dbPTM is a universal resource for protein post-translational modifications (PTMs). Due to the importance of protein post-translational modifications (PTMs) in regulating biological processes, the dbPTM was developed as a comprehensive database by integrating experimentally verified PTMs from several databases and annotating the potential PTMs for all UniProtKB protein entries.
In this 10th year anniversary, dbPTM aims to be a universal resource not only accumulating comprehensive dataset of experimentally verified PTMs supported with literatures but also providing an integrative interface for accessing all available databases and tools associated with PTM analyses.
In addition to collecting experimental PTM data from 14 public databases, this update manually curates more than 12,000 modified peptides, including the emerging S-nitrosylation, S-glutathionylation and succinylation, from approximately 500 research articles which were retrieved by a text mining approach. With an increasing number of various PTM prediction methods, this work compiles non-homologous benchmark datasets to evaluate the predictive power among online PTM prediction tools in order to provide suggestions to users with the need to predict PTM sites with high sensitivity (Sn), high specificity (Sp), or balanced Sn and Sp.
An increasing interest in structural investigation of PTM substrate sites motivated us to map all experimental PTM peptides to PDB protein entries by sequence identity, which enables users to study spatial amino acid composition, solvent-accessible surface area, and spatially neighboring amino acids for PTM substrate sites based on tertiary structures. In particular, the side chain orientations of the amino acids spatially surrounding the PTM substrate sites were determined for exploring the functional roles and drug-binding effects of the spatially neighboring amino acids to the substrate sites. Due to the annotation of drug binding in PDB, this update has identified over 1,100 PTM sites which are associated with the drug binding. Furthermore, this update has integrated the information of metabolic pathways and protein-protein interactions (PPIs) to implement the network analysis for a group of genes/proteins.