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.
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