Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/5399
Title: Improved computational prediction and analysis of protein - protein interaction networks
Researcher: Muley, Vijaykumar Yogesh
Guide(s): Ranjan, Akash
Keywords: Protein interaction networks
Protein
Upload Date: 7-Dec-2012
University: Manipal University
Completed Date: 26/09/2012
Abstract: Recent progress in computational methods for predicting physical and functional protein-protein interactions has provided new insights into the complexity of biological processes. Most of these methods assume that functionally interacting proteins are likely to have a shared evolutionary history. These methods include phylogenetic profiling (PP),gene neighborhood (GN), gene cluster (GC), and the mirrortree. Expression similarity in various physiological conditions also has been used an indicator of functional linkages between genes. Comprehensive newlinecomparison of these methods has not been frequently reported in literature. In this work, I have shown that the higher performance for predicting protein-protein interactions was achievable even with 100?150 bacterial genomes out of 565 genomes. I find that variants of PP and GN are robust against reference genome selection. This study also reveals that the prediction of metabolic pathway protein interactions continues to be a challenging task for all methods which possibly reflect flexible/independent evolutionary histories of these proteins. On the contrary, genetic information processing pathways are predicted with comparable accuracy. I have also shown that the effective use of a particular prediction method depends on the pathway under investigation. The topological properties of network predicted by each method differ significantly. This study suggests that organization of proteins in the predicted networks ensure the local perturbations in the metabolic pathways and protein complexes should communicate with quickly to other cellular proteins. A set of seven machine learning classifiers also used to predict genome-scale interactome. It is observed that probabilistic classifiers such as naïve bayes are best suitable for PPI prediction task. Finally, I have predicted functions for number of uncharacterized proteins and some of them tested experimentally. A high quality PPIs can be accessible through user friendly interface at http://www.cdfd.org.in/ecofunppi
URI: http://hdl.handle.net/10603/5399
Appears in Departments:Centre for DNA Fingerprinting and Diagnostics, Hyderabad

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02_certificate.pdf751.93 kBAdobe PDFView/Open
03_abstract.pdf226.87 kBAdobe PDFView/Open
04_declaration.pdf842.05 kBAdobe PDFView/Open
05_acknowledgement.pdf59.82 kBAdobe PDFView/Open
06_contents.pdf412.29 kBAdobe PDFView/Open
07_list_of_tables.pdf159.2 kBAdobe PDFView/Open
08_list_of_figures.pdf453.68 kBAdobe PDFView/Open
09_abbreviations.pdf395.35 kBAdobe PDFView/Open
10_chapter1.pdf1.61 MBAdobe PDFView/Open
11_chapter2.pdf831.42 kBAdobe PDFView/Open
12_chapter3.pdf1.27 MBAdobe PDFView/Open
13_chapter4.pdf962.96 kBAdobe PDFView/Open
14_chapter5.pdf906.73 kBAdobe PDFView/Open
15_chapter6.pdf1.26 MBAdobe PDFView/Open
16_chapter7.pdf2.49 MBAdobe PDFView/Open
17_conclusion.pdf54.27 kBAdobe PDFView/Open
18_bibliography.pdf158.63 kBAdobe PDFView/Open
19_appendix.pdf1.04 MBAdobe PDFView/Open


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