Literature DB >> 16861299

Free-energy distribution of binary protein-protein binding suggests cross-species interactome differences.

Yi Y Shi1, Gerald A Miller, Hong Qian, Karol Bomsztyk.   

Abstract

Major advances in large-scale yeast two-hybrid screening have provided a global view of binary protein-protein interactions across species as dissimilar as human, yeast, and bacteria. Remarkably, these analyses have revealed that all species studied have a degree distribution of protein-protein binding that is approximately scale-free (varies as a power law) even though their evolutionary divergence times differ by billions of years. The universal power law shows only the surface of the rich information harbored by these high-throughput data. We develop a detailed mathematical model of the protein-protein interaction network based on association free energy, the biochemical quantity that determines protein-protein interaction strength. This model reproduces the degree distribution of all of the large-scale yeast two-hybrid data sets available and allows us to extract the distribution of free energy, the likelihood that a pair of proteins of a given species will bind. We find that across-species interactomes have significant differences that reflect the strengths of the protein-protein interaction. Our results identify a global evolutionary shift: more evolved organisms have weaker binary protein-protein binding. This result is consistent with the evolution of increased protein unfoldedness and challenges the dogma that only specific protein-protein interactions can be biologically functional.

Entities:  

Mesh:

Substances:

Year:  2006        PMID: 16861299      PMCID: PMC1544203          DOI: 10.1073/pnas.0604316103

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  40 in total

1.  Emergence of scaling in random networks

Authors: 
Journal:  Science       Date:  1999-10-15       Impact factor: 47.728

2.  Scale-free networks from varying vertex intrinsic fitness.

Authors:  G Caldarelli; A Capocci; P De Los Rios; M A Muñoz
Journal:  Phys Rev Lett       Date:  2002-12-03       Impact factor: 9.161

3.  Role of tyrosine phosphorylation in the regulation of the interaction of heterogenous nuclear ribonucleoprotein K protein with its protein and RNA partners.

Authors:  J Ostrowski; D S Schullery; O N Denisenko; Y Higaki; J Watts; R Aebersold; L Stempka; M Gschwendt; K Bomsztyk
Journal:  J Biol Chem       Date:  2000-02-04       Impact factor: 5.157

4.  Preferential attachment in the protein network evolution.

Authors:  Eli Eisenberg; Erez Y Levanon
Journal:  Phys Rev Lett       Date:  2003-09-26       Impact factor: 9.161

5.  Diffusion dynamics, moments, and distribution of first-passage time on the protein-folding energy landscape, with applications to single molecules.

Authors:  Chi-Lun Lee; Chien-Ting Lin; George Stell; Jin Wang
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2003-04-17

Review 6.  Network biology: understanding the cell's functional organization.

Authors:  Albert-László Barabási; Zoltán N Oltvai
Journal:  Nat Rev Genet       Date:  2004-02       Impact factor: 53.242

7.  A simple physical model for scaling in protein-protein interaction networks.

Authors:  Eric J Deeds; Orr Ashenberg; Eugene I Shakhnovich
Journal:  Proc Natl Acad Sci U S A       Date:  2005-12-29       Impact factor: 11.205

8.  A novel genetic system to detect protein-protein interactions.

Authors:  S Fields; O Song
Journal:  Nature       Date:  1989-07-20       Impact factor: 49.962

9.  A comprehensive two-hybrid analysis to explore the yeast protein interactome.

Authors:  T Ito; T Chiba; R Ozawa; M Yoshida; M Hattori; Y Sakaki
Journal:  Proc Natl Acad Sci U S A       Date:  2001-03-13       Impact factor: 11.205

10.  A map of the interactome network of the metazoan C. elegans.

Authors:  Siming Li; Christopher M Armstrong; Nicolas Bertin; Hui Ge; Stuart Milstein; Mike Boxem; Pierre-Olivier Vidalain; Jing-Dong J Han; Alban Chesneau; Tong Hao; Debra S Goldberg; Ning Li; Monica Martinez; Jean-François Rual; Philippe Lamesch; Lai Xu; Muneesh Tewari; Sharyl L Wong; Lan V Zhang; Gabriel F Berriz; Laurent Jacotot; Philippe Vaglio; Jérôme Reboul; Tomoko Hirozane-Kishikawa; Qianru Li; Harrison W Gabel; Ahmed Elewa; Bridget Baumgartner; Debra J Rose; Haiyuan Yu; Stephanie Bosak; Reynaldo Sequerra; Andrew Fraser; Susan E Mango; William M Saxton; Susan Strome; Sander Van Den Heuvel; Fabio Piano; Jean Vandenhaute; Claude Sardet; Mark Gerstein; Lynn Doucette-Stamm; Kristin C Gunsalus; J Wade Harper; Michael E Cusick; Frederick P Roth; David E Hill; Marc Vidal
Journal:  Science       Date:  2004-01-02       Impact factor: 47.728

View more
  7 in total

Review 1.  Diversity in genetic in vivo methods for protein-protein interaction studies: from the yeast two-hybrid system to the mammalian split-luciferase system.

Authors:  Bram Stynen; Hélène Tournu; Jan Tavernier; Patrick Van Dijck
Journal:  Microbiol Mol Biol Rev       Date:  2012-06       Impact factor: 11.056

2.  Categorizing biases in high-confidence high-throughput protein-protein interaction data sets.

Authors:  Xueping Yu; Joseph Ivanic; Vesna Memisević; Anders Wallqvist; Jaques Reifman
Journal:  Mol Cell Proteomics       Date:  2011-08-29       Impact factor: 5.911

3.  Elucidating common structural features of human pathogenic variations using large-scale atomic-resolution protein networks.

Authors:  Jishnu Das; Hao Ran Lee; Adithya Sagar; Robert Fragoza; Jin Liang; Xiaomu Wei; Xiujuan Wang; Matthew Mort; Peter D Stenson; David N Cooper; Haiyuan Yu
Journal:  Hum Mutat       Date:  2014-04-07       Impact factor: 4.878

4.  The universal statistical distributions of the affinity, equilibrium constants, kinetics and specificity in biomolecular recognition.

Authors:  Xiliang Zheng; Jin Wang
Journal:  PLoS Comput Biol       Date:  2015-04-17       Impact factor: 4.475

Review 5.  Bayesian hierarchical models for protein networks in single-cell mass cytometry.

Authors:  Riten Mitra; Peter Müller; Peng Qiu; Yuan Ji
Journal:  Cancer Inform       Date:  2014-12-10

6.  Constraints imposed by non-functional protein-protein interactions on gene expression and proteome size.

Authors:  Jingshan Zhang; Sergei Maslov; Eugene I Shakhnovich
Journal:  Mol Syst Biol       Date:  2008-08-05       Impact factor: 11.429

7.  Deducing topology of protein-protein interaction networks from experimentally measured sub-networks.

Authors:  Ling Yang; Thomas M Vondriska; Zhangang Han; W Robb Maclellan; James N Weiss; Zhilin Qu
Journal:  BMC Bioinformatics       Date:  2008-07-03       Impact factor: 3.169

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.