Human protein-protein interaction networks: A topological comparison review

Abstract

Protein-Protein Interaction Networks aim to model the interactome, providing a powerful tool for understanding the complex relationships governing cellular processes. These networks have numerous applications, including functional enrichment, discovering cancer driver genes, identifying drug targets, and more. Various databases make protein-protein networks available for many species, including Homo sapiens. This work topologically compares four Homo sapiens networks using a coarse-to-fine approach, comparing global characteristics, sub-network topology, specific nodes centrality, and interaction significance. Results show that the four human protein networks share many common protein-encoding genes and some global measures, but significantly differ in the interactions and neighbourhood. Small sub-networks from cancer pathways performed better than the whole networks, indicating an improved topological consistency in functional pathways. The centrality analysis shows that the same genes play different roles in different networks. We discuss how studies and analyses that rely on protein-protein networks for humans should consider their similarities and distinctions.

Keywords: Centrality measures; Complex systems; Network topology; PPIN; Protein–protein interaction networks.

Figure 1

Edge score distribution for all networks with scores. The median $\tilde{x}$ and mean $\bar{x}$ are closer in significant networks, normalizing the distribution. STRING shows the biggest difference between the significant and complete versions, since most of the edges score in the complete network are below 40%.

Figure 2

Nodes intersection. All networks nodes intersection (a). Trio of networks nodes intersections (b), (c), (d), and (e). The networks have overlapping protein names while maintaining a significant amount of exclusive proteins.

Figure 3

Edges (protein interactions) intersection. All networks edges intersection (a). Trio of networks edges intersections (b), (c), (d), and (e). Only 0.6% of the protein interactions are shared with all the PPINs, while the majority of interactions are unique or shared in pairs of networks.

Figure 4

Scale free characterization. The four PPINs share a similar scale free degree distribution, especially the pairs (a) HINT & (b) IntAct and (c) Reactome & (d) STRING.

Figure 5

Small world behaviour: the X-axis shows the eccentricity value, and the Y-axis shows the percentage of nodes with such value. The four PPINs show a small world behaviour, even with thousands of nodes, the average shortest path is smaller than 4.

Figure 6

Communities: the X-axis shows the number of communities found, and the Y-axis shows the number of nodes in each community. The community length distribution size is similar, with few large and many small communities.

Figure 7

Clustering. The pair (a) HINT & (b) IntAct have almost equal distribution. (c) Reactome & (e) STRING are similar, while being distinct from (a) HINT & (b) IntAct.

Figure 8

Network resilience. The PPINs show a behaviour expected from scale free networks: resilient to random removal and fragile to hub removal. (d) STRING is the most resilient due to its degree assortativity.

Figure 9

Assortativity: the X-axis represents all nodes with the same degree K, and the Y-axis shows the average neighbours' degree of nodes of these nodes. The PPINs do not follow a linear distribution. HINT & IntAct follows the same trend, while Reactome and STRING have unique comportment.

Figure 10

Cancer pathways networks: edges contained in other networks.

Figure 11

Percentile position for individual genes.