PhD Defense - Mikhail Simin In study of diseases and their molecular foundation and evolution, it is critical to study the three dimensional structure of biological macromolecules. Structural characterization of biological macromolecules is further motivated by the fact that biomolecules with defined function(s) exhibit a correlation between their structure and their function. One functional group of bio¬logical macromolecules is proteins: strands of amino acids that form various shapes, and perform various cellular functions. Determining the three dimensional structure of a protein becomes a pivotal point in protein analysis and medicinal studies. Proteins are polymers that are composed of 20 fundamental units of Amino acids. Amino acids vary primarily through their side chain, while sharing nearly identical backbone atoms. In several instances studying the backbone structure of a protein is of significant benefit, as opposed to the all-atom study. It has also been shown that given a protein backbone the side-chains can be places analytically or computationally. One of the advantages of backbone-only study is that obtaining experimental data, such as residual dipolar coupling, is significantly easier than data for side-chains. In the recent years protein structure determination has been assisted by residual dipolar coupling (RDC) data. RDCs show promise as a powerful source for structure determination, not only due to their sensitivity, but also their applicability in macromolecules such as large proteins, membrane-anchored proteins, homo-multimeric protein complexes, carbohydrates, and nucleic acids. Although RDCs are commonly used with a minimum contribution in structure refinement of proteins, their information content extends to de novo structure elucidation. RDCs can be collected fairly easily yet common computational tools do not take maximum advantage of these data. A single RDC datum significantly restrains the possible orientation of a pair of interacting nuclei within a protein; this gives grounds for exploration of minimal data requirements for structure determination. If RDC data are utilized to their maximum potential they can become an informative means of structure determination. This work presents REDCRAFT software package and its advancements in computational structure determination from RDCs. Detailed analyses of the software, and its performance will be presented and discussed in this document. Multiple improvements, as well as new additions to the preceding version of this software will be discussed. In particular, a novel algorithm is presented for solution space decimation addressing REDCRAFT’s native style of search depth selection.