ADPredict: ADP-ribosylation site prediction based on physicochemical and structural descriptors

Abstract

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.

Fig. 1.

Schematic framework of ADPredict development. On the left, the vertical labels list the main stages of the study; relative itemized details follow. On the right, a diagram schematizes the activity flow

Fig. 2.

ADP-ribosylated site distribution among the training set proteins. Yellow dots mark the percentage of proteins reporting one or up to three modifications. Blue and orange lines refer to the count of modified aspartic acid and glutamic acid, respectively; gray bars display the percentage of proteins (left y-axis) with a certain number of modifications; and the green line represents the cumulative curve (right y-axis)

Fig. 3.

Cross-validation (a)–(c) and Benchmark (d)–(f) ROC curves. (a) Primary sequence-based models, (b) secondary structure-based models and (c) 3-D based model L1O results. Comparative analysis of the ADPredict and, ModPred performances in predicting (d) Yu, (e) Kraus and (f) Nielsen datasets. ModPred PSSM performance is evaluated for the Yu dataset only

Fig. 4.

ADPredict web application homepage semi-screenshot