"While automation is revolutionizing many aspects of biology, the determination of three-dimensional protein structure remains a long, hard, and expensive task. Novel algorithms and computational methods in biomolecular NMR are necessary to apply modern techniques such as structure-based drug design on a much larger scale. The goal of this project is to address a key computational bottleneck in NMR structural biology, resonance assignments. We will accelerate protein NMR assignment by exploiting a priori structural information. By analogy, in X-ray crystallography, the molecular replacement (MR) technique allows solution of the crystallographic phase problem when a “close” or homologous structural model is known, thereby facilitating rapid structure determination. In contrast, a key bottleneck in NMR structural biology is the assignment problem. An automated procedure for rapidly determining NMR assignments given an homologous structure, will similarly accelerate structure determination. Moreover, even when the structure has already been determined by crystallography or computational homology modeling, NMR assignments are valuable because NMR can be used to probe protein-protein interactions and protein-ligand binding (e.g. via chemical shift mapping), and dynamics (via, e.g., nuclear spin relaxation). We will develop an MR-like approach for structure-based assignment of resonances and NOEs, to be applied when a homologous protein is known. The tool that we develop will accept both CH- and NH- RDCs, and 4-D NOESY data, and will implement a Bayesian scoring function for structure-based assignments. It will provide the user the option to use only NH RDCs or NH and CH RDCs and will be tested on real proteins. The source code will be released as open source with the user manual."