translational Modification Crosstalk Within and Across Proteins.-&nbsp;Deep Learning-Based Advances In Protein Post-Translational Modification Site And Protein Cleavage Prediction.</p>