Global multiple protein-protein interaction network alignment by combining pairwise network alignments.

Juris Puchin,Jakob Dohrmann,Rahul Singh

doi:10.1186/1471-2105-16-s13-s11

Abstract

BackgroundA wealth of protein interaction data has become available in recent years, creating an urgent need for powerful analysis techniques. In this context, the problem of finding biologically meaningful correspondences between different protein-protein interaction networks (PPIN) is of particular interest. The PPIN of a species can be compared with that of other species through the process of PPIN alignment. Such an alignment can provide insight into basic problems like species evolution and network component function determination, as well as translational problems such as target identification and elucidation of mechanisms of disease spread. Furthermore, multiple PPINs can be aligned simultaneously, expanding the analytical implications of the result. While there are several pairwise network alignment algorithms, few methods are capable of multiple network alignment.ResultsWe propose SMAL, a MNA algorithm based on the philosophy of scaffold-based alignment. SMAL is capable of converting results from any global pairwise alignment algorithms into a MNA in linear time. Using this method, we have built multiple network alignments based on combining pairwise alignments from a number of publicly available (pairwise) network aligners. We tested SMAL using PPINs of eight species derived from the IntAct repository and employed a number of measures to evaluate performance. Additionally, as part of our experimental investigations, we compared the effectiveness of SMAL while aligning up to eight input PPINs, and examined the effect of scaffold network choice on the alignments.ConclusionsA key advantage of SMAL lies in its ability to create MNAs through the use of pairwise network aligners for which native MNA implementations do not exist. Experiments indicate that the performance of SMAL was comparable to that of the native MNA implementation of established methods such as IsoRankN and SMETANA. However, in terms of computational time, SMAL was significantly faster. SMAL was also able to retain many important characteristics of the native pairwise alignments, such as the number of aligned nodes and edges, as well as the functional and homologene similarity of aligned nodes. The speed, flexibility and the ability to retain prior correspondences as new networks are aligned, makes SMAL a compelling choice for alignment of multiple large networks.

Highlights

As part of the investigations presented in this paper, we demonstrate the multiple network alignments obtained with the proposed approach by utilizing prior alignments from SMETANA [30], IsoRankN [31], PINALOG [27] and NETAL [28] as inputs
Effect of the scaffold selection on the SMAL multiple network alignment (MNA) To demonstrate measure consistency, we compared the performance of SMAL to that of pairwise network aligners
As the MNAs are invariant to the order in which pairwise alignment are computed, SMAL can be enriched with additional protein-protein interaction networks (PPIN) at any point in time

Summary

Introduction

With the advent of high-throughput experimental techniques such as yeast two-hybrid screening [1,2,3] and coimmunoprecipitation coupled mass spectrometry [4,5] there has been a substantial increase in the data available understanding of the fundamental biological processes from the molecular to the evolutionary scales [19], PPIdata can be invaluable in translational contexts, for instance, by explaining mechanisms of infection spread [13,20,21,22,23] and through discovery of novel targets, such as dependency factors [24].The complexity of protein-protein interactions coupled with the volume and noisy nature of PPI data, underline the acute need for automated analysis of PPIs. An important problem in PPIN analysis, much like with traditional sequence-based genomics, is the establishment of correspondences between proteins and interactions across different species. This can be accomplished through PPI network alignment, where, by incorporating network topology, notions of protein similarity and other related data, members of one PPIN are matched with their closest analogues in another PPIN. A wealth of protein interaction data has become available in recent years, creating an urgent need for powerful analysis techniques In this context, the problem of finding biologically meaningful correspondences between different protein-protein interaction networks (PPIN) is of particular interest. While there are several pairwise network alignment algorithms, few methods are capable of multiple network alignment

Methods

Results

Conclusion