An in silico proteomics screen to predict and prioritize protein-protein interactions dependent on...

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.

We used as an initial example the interaction between the Src homology 2 (SH2) domains of the adaptor proteins CT10 regulator of kinase (CRK) and CRK-like (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 >9000 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.

Supplementary data are available at Bioinformatics online.