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

Lo Monte, Matteo
Manelfi, Candida
Gemei, Marica
Corda, Daniela
Beccari, Andrea Rosario
Hancock, John

¹Institute of Protein Biochemistry, National Research Council, Naples, Italy
²Dompé Farmaceutici SpA, L'Aquila
³Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Milano, Italy

The authors wish it to be known that, in their opinion, Matteo Lo Monte and Candida Manelfi authors should be regarded as Joint First Authors.

To whom correspondence should be addressed. Email: [email protected]

Received September 05, 2017; revised on January 09, 2018; accepted March 14, 2018

Bioinformatics 34(15):p 2566-2574, August 1, 2018. | DOI: 10.1093/bioinformatics/bty159

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.