graph-theoretical-analysis-modeled-protein-protein-interaction

Graph-theoretical Analysis of a Modeled Protein-protein Interaction Network in the RAS Signaling Pathway

The RAS signaling pathway is a fundamental regulator of cellular growth, proliferation, and survival. Dysregulation of this pathway is strongly implicated ...

University of the Western Cape

In the post-genomic era, a central and overarching question in the analysis of protein-protein interaction networks continues to be whether biological characteristics and functions of proteins such as lethality, physiological malfunctions and malignancy are intimately linked to the topological role proteins play in the network as a mathematical structure. One of the key features that have implicitly been presumed is the existence of hubs, highly connected proteins considered to play a crucial role in biological networks. We explore the structure of protein interaction networks of a number of organisms as metric spaces and show that hubs are non randomly positioned and, from a distance point of view, centrally located.

Protein interaction networks as metric spaces: a novel perspective on distribution of hubs.

We have recently shown by formally modelling human protein interaction networks (PINs) as metric spaces and classified proteins into zones based on their distance from the topological centre that hub proteins are primarily centrally located. We also showed that zones closest to the network centre are enriched for critically important proteins and are also functionally very specialised for specific 'house keeping' functions. We proposed that proteins closest to the network centre may present good therapeutic targets. Here, we present multiple pieces of novel functional evidence that provides strong support for this hypothesis.

Modelling human protein interaction networks as metric spaces has potential in disease research and drug target discovery.

The successful determination of reliable protein interaction networks (PINs) in several species in the post-genomic era has hitherto facilitated the quest to understanding systems and structural properties of such networks. It is envisaged that a clearer understanding of their intrinsic topological properties would elucidate evolutionary and biological topography of organisms. This, in turn, may inform the understanding of diseases' aetiology. By analysing sub-networks that are induced in various layers identified by zones defined as distance from central proteins, we show that zones of human PINs display self-similarity patterns. What is observed at a global level is repeated at lower levels of inducement. Furthermore, it is observed that these levels of strength point to refinement and specialisations in these layers. This may point to the fact that various levels of representations in the self-similarity phenomenon offer a way of measuring and distinguishing the importance of proteins in the network. To consolidate our findings, we have also considered a gene co-expression network and a class of gene regulatory networks in the same framework. In all cases, the phenomenon is significantly evident. In particular, the truly unbiased regulatory networks show finer level of articulation of self-similarity.

Article	Year
Protein interaction networks as metric spaces: a novel perspective on distribution of hubs. BMC systems biology\| PubMed ID: 24438364	2014
Modelling human protein interaction networks as metric spaces has potential in disease research and drug target discovery. BMC systems biology\| PubMed ID: 24929653	2014
Self-similarity of human protein interaction networks: a novel strategy of distinguishing proteins. Scientific reports\| PubMed ID: 25720740	2015

Eric Mwambene

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