Most of the recent studies on biomolecules address their three dimensional structure since it is closely related to their functions in a biological system. Determination of structure of biomolecules can be done by using various methods, which rely on data from various experimental instruments or on computational approaches to previously obtained data or datasets. Single particle reconstruction using electron microscopic images of macromolecules has proven resource-wise to be useful and affordable for determining their molecular structure in increasing details.
The main goal of this thesis is to contribute to the single particle reconstruction methodology, by adding a process of denoising in the analysis of the cryo-electron microscopic images. First, the denoising methods are briefly surveyed and their efficiencies for filtering cryo-electron microscopic images are evaluated. In this thesis, the focus has been set to information theoretic minimum description length (MDL) principle for coding efficiently the essential part of the signal. This approach can also be applied to reduce noise in signals and here it is used to develop a novel denoising method for cryo-electron microscopic images. An existing denoising method has been modified to suit the given problem in single particle reconstruction. In addition, a more general denoising method has been developed, discovering a novel way to find model class by using the MDL principle. This method was then thoroughly tested and compared with co-existing methods in order to evaluate the utility of denoising in single particle reconstruction.
A secondary goal in the research for this thesis deals with studying protein oligomerisation, using computational approaches. The focus has been to recognize interacting residues in proteins for oligomerization and to model the interaction site for hantavirus N-protein. In order to unravel the interaction structure, the approach has been to understand the phenomenon of protein folding towards quaternary structure.reviewe
