An in silico proteomics screen to predict and prioritize protein-protein interactions dependent on post-translationally modified motifs

An in silico proteomics screen to predict and prioritize protein-protein interactions dependent... Abstract Motivation The development of proteomic methods for the characterization of domain/motif interactions has greatly expanded our understanding of signal transduction. However, proteomics-based binding screens have limitations including that the queried tissue or cell type may not harbor all potential interacting partners or post-translational modifications (PTMs) required for the interaction. Therefore, we sought a generalizable, complementary in silico approach to identify potentially novel motif and PTM-dependent binding partners of high priority. Results We used as an initial example the interaction between the SH2 domains of the adaptor proteins CRK and CRKL and phosphorylated-YXXP motifs. Employing well-curated, publicly-available resources, we scored and prioritized potential CRK/CRKL-SH2 interactors possessing signature characteristics of known interacting partners. Our approach gave high priority scores to 102 of the more than 9,000 YXXP motif-containing proteins. Within this 102 were 21 of the 25 curated CRK/CRKL-SH2 binding partners showing a more than 80-fold enrichment. Several predicted interactors were validated biochemically. To demonstrate generalized applicability, we used our workflow to predict protein-protein interactions dependent upon motif-specific arginine methylation. Our data demonstrate the applicability of our approach to, conceivably, any modular binding domain that recognizes a specific post-translationally modified motif. Contact bballif@uvm.edu Supplementary Information Supplementary data are available at Bioinformatics Online. © The Author(s) (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bioinformatics Oxford University Press

An in silico proteomics screen to predict and prioritize protein-protein interactions dependent on post-translationally modified motifs

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Publisher
Oxford University Press
Copyright
© The Author(s) (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
ISSN
1367-4803
eISSN
1460-2059
D.O.I.
10.1093/bioinformatics/bty434
Publisher site
See Article on Publisher Site

Abstract

Abstract Motivation The development of proteomic methods for the characterization of domain/motif interactions has greatly expanded our understanding of signal transduction. However, proteomics-based binding screens have limitations including that the queried tissue or cell type may not harbor all potential interacting partners or post-translational modifications (PTMs) required for the interaction. Therefore, we sought a generalizable, complementary in silico approach to identify potentially novel motif and PTM-dependent binding partners of high priority. Results We used as an initial example the interaction between the SH2 domains of the adaptor proteins CRK and CRKL and phosphorylated-YXXP motifs. Employing well-curated, publicly-available resources, we scored and prioritized potential CRK/CRKL-SH2 interactors possessing signature characteristics of known interacting partners. Our approach gave high priority scores to 102 of the more than 9,000 YXXP motif-containing proteins. Within this 102 were 21 of the 25 curated CRK/CRKL-SH2 binding partners showing a more than 80-fold enrichment. Several predicted interactors were validated biochemically. To demonstrate generalized applicability, we used our workflow to predict protein-protein interactions dependent upon motif-specific arginine methylation. Our data demonstrate the applicability of our approach to, conceivably, any modular binding domain that recognizes a specific post-translationally modified motif. Contact bballif@uvm.edu Supplementary Information Supplementary data are available at Bioinformatics Online. © The Author(s) (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

BioinformaticsOxford University Press

Published: Jun 1, 2018

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