Global topological features of cancer proteins in the human interactome.

Motivation: The study of interactomes, or networks of protein-protein interactions, is increasingly providing valuable information on biological systems. Here we report a study of cancer proteins in an extensive human protein-protein interaction network constructed by computational methods.

Results: We show that human proteins translated from known cancer genes exhibit a network topology that is different from that of proteins not documented as being mutated in cancer.

Cluster analysis of networks generated through homology: automatic identification of important protein communities involved in cancer metastasis.

Background: Protein-protein interactions have traditionally been studied on a small scale, using classical biochemical methods to investigate the proteins of interest. More recently large-scale methods, such as two-hybrid screens, have been utilised to survey extensive portions of genomes. Current high-throughput approaches have a relatively high rate of errors, whereas in-depth biochemical studies are too expensive and time-consuming to be practical for extensive studies.

Structural context of exons in protein domains: implications for protein modelling and design.

Intron boundaries were extracted from genomic data and mapped onto single-domain human and murine protein structures taken from the Protein Data Bank. A first analysis of this set of proteins shows that intron boundaries prefer to be in non-regular secondary structure elements, while avoiding alpha-helices and beta-strands. This fact alone suggests an evolutionary model in which introns are constrained by protein structure, particularly by tertiary structure contacts.