Abstract

RosettaNMR combines the Rosetta de novo structure prediction method with limited NMR experimental data for rapid estimation of protein structure. The de novo Rosetta algorithm predicts protein three-dimensional structures using only sequence information by combining short fragments selected from known protein structures on the basis of local sequence similarity. These fragments are assembled using a Monte Carlo strategy to generate models that reproduce empirical statistics describing nonlocal protein structure such as overall compactness, hydrophobic burial, and beta-strand pairing. By incorporating chemical shift, nuclear Overhauser enhancement, and?or residual dipolar coupling restraints that are insufficient on their own to determine the protein global fold, the RosettaNMR method correctly estimates the global fold of a variety of different proteins, generating models that are that are generally 4?A or better Calpha root-mean-square deviation to the high-resolution experimental structures. Here we review the capabilities of the RosettaNMR approach, describe the underlying methods, and provide practical tips for applying the technique to structure estimation problems.