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CoPhosK: A Method for Comprehensive Kinase Substrate Annotation using Co-phosphorylation Analysis

Published: 04 September 2019 Publication History

Abstract

We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naïve Bayes framework with priors of known kinase-substrate associations (KSAs) to generate its predictions. Through the mining of MS data for the collective dynamic signatures of the kinases' substrates revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information (e.g. sequences, structures and interactions) using publically available MS data, including breast, colon, and ovarian cancer models. The benchmarking reveals that co-phosphorylation analysis can significantly improve prediction performance when static information is available (about 35% of sites) while providing reliable predictions for the remainder, thus tripling the KSAs available from the experimental MS data providing to a comprehensive and reliable characterization of the landscape of kinase-substrate interactions well beyond current limitations.

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  1. CoPhosK: A Method for Comprehensive Kinase Substrate Annotation using Co-phosphorylation Analysis

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        cover image ACM Conferences
        BCB '19: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
        September 2019
        716 pages
        ISBN:9781450366663
        DOI:10.1145/3307339
        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Published: 04 September 2019

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        Author Tags

        1. cancer
        2. co-phosphorylation network
        3. kinase-substrate association
        4. phosphoproteomics
        5. phosphorylation
        6. systems biology

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        BCB '19 Paper Acceptance Rate 42 of 157 submissions, 27%;
        Overall Acceptance Rate 254 of 885 submissions, 29%

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