Friday, 15 June 2018

Evaluation of protein-protein interaction detection methods as a source of capturing domain-motif interactions

Sobia Idrees, Richard J Edwards

This work was presented at the Sydney Bioinformatics Research Symposium 2018. (Abstract below.) Click on thumbnail for full resolution PDF.


One of the main pursuits in proteomics is to understand the complex network of protein-protein Interactions (PPI) that underpin biological processes. Two major classes of PPI are domain-domain interactions (DDI) between globular proteins, and domain-motif interactions (DMI) between a globular domain and a short linear motif (SLiM) in its partner. Advances in high-throughput experimental techniques have been applied at large-scale in an attempt to characterise the interactome of various organisms. However, PPI networks being identified by these high-throughput experiments have low resolution as compared to low-throughput technologies, such as protein co-crystallization. Furthermore, large-scale approaches may be poor at capturing low affinity or transient interactions, which includes the majority of known DMI. To date, several studies have been conducted to identify how well these PPI data can capture protein complexes, but the ability of high-throughput PPI-detection methods to capture DMI remains a largely unanswered question.

To help system biologists choose appropriate methods for predicting different types of interactions, we conducted a comprehensive comparison study on existing high-throughput PPI datasets. We have integrated PPI data, SLiM predictions, domain compositions and known SLiM-domain binding partnerships to identify possible DMI and DDI within interactomes. We identify PPI data that are enriched for DMI or DDI versus a background expectation generated by randomising the PPI within the network. Despite returning relatively few experimentally validated DMI when compared to interaction databases, we present evidence that high-throughput PPI data is enriched for DMI and thus potentially useful for the prediction of novel SLiMs. We discuss the relative merits of co-fractionation followed by mass spectrometry (CoFrac-MS), affinity purification coupled mass spectrometry (AP-MS), and yeast two hybrid (Y2H) for capturing DMI and DDI, as well as potential quality versus quantity trade-offs in DMI prediction.

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