141,078 research outputs found
A simple physical model for scaling in protein-protein interaction networks
It has recently been demonstrated that many biological networks exhibit a
scale-free topology where the probability of observing a node with a certain
number of edges (k) follows a power law: i.e. p(k) ~ k^-g. This observation has
been reproduced by evolutionary models. Here we consider the network of
protein-protein interactions and demonstrate that two published independent
measurements of these interactions produce graphs that are only weakly
correlated with one another despite their strikingly similar topology. We then
propose a physical model based on the fundamental principle that (de)solvation
is a major physical factor in protein-protein interactions. This model
reproduces not only the scale-free nature of such graphs but also a number of
higher-order correlations in these networks. A key support of the model is
provided by the discovery of a significant correlation between number of
interactions made by a protein and the fraction of hydrophobic residues on its
surface. The model presented in this paper represents the first physical model
for experimentally determined protein-protein interactions that comprehensively
reproduces the topological features of interaction networks. These results have
profound implications for understanding not only protein-protein interactions
but also other types of scale-free networks.Comment: 50 pages, 17 figure
Protein multi-scale organization through graph partitioning and robustness analysis: Application to the myosin-myosin light chain interaction
Despite the recognized importance of the multi-scale spatio-temporal
organization of proteins, most computational tools can only access a limited
spectrum of time and spatial scales, thereby ignoring the effects on protein
behavior of the intricate coupling between the different scales. Starting from
a physico-chemical atomistic network of interactions that encodes the structure
of the protein, we introduce a methodology based on multi-scale graph
partitioning that can uncover partitions and levels of organization of proteins
that span the whole range of scales, revealing biological features occurring at
different levels of organization and tracking their effect across scales.
Additionally, we introduce a measure of robustness to quantify the relevance of
the partitions through the generation of biochemically-motivated surrogate
random graph models. We apply the method to four distinct conformations of
myosin tail interacting protein, a protein from the molecular motor of the
malaria parasite, and study properties that have been experimentally addressed
such as the closing mechanism, the presence of conserved clusters, and the
identification through computational mutational analysis of key residues for
binding.Comment: 13 pages, 7 Postscript figure
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