Human Cancer Protein-Protein Interaction Network: A Structural Perspective

Gozde Kar,Ozlem Keskin,Attila Gursoy,Ruth Nussinov

doi:10.1371/journal.pcbi.1000601

Abstract

Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network). The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%). We illustrate the interface related affinity properties of two cancer-related hub proteins: Erbb3, a multi interface, and Raf1, a single interface hub. The results reveal that affinity of interactions of the multi-interface hub tends to be higher than that of the single-interface hub. These findings might be important in obtaining new targets in cancer as well as finding the details of specific binding regions of putative cancer drug candidates.

Highlights

Protein–protein interaction networks provide valuable information in the understanding of cellular function and biological processes
Analysis of interface properties in iSPIN We present the interface properties of interactions such as the accessible surface area (ASA), planarity, gap volume index and residue composition at the interfaces in iSPIN
The experiments were performed using 10fold cross validation with several classifiers using four interface features; interface ASA, DASA, planarity and gap volume index. (See Methods for the details of the classification procedure) For example, using support vector machine (SVM) as the classifier algorithm, interfaces were ranked as cancer or noncancer related with an accuracy of 61%, 71% and 67% for leukemia, breast cancer and colorectal cancer, respectively

Summary

Introduction

Protein–protein interaction networks provide valuable information in the understanding of cellular function and biological processes. To characterize interactions with respect to their physical and chemical properties and in particular, to understand how a function is exerted, it is essential to include structural details in the networks; such details come from three dimensional protein structures and from protein interfaces. Interface characteristics are important in determining the specificity and strength of interactions. Physical interactions through interface residues determine whether the binding will be promiscuous or specific. The same or overlapping binding sites should be frequently and repeatedly used in hub proteins making them promiscuous [18] With this in mind, Kim et al [19] distinguished overlapping from non-overlapping interfaces in their structural interaction network to determine

Author Summary

Conclusion

Findings

Methods