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Register a new userUnderstanding Functional Protein-Protein Interactions Of ABCB11 And ADA In Human And Mouse
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Added by
Antara Sengupta Mrs.
Publication date
2015
fields
Biology
and research's language is
English
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Proteins are macromolecules which hardly act alone; they need to make interactions with some other proteins to do so. Numerous factors are there which can regulate the interactions between proteins [4]. Here in this present study we aim to understand Protein -Protein Interactions (PPIs) of two proteins ABCB11 and ADA from quantitative point of view. One of our major aims also is to study the factors that regulate the PPIs and thus to distinguish these PPIs with proper quantification across the two species Homo Sapiens and Mus Musculus respectively to know how one protein interacts with different set of proteins in different species.
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Background: Typically, proteins perform key biological functions by interacting with each other. As a consequence, predicting which protein pairs interact is a fundamental problem. Experimental methods are slow, expensive, and may be error prone. Many computational methods have been proposed to identify candidate interacting pairs. When accurate, they can serve as an inexpensive, preliminary filtering stage, to be followed by downstream experimental validation. Among such methods, sequence-based ones are very promising. Results: We present MPS(T&B) (Maximum Protein Similarity Topological and Biological), a new algorithm that leverages both topological and biological information to predict protein-protein interactions. We comprehensively compare MPS(T) and MPS(T&B) with state-of-the-art approaches on reliable PPIs datasets, showing that they have competitive or higher accuracy on biologically validated test sets. Conclusion: MPS(T) and MPS(T&B) are topological only and topological plus sequence-based computational methods that can effectively predict the entire human interactome.
The genetic code is the set of rules by which information encoded in genetic material (DNA or RNA sequences) is translated into proteins (amino acid sequences) by living cells. The code defines a mapping between tri-nucleotide sequences, called codons, and amino acids. Since there are 20 amino acids and 64 possible tri-nucleotide sequences, more than one among these 64 triplets can code for a single amino acid which incorporates the problem of degeneracy. This manuscript explains the underlying logic of degeneracy of genetic code based on a mathematical point of view using a parameter named Impression. Classification of protein family is also a long standing problem in the field of Bio-chemistry and Genomics. Proteins belonging to a particular class have some similar bio-chemical properties which are of utmost importance for new drug design. Using the same parameter Impression and using graph theoretic properties we have also devised a new way of classifying a protein family.
The evolution, regulation and sustenance of biological complexity is determined by protein-protein interaction network that is filled with dynamic events. Recent experimental evidences point out that clustering of proteins has a vital role in many cellular processes. Upsurge in fluorescence imaging methods has given a new spin to our ability to probe protein interactions in cellular and sub-cellular compartments. Despite the increasing detection sensitivity, quantitative information that can be obtained from these imaging methods is limited. This is primarily due to (i) the difficulty in tracking the problem analytically and (ii) limitations in spatio-temporal resolution that can be achieved in interrogating living cells in real time. A novel point of view based on diffusion-driven percolative clustering is proposed here that can plausibly shed more light on the complex issues of protein-protein interactions. Since this model is open to computational analysis, it is quantitative in its premise. Besides being able to analyze the phenomenon, the power of any model is gauged by its ability to predict interesting and novel features of the phenomenon itself, which can subsequently be tested by additional experiments. To this end, an experimental assay based on fluorescence lifetime imaging is proposed to verify the validity of the percolation model.
Are turn-on and turn-off functions in protein-protein interaction networks exact opposites of each other? To answer this question, we implement a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. We study the roles of activators and deactivators, two core components of oscillatory protein interaction networks, and find a striking asymmetry in the roles of activating and deactivating proteins, where activating proteins tend to be synergistic and deactivating proteins tend to be competitive.
From the spectral plot of the (normalized) graph Laplacian, the essential qualitative properties of a network can be simultaneously deduced. Given a class of empirical networks, reconstruction schemes for elucidating the evolutionary dynamics leading to those particular data can then be developed. This method is exemplified for protein-protein interaction networks. Traces of their evolutionary history of duplication and divergence processes are identified. In particular, we can identify typical specific features that robustly distinguish protein-protein interaction networks from other classes of networks, in spite of possible statistical fluctuations of the underlying data.
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