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A picture is a key to understanding. Scientific breakthroughs often build upon the successful visualization of objects invisible to the human eye. However, biochemical maps have long been filled with blank spaces because the available technology has had difficulty generating images of much of life’s molecular machinery. Cryo-electron microscopy changes all of this. Researchers can now freeze biomolecules mid-movement and visualize processes they have never previously seen, which is decisive for both the basic understanding of life’s chemistry and for the development of pharmaceuticals. However, most molecular complexes appear to exhibit intrinsic conformational variability necessary to perform their functions. Even relatively stable macromolecular machines such as the ribosome undergo significant conformational changes during their functional cycle and therefore can occur in a mixture of conformational states. Thus, for most of the macromolecular complexes studied, sample heterogeneity appears to be a major obstacle in structure determination in addition to difficulties caused by low SNR of the data. It is now increasingly recognized that sample heterogeneity constitutes a major methodological challenge for cryo-EM. This thesis aims to develop multi-conformation reconstruction techniques where we’ll try to reconstruct different coexisting structures from the same sample.
In this section, I present a complete set of results on the algorithm described. Using this algorithm, we demonstrate how to successfully tackle all the problems mentioned earlier and achieve good quality reconstructions of all the conformations of the original object. The images used for our experiments were taken from the Database of Macromolecular Movements and the image sizes used were 200 × 200. The images used are shown below -