Prediction of protein-protein interactions from sequence using evolutionary relations of proteins and species

Güney, Tacettin Doğacan
Prediction of protein-protein interactions is an important part in understanding the biological processes in a living cell. There are completely sequenced organisms that do not yet have experimentally verified protein-protein interaction networks. For such organisms, we can not generally use a supervised method, where a portion of the protein-protein interaction network is used as training set. Furthermore, for newly-sequenced organisms, many other data sources, such as gene expression data and gene ontology annotations, that are used to identify protein-protein interaction networks may not be available. In this thesis work, our aim is to identify and cluster likely protein-protein interaction pairs using only sequence of proteins and evolutionary information. We use a protein’s phylogenetic profile because the co-evolutionary pressure hypothesis suggests that proteins with similar phylogenetic profiles are likely to interact. We also divide phylogenetic profile into smaller profiles based on the evolutionary lines. These divided profiles are then used to score the similarity between all possible protein pairs. Since not all profile groups have the same number of elements, it is a difficult task to assess the similarity between such pairs. We show that many commonly used measures do not work well and that the end result greatly depends on the type of the similarity measure used. We also introduce a novel similarity measure. The resulting dense putative interaction network contains many false-positive interactions, therefore we apply the Markov Clustering algorithm to cluster the protein-protein interaction network and filter out the weaker edges. The end result is a set of clusters where proteins within the clusters are likely to be functionally linked and to interact. While this method does not perform as well as supervised methods, it has the advantage of not requiring a training set and being able to work only using sequence data and evolutionary information. So it can be used as a first step in identifying protein-protein interactions in newly-sequenced organisms.


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Protein-protein interactions are important for the prediction of protein functions since two interacting proteins usually have similar functions in a cell. Available protein interaction networks are incomplete; but, they can be used to predict new interactions in a supervised learning framework. However, in the case that the known protein network includes large number of protein pairs, the training time of the machine learning algorithm becomes quite long. In this thesis work, our aim is to predict protein-...
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Structural details of protein interactions are invaluable to the understanding of cellular processes. However, the identification of interactions at atomic resolution is a continuing challenge in the systems biology era. Although the number of structurally resolved complexes in the Protein Databank increases exponentially, the complexes only cover a small portion of the known structural interactome. In this chapter, we review the PRISM system that is a protein–protein interaction (PPI) prediction tool—its r...
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Turanalp, Mehmet E.; Can, Tolga (Springer Science and Business Media LLC, 2008-06-11)
Background: In recent years, a considerable amount of research effort has been directed to the analysis of biological networks with the availability of genome-scale networks of genes and/or proteins of an increasing number of organisms. A protein-protein interaction (PPI) network is a particular biological network which represents physical interactions between pairs of proteins of an organism. Major research on PPI networks has focused on understanding the topological organization of PPI networks, evolution...
Citation Formats
T. D. Güney, “Prediction of protein-protein interactions from sequence using evolutionary relations of proteins and species,” M.S. - Master of Science, Middle East Technical University, 2009.