Dataset Information

Evidence for the additions of clustered interacting nodes during the evolution of protein interaction networks from network motifs.

ABSTRACT:

Background

High-throughput screens have revealed large-scale protein interaction networks defining most cellular functions. How the proteins were added to the protein interaction network during its growth is a basic and important issue. Network motifs represent the simplest building blocks of cellular machines and are of biological significance.

Results

Here we study the evolution of protein interaction networks from the perspective of network motifs. We find that in current protein interaction networks, proteins of the same age class tend to form motifs and such co-origins of motif constituents are affected by their topologies and biological functions. Further, we find that the proteins within motifs whose constituents are of the same age class tend to be densely interconnected, co-evolve and share the same biological functions, and these motifs tend to be within protein complexes.

Conclusions

Our findings provide novel evidence for the hypothesis of the additions of clustered interacting nodes and point out network motifs, especially the motifs with the dense topology and specific function may play important roles during this process. Our results suggest functional constraints may be the underlying driving force for such additions of clustered interacting nodes.

SUBMITTER: Liu Z

PROVIDER: S-EPMC3128043 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evidence for the additions of clustered interacting nodes during the evolution of protein interaction networks from network motifs.

Liu Zhongyang Z Liu Qijun Q Sun Hanchang H Hou Lin L Guo Hao H Zhu Yunping Y Li Dong D He Fuchu F

BMC evolutionary biology 20110520

<h4>Background</h4>High-throughput screens have revealed large-scale protein interaction networks defining most cellular functions. How the proteins were added to the protein interaction network during its growth is a basic and important issue. Network motifs represent the simplest building blocks of cellular machines and are of biological significance.<h4>Results</h4>Here we study the evolution of protein interaction networks from the perspective of network motifs. We find that in current prote ...[more]

PMID: 21595981

Similar Datasets

Project description:BackgroundComplex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions) and the extent of clustering (the tendency for a set of three nodes to be interconnected) are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics.ResultsHere we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks) provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics.Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks.ConclusionClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in unraveling the functional consequences of the structural properties of empirical biological systems and uncovering the mechanisms that drive these systems.

Project description:BACKGROUND:Cohesin is a macromolecular complex that links sister chromatids together at the metaphase plate during mitosis. The links are formed during DNA replication and destroyed during the metaphase-to-anaphase transition. In budding yeast, the 14S cohesin complex comprises at least two classes of SMC (structural maintenance of chromosomes) proteins - Smc1 and Smc3 - and two SCC (sister-chromatid cohesion) proteins - Scc1 and Scc3. The exact function of these proteins is unknown. RESULTS:Searches of protein sequence databases have revealed new homologs of cohesin proteins. In mouse, Mmip1 (Mad member interacting protein 1) and Smc3 share 99% sequence identity and are products of the same gene. A phylogenetic tree of SMC homologs reveals five families: Smc1, Smc2, Smc3, Smc4 and an ancestral family that includes the sequences from the Archaea and Eubacteria. This ancestral family also includes sequences from eukaryotes. A cohesion interaction network, comprising 17 proteins, has been constructed using two proteomic databases. Genes encoding six proteins in the cohesion network share a common upstream region that includes the MluI cell-cycle box (MCB) element. Pairs of the proteins in this network share common sequence motifs that could represent common structural features such as binding sites. Scc2 shares a motif with Chk1 (kinase checkpoint protein), that comprises part of the serine/threonine protein kinase motif, including the active-site residue. CONCLUSIONS:We have combined genomic and proteomic data into a comprehensive network of information to reach a better understanding of the function of the cohesin complex. We have identified new SMC homologs, created a new SMC phylogeny and identified shared DNA and protein motifs. The potential for Scc2 to function as a kinase - a hypothesis that needs to be verified experimentally - could provide further evidence for the regulation of sister-chromatid cohesion by phosphorylation mechanisms, which are currently poorly understood.

Dataset Information

Evidence for the additions of clustered interacting nodes during the evolution of protein interaction networks from network motifs.

Background

Results

Conclusions

Publications

Evidence for the additions of clustered interacting nodes during the evolution of protein interaction networks from network motifs.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets