Computational Methods for Predicting Post-Translational Modification Sites

Chapter 1 Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling

Chapter 2 PLDMS: Phosphopeptide Library Dephosphorylation Followed by Mass Spectrometry Analysis to Determine the Specificity of Phosphatases for Dephosphorylation Site Sequences.

Chapter 4 A Pretrained ELECTRA Model for Kinase-Specific Phosphorylation Site Prediction

Chapter 5 iProtGly-SS: A Tool to Accurately Predict Protein Glycation Site Using Structural-Based Features

Chapter 6 Functions of Glycosylation and Related Web Resources for Its Prediction

Chapter 7 Analysis of Posttranslational Modifications in Arabidopsis Proteins and Metabolic Pathways Using the FAT-PTM Database

Chapter 8 Bioinformatic Analyses of Peroxiredoxins and RF-Prx: A Random Forest-Based Predictor and Classifier for Prxs

Chapter 9 Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins

Chapter 10 iPTMnet RESTful API for Post-translational Modification Network Analysis

Chapter 11 Systematic Characterization of Lysine Post-translational Modification Sites Using MUscADEL

Chapter 12 Enhancing the Discovery of Functional Post-Translational Modification Sites with Machine Learning Models – Development, Validation, and Interpretation

Chapter 13 Exploration of Protein Posttranslational Modification Landscape and Cross Talk with CrossTalkMapper

Chapter 14 PTM-X: Prediction of Post-Translational Modification Crosstalk Within and Across Proteins

Chapter 15 Deep Learning–Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction