Predicting motif mimicry in viruses

Abstract

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 interactomes of various organisms. However, the PPI networks 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. Here, a new computational pipeline (SLiMEnrich) was designed to assess how well a given source of PPI data captures DMIs and thus, by inference, how useful that data should be for SLiM discovery. To help system biologists choose appropriate methods for predicting different types of interactions, a comparison study of existing high-throughput PPI datasets was performed. PPI data, SLiM predictions, domain composition and known SLiM-domain binding partnerships were integrated to identify possible DMI and DDI within interactomes. SLiMEnrich identified PPI data that were enriched for DMI or DDI by randomising the PPI within the network to generate a background expectation. Moreover, it was found that host-pathogen PPI data can be used to study molecular mimicry in viruses and to discover novel SLiMs. An in-silico peptide exchange approach was developed and applied to provide additional validation of predicted mimicry candidates. Despite limitations of this technique in large-scale validation of predicted SLiMs and DMIs, peptide exchange simulations identified a few high-confidence SLiMs that are likely to bind known structures and therefore constitute strong candidates for molecular mimicry by human viruses.