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

Matteo Lo Monte | Zendy; Candida Manelfi | Zendy; Marica Gemei | Zendy; Daniela Corda | Zendy; Andrea R. Beccari | Zendy

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

Author(s) -

Matteo Lo Monte,

Candida Manelfi,

Marica Gemei,

Daniela Corda,

Andrea R. Beccari

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty159

Subject(s) - bootstrapping (finance) , computer science , principal component analysis , computational biology , adp ribosylation , chromatin , machine learning , artificial intelligence , nad+ kinase , chemistry , biology , biochemistry , dna , mathematics , enzyme , econometrics

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.

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