↓ Skip to main content

PLOS

Structure of Protein Interaction Networks and Their Implications on Drug Design

Overview of attention for article published in PLoS Computational Biology, October 2009
Altmetric Badge

Mentioned by

blogs
1 blog

Citations

dimensions_citation
103 Dimensions

Readers on

mendeley
179 Mendeley
citeulike
17 CiteULike
Title
Structure of Protein Interaction Networks and Their Implications on Drug Design
Published in
PLoS Computational Biology, October 2009
DOI 10.1371/journal.pcbi.1000550
Pubmed ID
Authors

Takeshi Hase, Hiroshi Tanaka, Yasuhiro Suzuki, So Nakagawa, Hiroaki Kitano

Abstract

Protein-protein interaction networks (PINs) are rich sources of information that enable the network properties of biological systems to be understood. A study of the topological and statistical properties of budding yeast and human PINs revealed that they are scale-rich and configured as highly optimized tolerance (HOT) networks that are similar to the router-level topology of the Internet. This is different from claims that such networks are scale-free and configured through simple preferential-attachment processes. Further analysis revealed that there are extensive interconnections among middle-degree nodes that form the backbone of the networks. Degree distributions of essential genes, synthetic lethal genes, synthetic sick genes, and human drug-target genes indicate that there are advantageous drug targets among nodes with middle- to low-degree nodes. Such network properties provide the rationale for combinatorial drugs that target less prominent nodes to increase synergetic efficacy and create fewer side effects.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 179 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 5%
United Kingdom 5 3%
Germany 4 2%
India 4 2%
Spain 3 2%
Italy 2 1%
China 2 1%
Turkey 1 <1%
Finland 1 <1%
Other 6 3%
Unknown 142 79%

Demographic breakdown

Readers by professional status Count As %
Researcher 50 28%
Student > Ph. D. Student 42 23%
Student > Master 19 11%
Professor > Associate Professor 16 9%
Professor 9 5%
Other 30 17%
Unknown 13 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 85 47%
Biochemistry, Genetics and Molecular Biology 19 11%
Computer Science 18 10%
Medicine and Dentistry 9 5%
Physics and Astronomy 6 3%
Other 23 13%
Unknown 19 11%