MOTOR: Model assisted software for NMR structure determination

作者:Schieborr Ulrich*; Sreeramulu Sridhar; Elshorst Bettina; Maurer Marcus; Saxena Krishna; Stehle Tanja; Kudlinzki Denis; Gande Santosh Lakshmi; Schwalbe Harald
来源:Proteins: Structure, Function, and Genetics , 2013, 81(11): 2007-2022.
DOI:10.1002/prot.24361

摘要

Eukaryotic proteins with important biological function can be partially unstructured, conformational flexible, or heterogenic. Crystallization trials often fail for such proteins. In NMR spectroscopy, parts of the polypeptide chain undergoing dynamics in unfavorable time regimes cannot be observed. De novo NMR structure determination is seriously hampered when missing signals lead to an incomplete chemical shift assignment resulting in an information content of the NOE data insufficient to determine the structure ab initio. We developed a new protein structure determination strategy for such cases based on a novel NOE assignment strategy utilizing a number of model structures but no explicit reference structure as it is used for bootstrapping like algorithms. The software distinguishes in detail between consistent and mutually exclusive pairs of possible NOE assignments on the basis of different precision levels of measured chemical shifts searching for a set of maximum number of consistent NOE assignments in agreement with 3D space. Validation of the method using the structure of the low molecular-weight-protein tyrosine phosphatase A (MptpA) showed robust results utilizing protein structures with 30-45% sequence identity and 70% of the chemical shift assignments. About 60% of the resonance assignments are sufficient to identify those structural models with highest conformational similarity to the real structure. The software was benchmarked by de novo solution structures of fibroblast growth factor 21 (FGF21) and the extracellular fibroblast growth factor receptor domain FGFR4 D2, which both failed in crystallization trials and in classical NMR structure determination. Proteins 2013; 81:2007-2022.

  • 出版日期2013-11