Computational Methods for Predicting Post-Translational Modification Sites

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