Thursday, 7 March 2013

Protein-Protein Interaction(PPI) Analysis

Protein-Protein Interaction(PPI) Analysis


Systems biology is an emerging approach applied to biomedical and biological scientific research. Systems biology is a biology-based inter-disciplinary field of study that focuses on complex interactions within biological systems, using a more holistic perspective (holism instead of the more traditional reductionism) approach to biological and biomedical research.

Networks analysis in Systems Biology

One important toolkit of systems biology is network analysis. Network models are useful representations of several biological systems, ranging from metabolic pathways  to ecosystems . Network analysis has its roots in sociology where sociologists were (and still are) interested in the patterns of interactions between people in groups. Various methodologies of network analyzing tools were invented to describe network structures at the microscopic and the macroscopic levels. We can simply use degree centrality or we can employ more complicated methods by quantifying, for instance, the shortest distance (d) between a focal node and one other node, and then sum those distances up for all others as a proxy to its network position (a distance here refers to the number of links the focal node needs to traverse in order to reach the target node) with all others in the same social network. At the level of a network, network properties can be quantified by collecting information from the nodal level; this ranges from simple measures like link density, which is the number of observed links divided by the total amount possible, to more complicated one such as the averaged shortest distance between any node pairs. Several disciplines have borrowed the concept of network analysis from the sociologists in the last decade.
In PPI networks, graph nodes represent proteins, and links represent their interactions. Interactions can often be of two types. In the simplest case, an unsigned and undirected link exists between two proteins if they form a (part of a) protein complex pertaining to certain cellular functions. In the other case, a directed and signed link from one protein to another one exists if the former regulates (positively or negatively) the latter one. The first type of interaction is more common in PPI network studies, whereas the second type is more adequate for modelling signal transduction networks. It is important to note that the particular research problem and the available database largely determine exactly which network analytical tools can be used for analysis.

Case study

The S. cerevisiae protein-protein interaction network which was investigate by H. Jeong, S. P. Mason, A.-L. Barabásil and Z. N. Oltvai has 1870 proteins as nodes, connected by 2240 identified direct physical interactions, and is derived from combined, non-overlapping data  obtained mostly by systematic two-hybrid analyses.

Their first goal was to identify the architecture of this network, determining if it is best described by an inherently uniform exponential topology with proteins on average possessing the same number of links, or by a highly heterogeneous scale-free topology with proteins having widely different connectivity . As they show in Fig. b, the probability that a given yeast protein interacts with k other yeast proteins follows a power-law 5 with an exponential cutoff at kc 20, a topology that is also shared by the protein-protein interaction network of the bacterium, H. pylori. This indicates that the network of protein interactions in two separate organisms forms a highly non-homogeneous scale-free network in which a few highly connected proteins play a central role in mediating interactions among numerous, less connected proteins.


An important known consequence of the non-homogeneous structure is the network’s simultaneous tolerance against random errors coupled with fragility against the removal of the most connected nodes. Indeed, they found that random mutations in the genome of S. cerevisiae, modeled by the removal of randomly selected yeast proteins do not affect the overall topology of the network. In contrast, when the most connected proteins are computationally eliminated, the network diameter increases rapidly.
This simulated tolerance against random mutation is in agreement with systematic mutagenesis studies, which identified a striking capacity of yeast to tolerate the deletion of a substantial number of individual proteins from its proteome. Yet, if indeed this is due to a topological component to error tolerance, on average less connected proteins should prove less essential than highly connected ones.
To assess this hypothesis, they rank ordered all interacting proteins based on the number of links they have and correlated this with the phenotypic effect of their individual removal from the yeast proteome.

As shown in Fig. c, the likelihood that removal of a protein will prove lethal clearly correlates with the number of interactions the protein has. 











The simultaneous emergence of an inhomogeneous structure in both metabolic and protein
interaction networks indicates the evolutionary selection of a common large scale structure of biological
networks, and strongly suggests that future systematic protein-protein interaction studies in other
organisms will uncover an essentially identical protein network topology. The correlation between the
connectivity and indispensability of a given protein confirms that despite the importance of individual
biochemical function and genetic redundancy, the robustness against mutations in yeast is also derived
from the organization of interactions and topologic position of individual protein. Thus, a better
understanding of cell dynamics and robustness will be obtained from integrated approaches that
simultaneously incorporate the individual and contextual properties of all constituents of complex
cellular networks.


2. Ferenc Jordian, Thanh Phuong Nguyen, Wei-Chung,Liu-Studying protein-protein interaction networks: a system view on diseases
3. H.Jeong, S.P.Mason, A.L.Barabasi and Z.L.Oltvai-Lethality ad centrality in protein networks

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