Evidence map›Paper›PMID 16861299›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2006

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

Yi Y Shi, Gerald A Miller, Hong Qian, Karol Bomsztyk

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yi Y ShiDepartment of Applied Mathematics, University of Washington, Seattle, WA 98195, USA.
Gerald A Miller
Hong Qian
Karol Bomsztyk

Funding

INTERACTION OF K PROTEIN WITH PROTEIN KINASESR01GM045134 · NIGMS · UNIVERSITY OF WASHINGTON · PI BOMSZTYK, KAROL · 1991 to 2007
$2.4M
LAMININ GENE EXPRESSION IN GLOMERULAR CELLSR37DK045978 · NIDDK · UNIVERSITY OF WASHINGTON · PI BOMSZTYK, KAROL · 2003 to 2011
$2.2M
LAMININ GENE EXPRESSION IN GLOMERULAR CELLSR01DK045978 · NIDDK · UNIVERSITY OF WASHINGTON · PI BOMSZTYK, KAROL · 1993 to 2002
$563k
NIDDK NIH HHS DK45978NIDDK NIH HHS R01 DK045978NIDDK NIH HHS R37 DK045978NIGMS NIH HHS GM45134NIGMS NIH HHS R01 GM045134
6 · The paper itself

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.

Indexed as

Evolution, MolecularModels, TheoreticalProteinsAnimalsHumansMathematicsProtein BindingProtein ConformationProtein Interaction MappingThermodynamicsTwo-Hybrid System TechniquesProteins

Identifiers

PMID16861299
PMCPMC1544203

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.