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Identifier 000452749
Title Prediction and assessment of fusion proteins
Author Μπαλτσαβιά, Ισμήνη
Thesis advisor Ηλιόπουλος, Ιωάννης
Τοπάλης, Παντελής
Προμπονάς, Βασίλης
Abstract Protein protein interactions support and orchestrate biological processes, thus playing a crucial role in biological systems maintenance. Deviations from the proper functioning can impact the homeostasis of an organism in different ways. Prediction of protein protein interactions through experimental methods have proven to be a powerful strategy in a small scale, yet characterized by lack in precision due to the presence of a high rate of false positive results deriving from the experimental design. In the big data era where whole genome sequencing data are to be analyzed such strategies become time and money-consuming. Computational methods can overcome these challenges. Prediction of protein protein interactions can emerge from genome and protein sequences. The presence of fusion proteins is a phenomenon which can ascertain that two or more proteins interact. In this case the separate proteins from one organism are found as one fused protein in another organism. These fusion events suggest the functional relationship between the component proteins of a fused protein, thus supporting their interaction. The scope of this study is to explore the potential to discover protein protein interactions in prokaryotic and eukaryotic genomes, utilizing two algorithms (DifFuse and CompositeSearch) that detect fusion proteins from sequence similarity searches. In addition, Information regarding the amino acid composition of the region between the component proteins in the fused protein, called linker, are reported. Linkers could serve as a tool for the prediction of fused proteins and thus protein protein interactions, locating protein regions with a certain amino acid composition, akin to the composition of linker regions.
Language English
Subject Linkers
Protein sequence similarity
Ομοιότητα πρωτεϊνών
Πρωτεϊνικές αλληλεπιδράσεις
Συντηγμένες πρωτεϊνες
Issue date 2022-12-07
Collection   School/Department--School of Medicine--Department of Medicine--Post-graduate theses
  Type of Work--Post-graduate theses
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