MOTIVATION: ADP-ribosylation is a post-translational modification (PTM) 
implicated in several crucial cellular processes, ranging from regulation of DNA 
repair and chromatin structure to cell metabolism and stress responses. To date, 
a complete understanding of ADP-ribosylation targets and their modification 
sites in different tissues and disease states is still lacking. Identification 
of ADP-ribosylation sites is required to discern the molecular mechanisms 
regulated by this modification. This motivated us to develop a computational 
tool for the prediction of ADP-ribosylated sites.
RESULTS: Here, we present ADPredict, the first dedicated computational tool for 
the prediction of ADP-ribosylated aspartic and glutamic acids. This predictive 
algorithm is based on (i) physicochemical properties, (ii) in-house designed 
secondary structure-related descriptors and (iii) three-dimensional features of 
a set of human ADP-ribosylated proteins that have been reported in the 
literature. ADPredict was developed using principal component analysis and 
machine learning techniques; its performance was evaluated both internally via 
intensive bootstrapping and in predicting two external experimental datasets. It 
outperformed the only other available ADP-ribosylation prediction tool, ModPred. 
Moreover, a novel secondary structure descriptor, HM-ratio, was introduced and 
successfully contributed to the model development, thus representing a promising 
tool for bioinformatics studies, such as PTM prediction.
AVAILABILITY AND IMPLEMENTATION: ADPredict is freely available at 
www.ADPredict.net.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.