Targeting the human cancer pathway protein interaction network by structural genomics

Abstract

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

Fig. 1.

The HCPIN is a Web-accessible database. It is designed for use by cancer biologists interested in assessing 3D protein structural information in the context of the protein interaction network. A, HCPIN home page. B, a snapshot of Networks view, visualizing protein-protein interactions with structure annotations. The outside ring represents the percentage of structural coverage. Green ring, experimental model is available with >99% sequence identities; yellow ring, homology model is available with >80% sequence identities. The Website provides tools for interactive analysis of the HCPIN. C, a snapshot of Proteins view, listing sequence information and PDB BLAST hits, summarizing all structural information available for the human HCPIN protein and its homologues and providing links to the corresponding PDB entries and other structure-function annotation information. D, a snapshot of Icon gallery, a collection of ribbon diagrams for each of the known structures and the structural models in the HCPIN.

Fig. 2.

Scatter plot of degree and betweenness measures for HCPIN proteins. Black, HCPIN proteins; red, proteins also listed in the Cancer Gene Census Database (1). EGFR, EGF receptor; CREBBP, CREB-binding protein; DMPK, dystrophia myotonica protein kinase; LCK, Lymphocyte cell-specific protein-tyrosine kinase; MPL, myeloproliferative leukemia protein; HSPCA, heat shock protein HSP 90-alpha.

Fig. 3.

Cross-talk between pathways. A, frequency of observing one protein in one or more of the seven KEGG signaling pathways. ∼20% of HCPIN pathway proteins are associated with two or more pathways. B, frequency of observing one HCPIN protein in one or more of seven pathway interaction subnets. >50% of HCPIN proteins are associated with two or more interaction subnets. C, frequency of observing one pathway protein in one or more pathway interaction subnets. The frequencies (1–7) are also labeled on the side of these pie charts.

Fig. 4.

Ribbon diagram of some HCPIN proteins/domains solved by NESG. At the bottom of each ribbon diagram, we have listed Swiss-Prot (SW) name, NESG target ID, PDB ID, residue coverage, and method used for structure determination.

Fig. 5.

A, percent residue coverage distributions for HCPIN proteins. Intracellular, proteins inside the cell; s/m, proteins predicted to be secreted or having at least a segment that is integral or transmembrane. B, box plots of size distributions of HCPIN proteins and HCPIN proteins with single domain coverage. Intracellular, proteins inside the cell; s/m, as defined above; intracellular-SD, intracellular proteins with single domain coverage, s/m-SD, proteins predicted to be secreted or having at least a segment that is transmembrane with single domain coverage. C, box plots of size distributions of HCPIN proteins with residue coverage. Intracellular-residue and s/m-residue, residue coverage of intracellular proteins and predicted secreted/membrane-associated proteins, respectively. Single domain and residue coverages are shown at high accuracy level. A similar distribution is observed at medium accuracy level. D, box plots of size distributions of full-length and targeted subregions of proteins selected by the NESG structural genomics project.

Fig. 6.

HCPIN target selection process. SEG regions, low complexity regions predicted by the program SEG (37). SignalP region, signaling peptide predicted by SignalP (32). TM region, transmembrane region predicted by TMHMM (33). C/U-region, structure covered (C) or uncovered (U) region. T-region, targeted region. Disordered regions are predicted based on mean hydrophobicity and net charge (38). E, E-value; HTP, high throughput.