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Modeliranje 3D struktur interakcij med proteini in RNA
ID Čopar, Andrej (Author), ID Curk, Tomaž (Mentor) More about this mentor... This link opens in a new window

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PID: 20.500.12556/rul/21825b25-032a-440b-8a46-687443620c67

Abstract
Interakcije med proteini in RNA imajo ključno vlogo pri velikem številu celičnih procesov. Eksperimentalna analiza 3D struktur molekul je počasna in zahtevna, zato obstaja velika potreba po računskih metodah, ki uspešno napovedujejo mesta ter strukturo molekul v interakciji. V magistrskem delu smo definirali vrsto značilk, ki opisujejo lokalne lastnosti interakcij protein-RNA, na podlagi podatkov o 3D strukturah molekul protein-RNA. Razvili smo metodo, ki združuje strojno učenje in optimizacijski postopek za napovedovanje mesta interakcij med proteinom in RNA. Napovedi strojnega učenja se uporabijo za določanje začetnega stanja optimizacije. Optimizacijski postopek nato uporabi ocenjevalne funkcije osnovane na porazdelitvi 3D strukturnih značilk in tako predlaga najverjetnejšo pozicijo molekule RNA. Predlagani napovedni model dosega natančnost, ki je primerljiva z uspešnostjo najboljših obstoječih metod.

Language:Slovenian
Keywords:bioinformatika, interakcije protein-RNA, strukturna analiza, napovedni model, kombinatorična optimizacija, umestitev molekul
Work type:Master's thesis/paper
Organization:FRI - Faculty of Computer and Information Science
Year:2014
PID:20.500.12556/RUL-29433 This link opens in a new window
Publication date in RUL:04.09.2014
Views:1975
Downloads:660
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Secondary language

Language:English
Title:Modeling 3D structures of protein-RNA interactions
Abstract:
Protein-RNA interactions have an essential role in many cellular processes. Experimental analysis of 3D molecular structure is slow and difficult process. Consequently, computational methods, which successfully predict interaction sites and molecular conformations are needed. In this thesis we have defined a number of attributes to describe local properties of protein-RNA interactions using data on 3D structure of protein-RNA molecules. We have implemented a method that uses machine learning and optimization algorithm for prediction of protein-RNA interaction sites. Machine learning predictions are used to generate initial positions for optimization. Optimization algorithm uses scoring functions based on the distribution of 3D structural attributes to identify most likely positions of the RNA molecule interacting with a given protein. The accuracy of the proposed prediction model is comparable to results obtained with best existing methods.

Keywords:bioinformatics, protein-RNA interactions, structural analysis, prediction model, combinatorial optimization, molecular docking

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