Improving 3D structure prediction from chemical shift data

Abstract

We report advances in the calculation of protein structures from chemical shift nuclear magnetic resonance data alone. Our previously developed method, CSRosetta, assembles structures from a library of short protein fragments picked from a large library of protein structures using chemical shifts and sequence information. Here we demonstrate that combination of a new and improved fragment picker and the iterative sampling algorithm RASREC yield significant improvements in convergence and accuracy. Moreover, we introduce improved criteria for assessing the accuracy of the models produced by the method. The method was tested on 39 proteins in the 50–100 residue size range and yields reliable structures in 70 % of the cases. All structures that passed the reliability filter were accurate (\2 A° RMSD from the reference).