Self-similarity of human protein interaction networks: a novel strategy of distinguishing proteins

Abstract

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.

Computational network biology: Data, models, and applications

“…Further, simulation results of activity spreading on the brain network indicate that the fractal dimension would be a very relevant factor in controlling the spreading threshold value [304]. Additionally, the fractal and self-similar properties are widely applied in biological systems, including development of the algorithm to count the motifs in the biological networks [265], prediction of essential genes [305], and measuring the importance of proteins [306]. However, different from the previous view [288] in which metabolic networks exhibit self-similarity (fractality) using a box-counting method, Takemoto [307] found that metabolic networks are almost non-fractal for the increase in network density of the latest metabolic network data, highlighting the needs for a more suitable definition and careful examination of network fractal and self-similarity properties.…”

The Significance of Key Proteins in the RAS Signaling Pathway: Implications for Cancer and Therapeutic Targets

“…We suggested that proteins in the central position, particularly those engaged in sensory functions, could be potential targets for therapeutic intervention. Furthermore, we identified proteins near the network center as potential targets for therapeutic intervention [15][16][17]. Based on previous analyses, our current investigation focused on zone two, abundant in proteins linked to diverse cellular processes.…”

“…As a result, we deduced that assessing PPIs within a metric space, particularly considering zones relative to the center, can unveil pivotal distinctions between PPI networks observed in healthy and pathological tissues. Following the methodology outlined in our prior research, our continuing investigation proposes that core zones within specific human protein interaction networks exhibit a noteworthy enrichment of essential proteins and recognized drug targets [14][15][16][17].…”

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“…Our study found that the zone closest to the network center contains essential proteins specialized for certain basic functions. Furthermore, we identified proteins located near the network center as potential targets for therapeutic intervention [19][20][21].…”