Increasing the precision of orthology-based complex prediction through network alignment.

Roland A Pache,Patrick Aloy

doi:10.7717/peerj.413

Abstract

Macromolecular assemblies play an important role in almost all cellular processes. However, despite several large-scale studies, our current knowledge about protein complexes is still quite limited, thus advocating the use of in silico predictions to gather information on complex composition in model organisms. Since protein–protein interactions present certain constraints on the functional divergence of macromolecular assemblies during evolution, it is possible to predict complexes based on orthology data. Here, we show that incorporating interaction information through network alignment significantly increases the precision of orthology-based complex prediction. Moreover, we performed a large-scale in silico screen for protein complexes in human, yeast and fly, through the alignment of hundreds of known complexes to whole organism interactomes. Systematic comparison of the resulting network alignments to all complexes currently known in those species revealed many conserved complexes, as well as several novel complex components. In addition to validating our predictions using orthogonal data, we were able to assign specific functional roles to the predicted complexes. In several cases, the incorporation of interaction data through network alignment allowed to distinguish real complex components from other orthologous proteins. Our analyses indicate that current knowledge of yeast protein complexes exceeds that in other organisms and that predicting complexes in fly based on human and yeast data is complementary rather than redundant. Lastly, assessing the conservation of protein complexes of the human pathogen Mycoplasma pneumoniae, we discovered that its complexes repertoire is different from that of eukaryotes, suggesting new points of therapeutic intervention, whereas targeting the pathogen’s Restriction enzyme complex might lead to adverse effects due to its similarity to ATP-dependent metalloproteases in the human host.

Highlights

Almost every major process in a cell, such as replication, transcription, translation and degradation, is carried out not by single proteins, but by macromolecular complexes, regulated through intricate networks of protein–protein interactions
To test whether the incorporation of interaction information through network alignment can decrease the number of false positives and increase the precision of orthology-based complex prediction, we compared the performance of the orthologs approach to that of NetAligner (Pache & Aloy, 2012) in predicting yeast protein complexes based on human complexes and vice versa through complex to interactome alignment (Fig. 1A)
We found that incorporating interaction data through network alignment significantly increases the precision of orthology-based complex prediction

Summary

Introduction

Almost every major process in a cell, such as replication, transcription, translation and degradation, is carried out not by single proteins, but by macromolecular complexes, regulated through intricate networks of protein–protein interactions. Many small-scale studies have identified protein complexes in yeast and human, which have been collected in the public databases MPACT (Guldener et al, 2006) and CORUM (Ruepp et al, 2010), respectively. Several studies have shown that protein complexes in yeast are significantly enriched in essential genes (Dezso, Oltvai & Barabasi, 2003; Hart, Lee & Marcotte, 2007; Wang et al, 2009; Pache, Babu & Aloy, 2009). To discover the molecular details of how individual proteins function together as macromolecular assemblies, follow-up initiatives have aimed at identifying those complexes that are suitable for structural studies by combining systematic bioinformatics and experimental validation strategies (Pache & Aloy, 2008; Brooks et al, 2010)

Methods

Results

Conclusion