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PhosphoEffect: Prioritizing Variants On or Adjacent to Phosphorylation Sites through Their Effect on Kinase Recognition Motifs.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Cole, Stephen 
Prabakaran, Sudhakaran  ORCID logo  https://orcid.org/0000-0002-6527-1085

Abstract

Phosphorylation sites often have key regulatory functions and are central to many cellular signaling pathways, so mutations that modify them have the potential to contribute to pathological states such as cancer. Although many classifiers exist for prioritization of coding genomic variants, to our knowledge none of them explicitly account for the alteration or creation of kinase recognition motifs that alter protein structure, function, regulation of activity, and interaction networks through modifying the pattern of phosphorylation. We present a novel computational pipeline that uses a random forest classifier to predict the pathogenicity of a variant, according to its direct or indirect effect on local phosphorylation sites and the predicted functional impact of perturbing a phosphorylation event. We call this classifier PhosphoEffect and find that it compares favorably and with increased accuracy to the existing classifier PolyPhen 2.2.2 when tested on a dataset of known variants enriched for phosphorylation sites and their neighbors.

Description

Keywords

Biochemistry, Omics

Journal Title

iScience

Conference Name

Journal ISSN

2589-0042
2589-0042

Volume Title

23

Publisher

Elsevier BV