Thesis Title: Subtitle - NMR Spectroscopy Research Group

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114 Chapter 4. Protein Structure Determination from Pseudocontact Shifts using ROSETTA. corrected 13 C chemical shifts, except for thioredoxin where 15 N chemical shift were corrected (SI Table S4.1). CS-ROSETTA aims to generate 200 9-residue fragments and 200 3-residue fragments centered on each residue of the polypeptide chain for use in the ab initio fragment assembly protocol of ROSETTA. In cases where CS-ROSETTA failed to generate 200 fragments, we generated additional fragments using the conventional ROSETTA protocol in order to make 200 fragments available. For each of the target proteins, we removed any protein with recognizable sequence similarity (BLAST E-value below 0.05) from the CS-ROSETTA protein database. (For example, the structure of ε186 is present in the CS-ROSETTA database, but was explicitly excluded when fragments were generated.) In order to accelerate the grid search for the metal position, PCS-ROSETTA allows a precise description of the space to be searched, including the center of the grid search (cg), the step size between two nodes (sg), an outer cutoff radius (co) to limit the search to a minimal distance from cg, and an inner cutoff radius (ci) to avoid a search too close to cg. A moderately large step size (sg) was chosen to speed up computations during low resolution sampling (Table S4.1), and reduced to 25% of its value during the final high resolution scoring step to ensure maximum accuracy. For each target, the grid parameters cg, co and ci were chosen in accordance to prior knowledge about the approximate metal binding site. For example, for a covalent tag attached to the protein, we used the known geometric information of the tag to set cg, co, and ci, whereas for proteins with a natural metal binding site, a highly conserved negatively charged residue was picked as a reference point for cg. In the absence of prior biochemical information, the nuclear spin with the largest absolute PCS value was chosen as the center of the grid. SI Table S4.1 summarizes the grid parameters used for the different protein targets. In order to assess the impact of the initial grid parameters on the structures calculated, a set of PCS-ROSETTA calculations was performed for each target, where cg was centered at the nuclear spin of the largest PCS observed and the cutoff radius co was set to 15 Å. No change in the quality of the results was observed but in most cases the calculations took longer. 4.5.4 PCS-ROSETTA Protocol for Protein Structure Determination Chemical shifts of the proteins were prepared in Talos format (Cornilescu et al., 1999) and used by CS-ROSETTA for fragment selection. Chemical shift corrections, fragment selection, and determination of the weights w(c) were performed as described above. 10000 protein structures were computed with PCS-ROSETTA and subjected to the full atom relaxation protocol of ROSETTA to model the side chain conformations. The final structures were rescored using the ROSETTA full atom energy function combined with the PCS scores c, using the weighting factors w(c) (Equation (4.4)) with ahigh and alow calculated against the ROSETTA full atom energy, and
4.6 Acknowledgments. 115 with a total weight multiplied by 2 to give a larger contribution to the PCS score than in the fragment assembly. The best scoring structures can be assessed by the PCS quality factor Q = rms(PCS cal – PCS exp ) / rms(PCS exp ). Computation of 10000 PCS-ROSETTA structures took on average 137 CPU days per target and was run on a local cluster. SI Figure S4.6 shows a posteriori that 1000 structures per targets would have been enough for convergence of the protocol. 4.5.5 Computation of Structures to Evaluate the Effects of PCS Scoring 3000 decoys with a wide range of rmsd values to the target structure were generated by including the native fragment and limiting the number of alternatives fragments in the fragment generation step of the ROSETTA calculations. 1000 decoys each were calculated using two, five and ten fragments per residue, respectively. The presence of the native fragments in a small pool of fragments ensured the generation of structures very similar to the target structure. 4.6 Acknowledgments C.S. thanks the University of Queensland for a Graduate School Research Travel Grant to undertake this collaborative research project. T.H. thanks the Australian Research Council for a Future Fellowship. Financial support from the Australian Research Council for project grants to G.O. and T.H. is gratefully acknowledged. D.B. thanks the Howard Hughes Medical Institutes. 4.7 References Arnesano F, Banci L and Piccioli M (2005) NMR structures of paramagnetic metalloproteins. Q. Rev. Biophys. 38:167-219 Baig I, Bertini I, Del Bianco C, Gupta YK, Lee YM, Luchinat C and Quattrone A (2004) Paramagnetism-based refinement strategy for the solution structure of human α- parvalbumin. Biochemistry 43:5562-5573 Balayssac S, Jiménez B and Piccioli M (2006) Assignment strategy for fast relaxing signals: complete aminoacid identification in thulium substituted Calbindin D9K. J. Biomol. NMR 34:63-73
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