English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Optimized null model for protein structure networks

Milenković, T., Filippis, I., Lappe, M., & Pržulj, N. (2009). Optimized null model for protein structure networks. PLoS ONE, 4(6), e5967-e5967. doi:10.1371/journal.pone.0005967.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-7D6A-9 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-7D6B-7
Genre: Journal Article

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Milenković, Tijana, Author
Filippis, Ioannis1, Author
Lappe, Michael2, Author              
Pržulj, Nataša, Author
Affiliations:
1Max Planck Society, ou_persistent13              
2Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433554              

Content

show
hide
Free keywords: -
 Abstract: Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by comparing various RIG definitions against a series of network models.

Details

show
hide
Language(s): eng - English
 Dates: 2009-06-26
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: PLoS ONE
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 4 (6) Sequence Number: - Start / End Page: e5967 - e5967 Identifier: ISSN: 1932-6203